Each year, Business Insider highlights Wall Street's rising stars.
These are up-and-comers in investment banking, trading, and investing.
All are 35 or younger. Check out our lists over the years.
For the past eight years, Business Insider's finance reporters have tapped their contacts to put together a list of who to watch on Wall Street.
We've received recommendations from bosses, colleagues, recruiters, and financial industry experts to create our annual feature. To be eligible, nominees must be based in the US, 35 or younger, and stand out among their peers. The editors make the final decisions.
Business Insider asked these rising stars from leading firms like Goldman, Blackstone, and Citadel to reflect on their successes, challenges, and best career advice.
Our most recent set of young professionals reflect the future of finance. A number of them are shaping the trajectory of clean energy and artificial intelligence by financing the infrastructure that will underpin it. Some have seen their focus go from niche to hot asset. Others are influencing how Wall Street interacts with Main Street, using their skills and savvy to create new products and services for ordinary investors or giving employees at portfolio companies ownership stakes.
The rising stars also shared how they unwind and stay grounded in order to stay mentally sharp.
2023's cohort included traders setting new playbooks for deals and trades and an investor building out burgeoning private markets businesses within the world's largest bank. These influencers also financed some of the biggest deals of the past few years and provided an edge to top investors with complex and innovative products.
They shared the lessons learned from their biggest career mistakes and how their Wall Street wardrobe had evolved from their COVID work-from-home days.
2022
Fidelity; General Atlantic; Jefferies Group; Goldman Sachs; Rachel Mendelson/Insider
As Wall Street navigated volatile markets, fewer deals, and plummeting company valuations, we found the players rising up despite the challenges.
One invested in space ventures, and another executed multibillion-dollar trades. Some up-and-comers pushed their teams to the top of industry rankings.
From books on the science of sleep to fantasy football strategy podcasts, here's what these bright leaders were reading and listening to. And here are some of their lessons and advice.
Here are the previous editions of our Wall Street rising stars list:
Check out these pitch decks for examples of fintech founders sold their vision.
Yulia Reznikov/Getty Images
Business Insider has been tracking the next wave of hot new startups blending finance and tech.
Startups include generative AI fintechs, ones disrupting lending and banking, and more.
Check out these pitch decks to see how fintech founders sold their vision.
Check out these pitch decks to see how fintech founders are selling their vision and nabbing big bucks in the process. You'll see new startups using generative AI to ease up grunt work at investment banks and private equity firms, fresh twists on digital banking, and innovation aimed at making it easier for consumers to gain access to financial services and investments.
SecureSave
From left: SecureSave cofounders Devin Miller, Suze Orman, and Bassam Saliba.
SecureSave
SecureSave works with employers to offer their workers an emergency savings account. An emergency savings account operates much like a health savings account, in that it sets aside a portion of an employee's wages to pay for emergency expenses.
Yinon Ravid, the chief executive and cofounder of Albert.
Albert
Founded in 2015, Albert offers automated budgeting and savings tools alongside guided investment portfolios. It's looked to differentiate itself through personalized features, like the ability for customers to text human financial experts.
CEO Michael Simon lured Roni Israelov, former principal at the hedge fund, AQR Capital Management, to head up his portfolio management and research team.
NDVR
NDVR is a portfolio management app that customizes portfolios and investment strategies to the needs of wealthy investors. The Boston-based startup — pronounced "endeavor"— applies to portfolios holding between $1 million and $100 million, and leverages quantitative investing strategies, which use computers and algorithmic trading to decide what stocks and other assets to buy, a method typically available to only the largest institutional investors.
Percent is a three-sided private-credit marketplace that connects borrowers, investors, and underwriters. The marketplace is geared towards accredited retail investors but plans to support more institutional investors in the future.
Vint wants to make investing in wines and spirits accessible to everyone. In June 2019, it launched as a marketplace to enable both accredited and non-accredited investors to buy shares in collections of fine wines and spirits.
London-based Tulipshare lets individuals in the UK invest in publicly-traded company stocks, and then pools individuals' shareholder rights with other like-minded investors to advocate for environmental, social, and corporate governance change at firms like JPMorgan, Apple, and Amazon.
Los Angeles-based Altruist is a digital brokerage built for independent financial advisors, intended to be an "all-in-one" platform that unites custodial functions, portfolio accounting, and a client-facing portal. It allows advisors to open accounts, invest, build models, report, trade, and bill clients through an interface that can save advisors time by eliminating mundane operational tasks.
Doorvest cofounders Andrew Luong (left) and Justin Kasad (right).
Doorvest
Doorvest helps everyday investors buy single-family homes to rent out for passive income. Through its online platform, users can purchase and manage their rental properties.
Relief offers an app that automates the credit-card debt collection process for users. It negotiates with lenders and collectors to settle outstanding balances on their behalf.
Priyank Singh and Rohit Mittal are the cofounders of Stilt, which announced its $14 million Series A funding round Thursday.
Stilt
Stilt offers loans and credit cards to immigrants coming to the US. The startup uses data, such as education and employment details, to predict an individual's future income stability and cash flow before issuing a loan, rather than rely on traditional metrics like a credit score. It also sells its loan software to other companies looking to offer a credit product.
Laura Kornhauser, cofounder and CEO, and Dmitry Lesnik, cofounder and chief data scientist, of Stratyfy
Stratyfy is a startup that uses AI to help lenders underwrite consumers without long US credit histories or gig economy workers who don't get a traditional W-2 from their employers.
Miren works with federally-certified lenders that focus on reaching underserved customers in low-to-moderate income areas to underwrite credit-thin small-business owners.
Dallas-based fintech CollateralEdge works with regional and community banks — typically those with between $1 billion and $50 billion in assets — to help analyze and price slices of commercial and industrial loans that previously might have gone unserved by smaller lenders.
Pinwheel founders Curtis Lee, Kurt Lin, and Anish Basu
Pinwheel
Pinwheel shares payroll data to help fintechs and traditional lenders serve consumers with limited or poor credit who have historically struggled to access financial products.
Tricolor is an alternative auto lender that caters to thin- and no-credit Hispanic borrowers. The Dallas-based auto lender is a community development financial institution that uses a proprietary artificial-intelligence engine that makes decisions for each customer based on more than 100 data points, such as proof of income.
Justin Straight and Bernard Worthy, LoanWell co-founders
LoanWell
LoanWell works with community-focused lenders to fill a gap in the SMB financing world by boosting access to loans under $100,000. It automates the financing process — from underwriting and origination to money movement and servicing — which can shave down an up-to-90-day process to 30 days or even same-day with some LoanWell lenders.
Parafin cofounders Vineet Goel, Sahill Poddar, and Ralph Furman
Parafin
Parafin works with companies that other small businesses sell their products through (like DoorDash and Mindbody), to offer capital to these small businesses. Parafin's tech offering spans product, marketing, compliance, and IT support. It also provides the capital, sourced via debt capital providers, and manages underwriting and risk.
Reflexivity cofounders Jan Szilagyi and Giuseppe Sette
Reflexivity
Reflexivity, formerly Toggle AI, builds data-analysis tools for traders and investors. Its investors include Wall Street investors Izzy Englander, Stanley Druckenmiller, Greg Coffey. Reflexivity's tech is used by ExodusPoint, Soros Fund, and Millennium Management.
Rogo cofounders Tumas Rackaitis (CTO), Gabe Stengel (CEO) and John Willett (President)
Rogo
Rogo is building a generative AI chatbot for bankers and analysts that can automate tasks like creating PowerPoint decks. It was cofounded by former investment bankers.
QC Ware is a startup looking to cut the time and resources it takes to use quantum computing. The technology has potential to enable companies to do complex calculations faster than traditional computers and is especially helpful in risk analytics or algorithmic trading. The fintech is backed by Wall Street giants, including D.E. Shaw, Citi, and Goldman Sachs.
Eric Chang and Alex Schumacher, co-founders of Claira
Claira
Claira is a startup that uses artificial intelligence to analyze financial contracts and documents. Its founding team brings experience from Citadel, Goldman Sachs, and BlackRock.
Kirat Singh and Mark Higgins, Beacon's cofounders.
Beacon
Beacon is a fintech that provides a shortcut for banks, asset managers, and trading firms looking to use quantitative modelling and data science to help with analyzing risk, ensuring compliance, and improving operational efficiency. The company has been backed by Warburg Pincus, Blackstone, PIMCO, and Global Atlantic.
For years, the only way investors could figure out the going price of a corporate bond was by calling up a dealer on the phone. The rise of electronic trading has streamlined that process, but data can still be hard to come by sometimes. BondCliQ is a fintech that provides a data feed of pre-trade pricing quotes for the corporate bond market. It was founded by Chris White, a former Goldman Sachs exec.
Proper Finance co-founders Travis Gibson (left) and Kyle Maloney
Proper Finance
Proper Finance helps other businesses keep track of transaction data moved between third-party and in-house systems. In 2024, Proper Finance's tech and team were acquired by Intuit to help small businesses.
Jessica Chen Riolfi and Chris Goodmacher are the cofounders of Uprise.
Uprise
Uprise is an app that offers small businesses, entrepreneurs, and freelancers financial advice and tax-planning services. The San Francisco-based startup partners with financial institutions and other fintechs for them to offer Uprise's tech.
Productfy aims to help non-finance companies offer their own banking products without additional engineering resources or background knowledge of banking compliance or legal requirements.
OppZo is a fintech that is figuring out how to speed up loans to small government contractors. It works with financing partners to extend working capital loans to firms that have won contracts, and who need cash to quickly ramp up their businesses, but might not see that first contract payment for as long as 120 days.
Decimal provides a back-end tech layer that small- and medium-sized businesses can use to integrate their accounting and business-management software tools in one place, and automate some accounting operations.
Now is a startup, cofounded by politician Stacey Abrams, that aims to solve the capital supply chain woes that plague bootstrapped businesses that have to choose between paying invoices and keeping the lights on. Now uses its own line of credit to purchase invoices from customers, paying Now's business customers immediately. When the invoice payment is eventually made by the end-customer, that money goes to Now.
Jaideep Singh is the CEO and co-founder of FlyFin, an AI-driven tax preparation software program for freelancers.
FlyFin
FlyFin is an artificial-intelligence tax preparation program that helps freelancers, who don't get traditional W-2 forms, with their taxes. It connects to a person's bank accounts, allowing the AI program to help users monitor for certain expenses that can be claimed on their taxes like business expenditures, the interest on mortgages, property taxes, or whatever else that might apply.
Worksome is a startup that wants to eliminate the extra work required to manage contractors and freelancers by automating the administrative burdens of hiring, paying, and accounting for contract workers.
HoneyBook cofounders Dror Shimoni, Oz Alon, and Naama Alon.
HoneyBook
HoneyBook provides payment and operations support for freelancers. Its $155 million Series D was led by Durable Capital Partners and included Tiger Global and Citi Ventures.
Salt Labs is a loyalty and payments fintech that is helping hourly-wage workers build wealth through a rewards points system. In June 2024, it was acquired by neobank Chime Financial for as much as $173 million, according to a Fortune report.
Hive pools unused cloud capacity on people's phones and computers and resells it to businesses. It was founded by the former CEO of Symphony, an instant messaging service widely used by Wall Street firms.
Bolt gives merchants the tools to offer a one-click checkout experience. The e-commerce startup made headlines in August 2024 for its pitch to raise a $450 million Series F that would see once-ousted founder Ryan Breslow back as CEO and value the company at $14 billion.
Method cofounders Mit Shah, Jose Bethancourt, and Marco del Carmen
Method
Y Cominator-backed Method Financial aims to help consumers pay off their debt by providing an application programming interface to move money more easily.
Lil Baby poses in the press room with the award for best new artist at the BET Awards on Sunday, June 23, 2019, at the Microsoft Theater in Los Angeles.
Richard Shotwell/Invision/AP
Kasheesh allows users to split online payments across several debit and credit cards. The financial-technology company positions itself as a "responsible" alternative to buy now, pay later services.
Atomic simplifies payroll integrations with its API, which can help financial institutions access financial data for verification of income and employment, offer consumers automated set-up or updating of direct deposits, collect financial obligations straight from consumers' paychecks, and more.
Kamino cofounders Gut Fragoso, Rodrigo Perenha, Benjamin Gleason, and Gonzalo Parejo
Kamino
Sao Paulo-based Kamino helps businesses automate their payments processes, such as invoice processing and cashflow management. Investors include Inspired Capital, Flourish Ventures, Clocktower Technology Ventures, and QED Investors.
Slope cofounders Alice Deng and Lawrence Lin Murata
Slope
Slope wants to digitize the largely manual, $125 trillion industry of business-to-business payments to help companies process customer orders, collect payments, and manage cash flow. It also uses AI to underwrite buyers and extend short-term financing. Investors include OpenAI's Sam Altman, Y Combinator, and Union Square Ventures.
Founded in 2017, Ladder uses a tech-driven approach to offer life insurance with a digital, end-to-end service that it says is more flexible, faster, and cost-effective than incumbent players.
Counterpart is a fintech that applies data science to the commercial insurance industry to more accurately measure risk and find coverage that best fits the customers' needs.
Honeycomb is using AI to streamline the often time-consuming and expensive process of sending an inspector to identify potential risks of a commercial property. It analyzes a combination of third-party data and photos submitted by customers through the startup's app to quickly identify any potential risks at a property and more accurately price policies.
ValidMind cofounders (left to right): Mehdi Esmail, Jonas Jacobi, and Andres Rodriguez
ValidMind
ValidMind automates risk-manages processes for AI models on Wall Street, specifically testing, verifying, validating, documenting and monitoring models. Its $8.1 million seed round was led by Point72 Ventures.
Spade cofounders (left to right) Cooper Hart, CTO, Tess Bloch, head of operations, and Oban MacTavish, CEO
Spade sells its software to banks and fintechs so they have more granular payments data about merchants to mitigate fraud. Its Series A was led by Flourish Ventures and included Andreessen Horowitz, Y Combinator, and Everywhere Ventures (The Fund).
Themis develops compliance software for banks, fintechs, and the companies they work. Founder and CEO Neepa Patel worked as a bank regulator at the Office of the Comptroller of the Currency and in compliance at Morgan Stanley and Deutsche Bank.
Zamp's Clete Werts, Edward Lando, and Rohit Bhadange.
Most people think of sales tax as a couple of dollars tacked onto their grocery receipt, but for the seller, managing those dollars on every single product is more complex. Zamp aims to help online sellers of all sizes manage their sales tax compliance.
LeapXpert is helping companies ensure the messaging channels employees use to communicate professionally are safe and compliant. Monitoring business discussions outside traditional channels has been a sore spot for Wall Street firms, and a regulatory crackdown in recent years has meant firms have gotten more aggressive in their monitoring efforts.
Michael M. Santiago/Getty Images; Getty Images; BI
Wall Street banks are proving that generative AI is here to stay and the tech is not just a fad.
Business Insider has reported on how some of finance's biggest banks are approaching generative AI.
See how giants like Goldman Sachs and JPMorgan are weaving the tech into the fabric of their firms.
Wall Street bank leaders say generative AI is here to stay, and they're weaving the technology throughout the fabric of their banks to make sure.
From trading to payments to marketing, it's hard to find a corner of the banking industry that isn't claiming to use AI.
In fact, the technology's impact, made mainstream by OpenAI's ChatGPT in late-2022, is becoming cultural. Generative AI is changing what it takes to be a software developer and how to stand out as a junior banker, especially as banks begin dispatching autonomous AI agents. The technology is even changing roles in the c-suite.
Mary Erdoes, the boss of JPM's asset- and wealth-management business, used these slides to outline how she wants to get her people ready for the "AI of the future."
Manuela Veloso has been focused on AI for decades. The former head of machine learning at Carnegie Mellon University, Veloso has been JPMorgan's head of AI research since 2018. She broke down seven main challenges her team is trying to solve with AI for the bank.
Goldman's top partners and CEO David Solomon are eager to see AI rev up their businesses. From realizing internal productivity gains to capturing more business as clients look to raise money in anticipation of AI development and acquisitions, here's what the top echelon is expecting.
There is no AI without data, and there is no data strategy at Goldman without its chief data officer, Neema Raphael. Raphael gave BI an inside look at how his roughly 500-person team melds with the rest of the bank to get the most out of its data.
AI's impact has ripple effects that go far beyond technology. Goldman's chief information officer, Marco Argenti, predicts that cultural change will be critical to getting the bank to 100% adoption.
Many dollars are being spent on Wall Street's AI ambitions. But how do you measure the return on the investment? Argenti offers some tips on the calculus that can help firms prioritize where to invest.
Thanks to its partnership with ChatGPT-maker OpenAI, Morgan Stanley has ramped up its AI efforts. The exec in charge of tech partnerships and firmwide innovation opened up about how it all started.
Bank of America's chief experience officer, Rob Pascal, details how the bank's internal-facing AI assistant helps bankers collect, record, and review client data. Here are all the ways it's helping employees be more effective and efficient.
Wall Street investment banks prepare for an AI future.
Momo Takahashi/BI
Investment bankers are hopeful that corporate America's obsession with AI could kick off a new era of mergers, acquisitions, and IPOs. From execs stepping into recently created roles to accommodate the sector to industry veterans launching their own AI-focused M&A-advisory firm, meet 11 investment bankers poised to lead Wall Street's AI revolution.
AI could save junior bankers time by automating tedious tasks known all too well by Wall Street's youngest ranks. But it can also make it harder to break into the industry by shifting the skills required for entry.
A former Goldman Sachs managing director built an AI-powered networking tool to spur dealmaking. The budding startup, Louisa AI, already has a few clients, including Goldman Sachs, Insight Partners, and a global exchange.
Distinguished engineers and technical fellows are among the highest-level technologists in banking.
They are selected through an extensive process that highlights technical prowess and influence.
BI discussed the the program with execs from Goldman Sachs and Morgan Stanley.
Every two years, a selective group of Morgan Stanley's elite technologists open their ranks to welcome a new crop of distinguished engineers.
This month, the existing distinguished engineers at the firm themselves selected their 19 newest members after a rigorous half-a-year vetting process. The group, now made up of 78 employees from across the firm, represents the top 0.3% of technologists at Morgan Stanley.
"Anywhere we're making those core decisions at the center of tech, you can almost guarantee that all or some portion of the DEs are involved in those decision processes," Michael Pizzi, head of US banks and head of technology at Morgan Stanley, told Business Insider.
Goldman Sachs, meanwhile, last month announced its class of 111 technical fellows, representing the top 3% of engineers at the bank. They specialize in specific domains, and are often tapped to work on some of the firm's trickiest technical issues.
Goldman Chief Technology Officer John Madsen remembers assembling an A-team of about 10 technical fellows a few years ago to make upgrades to an important system that was about to max out on its capacity, he said, declining to specify which business the system supported. "These kinds of moments often go unnoticed by the bulk of the firm, but they highlight the exact value this community brings to Goldman Sachs," Madsen told BI.
At JPMorgan, distinguished engineers represent the highest level of engineer, and includes the likes of Marco Pistoia, who leads applied research on cutting-edge technologies like quantum computing, and William Patrick Opet, the bank's global chief information security officer.
While it's usually the rainmakers, investors, and traders who take the spotlight on Wall Street, there's a growing group of technologists whose influence is taking center stage as technology is increasingly viewed as a competitive advantage.
Call them distinguished engineers or technical fellows (depending on the firm), they're who bank leaders go to with their thorniest and most challenging technical problems. They also help put into effect and execute the longer-term firm-wide tech strategy set out by C-suites. For many engineers, it's a dream and one of the highest technical designations in the business.
BI spoke with some of the newly minted distinguished engineers, a long-time tech fellow, and the executives running these programs to find out what they do, how they influence the wider tech strategy, and to get an inside look at the vetting process.
"I wanted it more than anything"
Some of the first tech fellows on Wall Street were at Goldman Sachs, whose program dates back to 2004. Miruna Stratan still remembers the call when she was told she had made it.
"I remember I wanted it more than anything," Stratan told BI of the program that celebrates technologists in a way that was historically reserved for rainmakers and business leaders.
Now, she is a top tech exec within Goldman's lucrative investment-banking division and is one of just four partner-level tech fellows at the firm. The distinction is highly sought after by Goldman's technical population, she said, and the bank even hosts presentations to demystify what it means to be a tech fellow.
Part of the allure is being able to show off your technical chops. But being a tech fellow also means you can shape the firm through your engineering solutions, Stratan said.
"The tech fellows at Goldman Sachs have been at the center of all the major business transformations through technology since the program began. From grid compute and virtual desktops to cloud and data lakes, and now, of course, with AI," she said of previous technology waves.
It's a similar story at Morgan Stanley, where distinguished engineers are expected to spend 15% to 20% of their time on enterprise-level problems, not unit-specific problems, Pizzi said. That additional responsibility, which doesn't necessarily come with a raise or promotion, is actually a plus for many engineers in the program.
"We can help to make some firm-wide decisions in technology and make sure we are moving in the right direction," said Ken Zhang, one of Morgan Stanley's newest DEs and one of the brains behind the bank's generative AI tool, AskResearch.
Fang Song, another newly minted Morgan Stanley DE and executive director in the enterprise computing group, referred to her peers as "technical influencers" who make widely impacting decisions on reusable technology blueprints that are used across departments.
What makes a distinguished engineer or technical fellow?
At Morgan Stanley and Goldman Sachs, distinguished engineers and technical fellows cannot apply for the designation. They must be nominated by senior management or existing TFs and DEs.
While engineering excellence is at the heart of being a DE and TF, it's not the only thing candidates are judged on.
At Morgan Stanley, Pizzi said contributing to the firm's technical reputation externally is important, whether it involves writing technical books and white papers or holding patents. The bank's DEs hold one third of the patents the bank has generated, he added.
Goldman Sachs judges candidates on how they nurture the broader technology population. Stratan said she remembers and keeps in touch with her mentors, who taught her "how to be a senior engineer and how to think through complex problems."
"A very important piece is mentoring and coaching," Stratan said. "Basically the tech fellows are coaching the next generation of tech talent at the firm."
Recruiters say Wall Street firms are planning to hire more tech talent next year.
Jobs at AI companies like Nvidia and OpenAI, or ones that work with their products, are highly sought after.
Recruiters Ben Hodzic and Matt Stabile outline how jobseekers can stand out in the hiring process.
It's been a tough year for software engineers on the job market, but one bright spot is starting to emerge on Wall Street for technologists looking for a new gig.
Ben Hodzic, a managing director at recruitment firm Selby Jennings who finds talent for hedge funds and investment banks, told Business Insider there's "a lot of optimism" around AI in financial services.
"Financial services institutions are slowly adopting their workflows and they've come to a reality where you need the right talent to actually build and implement and manage those products," he said.
In some cases, the hiring spree is already happening. Jamie Dimon, the boss of America's biggest bank JPMorgan Chase, said earlier this year that he's anticipating adding thousands of jobs related to AI in the next few years. Hedge fund and proprietary-trading firms are shelling out as much as $350,000 in annual salaries for top-tier AI researchers and engineers. Meanwhile, private-equity firms have been "clamoring" to hire AI operating executives to improve their portfolio companies.
Hodzic said the rosier outlook stems from a desire to build AI tools in-house and boost worker productivity in areas like wealth advisory, investment banking, and trading. More clarity on the direction of macroeconomic factors, like inflation and the impacts of the US election, is also providing tailwinds for banks looking to invest in human capital in 2025, he said.
AI is also changing what it takes to get a tech job on Wall Street. BI spoke with recruiters to find out how candidates should adapt and what they need to do to stand out.
They outlined some of the industry's most in-demand skills, explained why having Big Tech experience might not get you that far anymore, and shared the companies that hiring managers want to see on resumes. They declined to disclose specific client activity due to privacy agreements.
Here's what software engineers need to know to get hired on Wall Street
Big Tech experience will only get you so far
Big Tech experience used to stand out on Wall Street. Not so much anymore, according to a recruiter.
Samantha Lee/Business Insider
A few years ago, Matt Stabile, a tech recruiter who works with buy-side firms including Two Sigma and Susquehanna International Group, could almost guarantee an interview with a hiring manager if a candidate was coming out of a FAANG company.
"Now, due to overhiring and layoffs, those resumes seem to be a dime a dozen," Stabile told BI.
Big Tech companies have shed thousands of workers this year, flooding the job market with resumes touting the same companies, like Meta and Amazon. Only certain divisions of Big Tech companies on a resume will catch hiring managers' eyes, like Google's DeepMind, for instance, Stabile said.
Nvidia, OpenAI, and Anthropic are all the rage
Hedge funds are specifically looking for talent from Nvidia and other AI companies.
Getty Images; Chelsea Jia Feng/BI
Finance firms want to build their own homegrown solutions, Hodzic told BI. That's created demand among hedge-fund clients to ask for technologists specifically from Nvidia.
"There's definitely an inherent need for people to understand the infrastructure side as well, how to actually construct the computer in the right way to be able to process some of this information and what quality of chips are needed in order to actually produce the output they're looking for," Hodzic said.
Stabile is seeing the same, with hiring managers getting excited to see talent coming from the chipmaker, AI startup Anthropic, and ChatGPT-maker OpenAI. He said these are the resumes that are getting through and being considered over all the others.
Even if you haven't worked at any of these companies, Stabile said experience and exposure to their software tools is still a highly sought after skill. He specifically highlighted the Nvidia Triton Inference Server, an open-source software that's key to deploying and executing AI workloads, Nvidia TensorRT-LLM, used to optimize the performance of large language models, and Nvidia Fleet Command, which is important for scaling AI deployments.
Highlight your migration experience
Banks like to see migration experience on tech applicants' resumes.
Tetra Images/Getty Images
If you haven't worked at a large AI company, or haven't worked with their latest products, not all hope is lost.
That has introduced, however, the need to ensure that systems old and new can work together and exist in the same environment without introducing bugs or dependency issues. In the case of firms moving on-premise systems to the public cloud, sometimes entire back-end systems have to be rebuilt.
As a result, hiring managers often light up when candidates can talk about their experience with software migrations, Selby Jennings' Hodzic said. If you've shifted data or software from one system to another, or translated code from one language to another, be sure to bring it up during the interview process.
"People who can demonstrate that engineering skillset of recreating and reconstructing things are really sought after," Hodzic said. "I think what a lot of clients want are people who can come in and show them what's not working well, how to iterate and how to improve, and then actually do it."
Rohan Doctor was a managing director at Goldman Sachs when he founded Louisa AI.
The startup uses AI to feed deal ideas and networking prompts to bankers and investors.
Here's why he wants to bring the dealmaking playbook to startups.
Cold call after cold call, Rohan Doctor wasn't getting as far as he would've hoped.
The former Goldman Sachs managing director had emailed a list of digital strategy execs at banks and private equity firms to try to sell them on his startup, Louisa AI. But he only got a handful of replies back.
Two years since its launch, Louisa AI has secured about a dozen clients, including some of the biggest names in corporate America. They include Goldman Sachs, VC firm Insight Partners, and, more recently, one of the biggest AI chipmakers and a top consulting firm. But he didn't secure those contracts from cold outreach. He used his own startup's technology, which proactively prompts deal ideas based on people's personal and professional connections, to get in through the front door.
Now, Doctor wants to bring his dealmaking playbook to other startups ahead of an anticipated M&A boom.
"If we're able to close more deals through warm relationships this way, then other startups can, too," he said.
The near-term outlook for M&A activity has gotten brighter, with lower interest rates reducing the cost of borrowing. Wall Street execs are optimistic that Trump's return to the White House, and any business-friendly regulations that may come with it, will be a tailwind for dealmaking. Also, companies resetting their valuations could spur more transactions to close as price expectations align between buyers and sellers.
Meanwhile, in Silicon Valley, VCs and founders are hopeful about the anticipated looser environment, which could boost tech building and dealmaking. VCs, which rely on selling startups in M&A deals for many of their returns, have been dampened by theFederal Trade Commission's antitrust stance on M&A.
Louisa AI was built to suggest potential deals based on the data it's exposed to. It ingests information about who and what employees know by plugging into company CRMs, messaging platforms like Slack and Symphony, and email providers. Since spinning out of Goldman Sachs in 2023, Louisa AI has raised $5 million in seed funding. It suggests about $1 billion in deal values per quarter, Doctor said.
It also highlights mutual connections to establish a warm introduction, which can make all the difference in the multi-billion investment banking industry built on relationships. While running the bank-solutions group at Goldman Sachs, Rohan Doctor used his network to close transactions worth tens of millions of dollars. As a startup founder, it's been a different story.
"I've tried the cold outreach and just emailing," Doctor said, adding that the startup stopped doing that after it didn't yield good results. What has worked for Doctor is realizing he knows someone who knows someone.
Louisa AI scored the chipmaker contract after the AI flagged that one of Doctor's staff used to work for someone who now worked at the chip manufacturer. With the consultancy, one of Louisa AI's investors connected Doctor with the consulting firm they used to work for. He declined to name these firms due to non-disclosure agreements.
"Everything needs to be warm when it comes to big companies doing big things with other people. It has to rely on trust," he said.
Goldman Sachs' Jung Min, Bank of America's Neil Kell, Axom Partners' Brandon Hightower and Alan Bressers
Goldman Sachs; Bank of America; Axom Partners
Macroeconomic signs, like lower interest rates and a new administration, point to a rise in M&A.
Bankers anticipate more AI dealmaking to benefit data and infrastructure companies.
Execs from Goldman Sachs, Bank of America, and Axom Partners outline their 2025 predictions.
AI offers the promise of a dealmaking gold rush in years to come, and investment bankers are looking to cash in on deals involving data and infrastructure companies selling the proverbial pickaxes and shovels.
"Everybody's rightly caught up in the lore of AI and what the industry leaders are doing regarding building out platforms, but people forget; unless you have good data management and good data integrity, you can't fully deploy any application of AI," said Neil Kell, Bank of America's chair and global head of technology, media, and telecom for equity-capital markets. "Companies that are looking to be acquisitive are focused on this," he said.
Brandon Hightower, a founder and partner at the tech-focused M&A advisory firm Axom Partners, attributes an increase in AI-themed deals to "an arms race" earlier this year around infrastructure and talent.
Those are two themes that are expected to see continued momentum, according to interviews with four AI bankers. Tech companies focusing on managing, moving, and securing data will be at the forefront of the AI M&A wave. Other "pickaxe and shovel" companies include those focusing on developer tools and resource optimization. AI dealmaking is also poised to touch non-technology sectors, like customer service, commerce, and industrials.
While there hasn't been a lot of dealmaking activity among pure-play generative AI companies, tens of billions have been spent on technology companies that are building infrastructure around AI like storage, server, cloud, and software companies, according to Scott Denne, a principal research analyst at S&P Global Market Intelligence.
In 2024, some $82 billion was spent on AI- and AI-related acquisitons, up from about $55 billion in 2023, according to data from 451 Research, an arm of S&P Global Market Intelligence. This includes purchases of companies selling AI products or products built with AI, as well as companies that sell software, hardware, and other tech to support AI development and deployments.
There are several reasons the timing may be right for companies to shop around. Lower interest rates have reduced the cost of borrowing. The gap between buyer and seller price expectations is shrinking as companies, AI ones included, reset their valuations. Election uncertainty is behind us, and president-elect Trump's pick of Andrew Ferguson to run the Federal Trade Commission is looking like a positive for Big Tech, according to Wedbush Securities.
Ferguson was tapped "at a key time in the AI arms race in which we expect the strong to get stronger as Mag 7 gets the engines started up again on M&A," Wedbush analysts wrote in a note to clients Wednesday.
All eyes on data
Because generative AI is still relatively nascent, bankers are focused on the raw ingredients that are critical to AI models'success.
"Two of the most interesting areas to watch next year will be data infrastructure, management, and analytics companies. The second is developer tools," Jung Min, the co-COO of Goldman Sachs' TMT division, told BI.
Also important is data security, according to Bank of America's Kell, which he said continues to be very relevant and vital for those companies thinking about M&A.
Eventually, the front-facing application layer will be the biggest area, he said. But "in that creation phase, the infrastructure and the developer tools, those are the things that really matter first" after the models are trained, Min said.
That's because companies are trying to figure out "how to get better quality data" and "more efficient flows of data," Axom's Hightower said.
It's something even the biggest AI companies are opening their wallets for. When Axom worked on the sale of database analystics firm Rockset to OpenAI earlier this year, the deal came down to enabling faster data retrieval and improving the data pipeline, according to Axom cofounder Alan Bressers.
The focus on data infrastructure is spotlights two big-data giants Databricks and Snowflake, which respectively have made a handful of acquisitions related to data and AI this year.
"If you have data and if you own the models, those are two key components. How do I bring those together? And if you've got Databricks and Snowflake holding a lot of enterprise data, that's a natural place for a future winner in the AI world," he said.
Optimizing the back end
A need to scale is also driving tech companies to acquire infrastructure players, Axom's Hightower said. He pointed to Nvidia's purchase of Seattle-based OctoAI, a deal that Axom worked on "so that it can scale and do certain aspects of AI workflows in a more efficient manner," he said. It's at least Nvidia's second acquisition this year with an eye toward scalability.
Earlier this year, Nvidia set out to buy Run:ai. The deal, which is still in the regulatory approval process, could help the chipmaker run compute more economically by allowing more work to be done on fewer chips.
There's also a greater focus on lowering inference costs, essentially the price of asking an AI models a question and having them generate a response. While it's well known that training LLMs can come with astronomical prices, getting them to beam back an answer might come at an even steeper price. It's something that AI adopters are learning the hard way, prompting a "very near-term thing where you can see M&A because people can say there's real dollar value from inference savings," Axom's Bressers said. He noted potential buyers interested in this part of the tech stack could be the newer generation of cloud companies, like CoreWeave or Lambda.
Some cross-sector action
While most of the AI and AI-related deals will likely be between tech companies, Goldman's Min anticipates some transactions in the industrial space.
"Companies that already automate or help automate the supply chain, a lot of those interaction are already software to software or machine to machine, so those are ripe for AI to really deploy there," he said.
Other fertile grounds for acquisition going into 2025 include customer service and customer-relationship management companies, potentially spurred by some of Salesforce's recent AI ambitions, Axom's Hightower said. This year, the CRM giant Salesforce made "a hard pivot" to AI agents with a product that lets clients build their own custom ones to interact directly with clientele. Competitors like ServiceNow, Braze, Klayvio, and HubSpot to broaden their suite of solutions and add data and data connectivity to their offerings, he said.
McKinsey helps banks and financial institutions with their generative AI efforts.
It outlined the dos and don'ts of seeing a return on AI investments in a report.
Business Insider spoke with McKinsey's Larry Lerner about what will separate winners and losers.
The bill is coming due for Wall Street banks' AI investments.
It's been two years since generative AI captured the attention and dollars of bank leaders. They amassed teams of technologists to experiment with generative AI and run proofs of concepts. Some of those have since scaled to enterprise-wide initiatives used by thousands of employees. Now, leaders are beginning to question when these investments will pay off.
"That is the $20 billion question," according to Larry Lerner, a partner in McKinsey's banking practice.
For a handful of firms, Lerner said tangible returns are starting to emerge in the form of current cost savings, future cost avoidance, and incremental revenue. But for many, the reality is "POC purgatory," Lerner said, referring to proofs-of-concept pitfalls where firms get stuck in the experimentation phase and "become very tepid about really leaning in." In those cases, the "institution has spent the last two years investing and investing and not seeing anything at all," Lerner said.
According to an October report from Evident AI, which tracks AI adoption in financial services, only six out of 50 banks disclosed dollar-level cost savings or revenue lifts as a result of their AI investments.
So, what separates the frontrunners from the laggards? According to fresh research from McKinsey, it can come down to a few key decisions around concentrating efforts on a couple of uses, having CEO buy-in, and using generative AI in conjunction with other technologies. Most of all, it'll involve a mindset shift where AI is viewed and treated as a business opportunity rather than a technological problem, Lerner said.
Lerner outlined what will separate the winners from the losers. He declined to comment on specific companies.
Viewing AI as a business problem, not a tech one
Leadership teams have to recognize that generative AI is a business opportunity, not just a technology play, Lerner said. Because of that, he said business leaders should bear the brunt of the accountability, rather than that responsibility falling solely on tech leaders' shoulders.
"The institutions that make business leaders accountable for delivering their results will over time tend to do better because there's a much stronger partnership," Lerner said.
Concentrating firepower
Generative AI has lead to more value when there are only a handful of use cases, instead of every business unit doing a little bit here and there and seeing what sticks, Lerner said.
"Instead of having 60 use cases across 15 different business lines and functions, narrow down to three areas where you want to go deep," where you're reimagining the entire domain or workflow has led to a faster path to value, Lerner said.
Choose areas where ROI can actually be tracked
It's becoming increasingly clear that generative AI's main strength in saving workers time can't always be traced back to bottom-line impact, which is leading to some frustration in the boardroom.
"The value of what you're doing depends on how you're going to repurpose your time, and that's really hard to do," Lerner said. "Because it's an indirect sort of lever, it's very difficult to actually measure and get people to agree that there's value."
On the other hand, AI tools like call-center copilots and AI-powered marketing campaigns that improve the customer experience can generate incremental value that is measurable, Lerner said. One large bank referenced in the McKinsey report is projecting a 10% revenue increase thanks to a new analytics platform to target new customers and cross-sell products to existing ones.
For buy now, pay later fintech Klarna, leveraging an OpenAI-powered call center agent is estimated to bring some $40 million in profit this year, the company said in a blog post earlier this year. At the time, the AI was doing the work of 700 full-time agents, according to Klarna.
Lerner said he's starting to see some banks modify forward-looking hiring plans, especially in the contact center, thanks to the increase in self-service and faster resolution times. "That cost avoidance is absolutely measurable," he said.
Reusability is key
Build something once and redeploy it a hundred times, Lerner said. Doing so can accelerate development times and let companies scale faster because the tool has already gone through the required risk, security, and compliance approvals, he said.
Execution will come down to adoption
Getting workers and customers to adopt a new way of doing something or a new technology is one of the most important parts of the value equation. It's an old challenge that banks have had with previous technology cycles. When it comes to AI, "most companies have done a pretty bad job of getting adoption to the level that's going to yield the results that they want to yield," Lerner said.
Reflexivity cofounders Jan Szilagyi and Giuseppe Sette.
Reflexivity
Reflexivity is a startup cofounded by two former hedge fund traders.
It sells software to hedge funds and institutional investors to speed up the research process.
The 4-year-old upstart just raised its $30 million Series B.
A startup looking to transform how investors and traders use data just received funding from some of the biggest names in the hedge fund world.
Reflexivity, formerly known as Toggle AI, raised its $30 million Series B in late October. Interactive Brokers and Greycroft led the round, which included participation from billionaire investor Stanley Druckenmiller and Greg Coffey, the Australian founder of hedge fund Kirkoswald. Existing investors include Millennium Management's founder, Izzy Englander, and General Catalyst.
Reflexivity was founded by two former hedge fund traders who were all too familiar with the woes of wrangling disparate data sets to find an investing edge, or at the very least, to not miss out on an opportunity others are seizing. The startup aims to mitigate that by combining third-party data from a dozen providers like S&P Global and newsfeeds, in addition to proprietary internal information, for a full-view analysis and also flagging the potential impact world and market events may have on a portfolio.
"When you are an investor inside a major hedge fund, one big fear that is always present is that you are going to miss something," cofounder and CEO Jan Szilagyi told BI. "It's always exciting to have a trade that you are the only one that's in it, but the thing that is far worse is to miss on the trade that everybody else but you is in."
The four-year-old startup recently changed its name to more closely align with how the platform helps with the investment process, Szilagyi told Business Insider.
"Reflexivity is the act of examining one's own assumptions, beliefs, and judgment systems, and thinking carefully and critically about how these influence the research process," he said, referring to a term popularized by legendary investor George Soros.
Szilagyi was a portfolio manager for nearly 20 years at firms including Druckenmiller's Duquesne Capital Management and Fortress Investment Group. The fintech's president and other cofounder, Giuseppe Sette, also worked in asset management including a stint at macro giant Brevan Howard. They remember the investment analysis process as one that "seemed hopelessly broken" because critical data sources were fragmented and spread out across different providers and systems, Szilagyi said.
The firm estimates the potential market for its services is $16.4 billion. Reflexivity so far has about 20 institutional clients, tallying some 15,000 individual users. Clients include trading platforms like Interactive Brokers, banks, including Japan's largest in MUFG, and several hedge funds, including Millennium Management, Soros Fund, and ExodusPoint. The startup was highlighted as one of Business Insider's up-and-coming fintechs in 2023.
It has a valuation between $115 million and $150 million, Szilagyi said.
How Reflexivity works
The upstart's platform lets stock-picking investors analyze data that covers about 40,000 securities from a dozen different providers, including Refinitiv and the London Stock Exchange Group, the Federal Reserve, and S&P Global. It is also built to alert customers, mostly discretionary investors who work at hedge funds and asset managers, to market events and their potential ripple effects on a given portfolio.
If there's a big move in treasury yields, Reflexivity will automatically examine the ripple effects and see how that could impact banking stocks. In this hypothetical example, Reflexivity would see that its user, say a hedge fund trader, has Wells Fargo stock in her portfolio, and flag to her that Wells Fargo stock historically reacts very well to a rise in yields.
Behind the scenes, a proprietary knowledge graph and generative AI-powered user interface helps users connect the dots and better understand investing relationships, Szilagyi said.
Szilyagyi says he also has an answer to a question many Wall Street technologists are facing with hallucinations, or generative AI's tendency to make up answers that are presented as fact.
Reflexivity's answer is a so-called closed system, wherein the AI models can only pull answers from data that's been pre-vetted by the startup. The reason other models, like OpenAI's ChatGPT, hallucinate is because it operates on an open system that takes in data from anywhere on the internet, Szilagyi said. If it can't find anything, it'll be inclined to make up an answer because these tools are built to deliver some kind of answer, he said.
On top of that, Reflexivity also programmed its models to not force an answer to every question. About 5% of the time, Reflexivity will say it doesn't have the ability to answer a given question if it's unable to generate an answer from the data it's been given, Szilagyi said.
"For finance professionals, the ability to get the candid and honest answer is absolutely critical because it only takes one, two hallucinations to be extremely costly when it comes to trading," Szilagyi said.
Here's Reflexivity's pitch deck it used to raise its Series B.
(Because the startup only recently changed its name, these slides include its former name, Toggle AI.)
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Reflexivity
Pitch deck that Reflexivity, formerly Toggle AI, used for its $30 million Series B
Iris Finance's Intel Chen; Brico's Snigdha Kumar; Materia AI's Lucas Adams; Clerkie's Guy Assad
Iris Finance; Brico; Materia AI; Clerkie
Business Insider asked 27 venture-capital investors to nominate the most promising fintechs.
Fintechs using AI to help Wall Street firms, bankers, and consumers lead this year's series.
Here are 15 top AI-powered fintechs, according to VCs.
Fintech investors still see at least one bright spot in the industry, despite funding to the sector hitting one of its lowest points since the pandemic in 2024.
However, the dealmaking drought could ease up in the next year as a result of antitrust scrutiny softening and VCs might be more willing to open up their pocketbooks. One area that investors will likely hone in on is AI.
Business Insider asked dozens of VCs to identify the most promising fintechs to watch last fall. Nearly one-quarter of the startups they named are leveraging AI as a key part of their offering. Indeed, it is difficult to point to one area of finance where AI startups aren't threatening to change the way people bank, invest, save, and work.
Some of the startups on this list are business-facing, helping dealmakers negotiate debt agreements, streamlining tedious processes for junior bankers, or automating manual processes for accountants and CFOs. Others use AI to serve consumers, whether it's helping them figure out the best way to pay off debts or maintaining access to healthcare between jobs.
The startups named haven't raised beyond a Series C and include a mix of investors' portfolio companies and ones they have no financial interest in.
Here are 15 of the most promising AI fintechs to watch, according to top VCs.
BeatBread
BeatBread cofounders, Peter Sinclair, CEO, and John Haller, COO and chief data scientist.
BeatBread
Cited by: Deciens Capital (investor)
Total raised: More than $150 million
What it does: BeatBread uses AI to analyze and predict revenue potential for the music industry, providing funding advances to a broad range of artists.
Why it's on the list: "Artists of all sizes want independence and ownership over their music, to work with their preferred partners, and to control their own destinies. Historically, there hasn't been a real alternative to the major label advance for artists to get the capital they needed to scale their careers, which locks them into the label ecosystem," Dan Kimerling, the managing partner at Deciens Capital, said.
"2024 has been a pivotal year for BeatBread, marked by strategic moves and partnerships that further solidify its mission," Kimerling said, referring to its partnerships with the administrative publishing company Kobalt and its subsidiary AMRA to offer artists increased royalties and faster payments. Other strategic moves include a series of deals providing funding to independent labels to expand how BeatBread provides capital to artists.
Brico
Brico cofounders Edward Swiac and Snigdha Kumar.
Brico
Cited by: TTV Capital, Homebrew
Total funding: $8.1 million
What it does: Brico helps financial institutions and fintechs manage their licensing by using AI to automate applications and renewals.
Why it's on the list: "With Brico, businesses can effortlessly navigate the complexities of acquiring, renewing, and managing compliance for various financial licenses — including Credit, Money Transmitter, Mortgage Loan Originator, and more — in all 50 states," Lizzie Guynn, a partner at TTV Capital, said. "Brico makes regulatory compliance seamless and cost-effective with its user-friendly tools that reduce time and money spent on financial licenses."
"It's addressing a very manual and expensive process that nearly every financial services company needs to deal with on an annual basis," Satya Patel, a partner at Homebrew, said.
Cascading AI
Cascading AI cofounders Isaiah Williams, CTO, and Lukas Haffer, CEO.
Cascading AI
Cited by: QED Investors, Vesey Ventures
Total raised: $4.1 million
What it does: Cascading AI, through its main product Casca, offers loan-origination software for the banking sector with an integrated AI assistant that allows firms to extend their hours.
Why it's on the list: "Customers do not operate on the 9-to-5, Monday-to-Friday schedules that banks do," Laura Bock, a partner at QED Investors, said. "When a pizzeria's oven breaks, the owner is inquiring about a loan after closing shop. While today, it might take nearly three days to hear back from a loan officer after submitting an application, financial institutions using Casca's AI platform are able to unlock 24/7, 365 support for current and potential customers."
Dana Eli-Lorch, a founding partner at Vesey Ventures, said: "Their flagship product, an AI-powered loan assistant, enables manifold increases to banks' productivity and loan conversion rates, all while enhancing both accuracy and applicant experience. Casca exemplifies the powerful impact AI can have on financial services, driving significant operational efficiency and customer satisfaction."
Clerkie
Clerkie's Guy Assad, CEO, and Sebastian Wigstrom.
Clerkie
Cited by: Flourish Ventures (investor)
Total raised: $41 million
What it does: Clerkie embeds its AI debt-automation software in financial institutions' mobile apps, allowing consumers to make financial decisions about their debts and discover solutions if they're struggling to pay them off.
Why it's on the list: "Clerkie's data flywheel and network create a win-win scenario for both consumers and financial institutions. Consumers enjoy a seamless experience within their banking app, with flexible solutions tailored to their specific cash flow needs, helping them avoid the collections process and protect their credit scores. Banks benefit from direct ROI through loan repayment while maintaining customer relationships," while also expanding loan-to-value ratios, Flourish Ventures' Emmalyn Shaw said.
She added that "Clerkie assumes no balance-sheet risk, serving as the debt-network and debt-payment infrastructure for financial institutions."
Comulate
Comulate cofounders Jordan Katz, CEO, and Michael Mattheakis, CTO.
What it does: Comulate automates insurance statement processing, reconciliation, revenue recovery, and forecasting.
Why it's on the list: "Leveraging AI to drive real revenue lift for insurance carriers is driving success in a category" that's historically been hard to break into, Charley Ma, a cofounder and managing partner of Pathlight Ventures, said.
Coris
Coris cofounders Shyam Maddali, CTO, and Vinodh Poyyapakkam, CEO.
Coris
Cited by: Pathlight Ventures (investor)
Total raised: $3.7 million
What it does: Coris builds software for fintechs and other tech companies to manage risk and fraud among small- and medium-size business clients.
Why it's on the list: "Aggregating unstructured data on SMBs to generate insights at scale is challenging. Coris is at the forefront, leveraging a variety of methods across LLMs, ML, and good old-fashioned software to establish itself as the leading platform for managing SMB risk and fraud — already working with clients like Mindbody and ClassPass," Pathlight's Ma said.
Fintary
Fintary founder Qiyun Cai.
Fintary
Cited by: Harlem Capital (investor)
Total raised: $2.5 million
What it does: Fintary helps insurance companies manage their finance and accounting needs by using AI to automate workflows.
Why it's on the list: "They have been invited to their customers' conferences in order to share the product with their customers' customers," Henri Pierre-Jacques, the cofounder and managing partner of Harlem Capital, said. He said Fintary has grown more than 10 times since Harlem's investment last fall, adding that "the quick ramp has been one of the fastest we've seen for a preseed company."
Greenlite
Greenlite cofounders Will Lawrence and Alex Jin.
Greenlite
Cited by: Greylock (investor)
Total raised: $4.8 million
What it does: Greenlite automates compliance processes using AI for fintechs and banks.
Why it's on the list: "Greenlite has seen exceptional customer demands with enterprise banks and fintechs and has proven one of the few enterprise-grade applications for generative AI — automating tedious compliance workflows like alert handling, periodic reviews, and document processing, improving efficiency and reducing human error," Seth Rosenberg, a general partner at Greylock, said.
Iris Finance
Iris Finance cofounders Alex Heckmann, Drew Fallon, and Intel Chen.
Iris Finance
Cited by: Redpoint Ventures
Total raised: $3.5 million
What it does: Iris Finance offers consumer-facing companies AI-powered financial planning and analysis software.
Why it's on the list: "While the notion of AI bookkeeping is very much in vogue today, replacing Quickbooks is hard — and not something most brands or outsourced accountants are looking to do in the near term," Redpoint's Clark said. "Iris, instead, complements Quickbooks with a more holistic AI-powered CFO-in-a-box for brands, enabling them to seamlessly track and improve day-to-day sales and margin performance across channels, which much more closely aligns with what founders want and how modern brands are managed."
Materia AI
Materia AI cofounders Kevin Merlini and Lucas Adams
Materia AI
Cited by: Bain Capital Ventures
Total raised: $6.3 million
What it does: Materia AI helps accountants organize their data, enabling them to automate parts of their work.
Why it's on the list: "With a decline in new auditors and an immense volume of manual data entry, professional-service audits are the perfect place for an AI copilot," Alysaa Co, principal at Bain Capital Ventures, said. "LLMs enable the automation of work like ingesting large sets of unstructured financial data, searchability, comparing against historicals and across the industry, and direct citations for where the data comes from."
Nilus
Nilus cofounders Daniel Kalish and Danielle Shaul.
Adi Eckstein
Cited by: Vesey Ventures (investor)
Total raised: $8.6 million
What it does: Nilus offers an AI-powered cash and treasury management platform for fintechs, financial firms, marketplaces, and other companies moving money.
Why it's on the list: Nilus "provides better data connectivity combined with AI to transform the CFO suite: a trend we are actively investing behind," Lindsay Fitzgerald, a general partner and the cofounder of Vesey Ventures, said.
"With Nilus, treasurers can skip the manual reconciliation work that previously took most of their day and focus on actions that can drive bottom-line impact. We think Nilus is poised to become the default software for modern treasury teams, displacing decades-old workflow tools like Kyriba and GTreasury," she said.
Noetica
Noetica AI cofounders Dan Wertman, Tom Effland, and Yoni Sebag.
Noetica AI
Cited by: Avid Ventures, Index Ventures
Total raised: $7.85 million
What it does: Noetica helps deal professionals negotiate debit agreements with their data using an AI platform that benchmarks terms in corporate debt transactions.
Why it's on the list: "Noetica is a capital-markets data company for corporate debt, a market valued at trillions of dollars. Its AI-powered software allows professionals to upload any credit or bond document and compare all terms to similar public and private deals," Jahanvi Sardana, a partner at Index Ventures, said.
"Corporate debt terms are time-consuming and difficult to benchmark, leading deal professionals, such as lawyers and investment managers, to often miss higher-risk terms, as well as opportunities for negotiation. By building the largest proprietary dataset of corporate debt terms, Noetica is changing how these deals are negotiated and transacted," Tali Miller, a founding investor at Avid Ventures, said.
Novella
Novella founder and CEO Max Kane.
Novella
Cited by: Avid Ventures (investor)
Total raised: $2.5 million
What it does: Novella is an AI-powered insurance wholesaler specializing in excess and surplus insurance, which addresses higher-risk situations that standard carriers don't usually cover.
Why it's on the list: "Given the complexity of E&S insurance, it is sold through wholesalers who have relationships with specialty carriers, and retail brokers must work with these wholesalers to access these carriers. However, brokers have been frustrated by the leading wholesalers such as Ryan Specialty, Amwins, and CRC Group, whose lack of technology and system integrations lead to slow, inefficient, and opaque quoting processes," Avid Ventures' Miller said.
"The E&S market continues to grow," Miller said, adding that E&S direct premiums written in the US climbed to more than $86 billion in 2023, more than doubling since 2018.
"Using data and AI, Novella aims to reinvent this massive industry by making the information transfer between brokers and carriers fast and error-free and, ultimately, automating quote creation," she added.
Rogo
Rogo cofounders Tumas Rackaitis (CTO), Gabriel Stengel (CEO) and John Willett (President).
Rogo
Cited by: Two Sigma Ventures
Total raised: $26 million
What it does: Rogo is building a generative AI assistant to help investment bankers and analysts do their jobs more efficiently.
Why it's on the list: "Rogo's platform is purpose-built for the complex data needs of the financial sector, allowing nontechnical users to query vast amounts of financial data using natural language processing. This is a game changer for institutions like banks, investment firms, and insurers," Frances Schwiep, a partner at Two Sigma Ventures, said.
"I see immense potential in Rogo's ability to give first-of-its-kind access to critical financial analytics, positioning them as a key player in transforming how financial institutions interact with their data to drive more informed decisions across the industry," she added.
What it does: When uses an AI assistant to help exiting employees maintain access to healthcare by providing affordable alternatives to COBRA and making it easy to compare pricing and deductibles.
Why it's on the list: "There are more than 700,000 companies in the United States with 20-plus employees, which means they are required by law to offer COBRA. Last year's 721,677 planned job cuts brought some of the largest reductions in company head count that we've seen in the past two decades," TTV Capital's Guynn said.
"Offering an alternative to expensive, inflexible COBRA not only makes common sense but also economic sense. COBRA participants are three times more costly than active employees, which is especially burdensome for self-insured companies. To date, companies that offer When's fixed-dollar health-insurance premium reimbursement have seen an 80% conversion rate from COBRA. Employees that applied their When benefit to available plans have saved as much as 50% in out-of-pocket healthcare costs," Guynn said.
Two of those are fictional movie characters, and one was based on a real person, but they've all shaped the public's perception of what working on Wall Street could be like.
If you ask successful people at some of the biggest banks, asset managers, trading firms, or hedge funds whether they see their reality accurately perceived on the screen or in books, they'll tell you that working on Wall Street is a little less colorful than it's often painted to be.
"I don't know that there's a great movie or book depicting life on Wall Street," Mark Zhu, 34, a managing director at Blackstone, told Business Insider. "The day-to-day is a lot more boring than you think. It's a lot of calls and a lot of emails. There's not as much flamboyance or out-there behavior. It's almost not movie-worthy. Why would you pay money to watch somebody just sit in front of a computer doing Zooms?"
So maybe they think all that partying on HBO's show about twentysomething investment bankers, "Industry," is a little overdone, but there are still some elements the entertainment industry gets right occasionally.
We asked up-and-comers on Wall Street about the shows, movies, or books that best represent their daily lives. While no one representation was perfect, the young professionals talked about the parallels they saw. Some even shared some nonfinancial references that give a window into their world.
Here are the shows, movies, or books that give a flavor of what it's like to work on Wall Street.
Shows: "Industry"
"Industry" follows junior bankers at a fictional elite institution in London.
Amanda Searle/HBO
The hit TV show "Industry" — full of sex, drugs, and spreadsheets — just wrapped up its third season.
"My friends in the last few years have nonstop bothered me about 'Industry,'" Justin Elliott, 29, a vice president of institutional rate sales at Bank of America, said.
"They see a crazy show about the industry and say, 'My God, I can't believe that happens in your world every day.' From what I've seen, there's definitely some thrills from getting a trade done that might mirror the show a bit, but it's a very exaggerated depiction of life on Wall Street."
"I don't know that any of them do a great job, but I am quite a fan of 'Industry,'" Erica Wilson, a vice president at the private credit firm Blue Owl, said. "I am still behind on the third season, but I think that show is fun."
"Succession"
"Succession" siblings fight it out over four seasons for the future of their father's media conglomerate.
David Russell/HBO
Though the blockbuster show "Succession" isn't specifically about the banking industry, Daniela Cardona, a 29-year-old investment banker at RBC Capital Markets, watched it in its entirety and found some similarities in high-stress moments.
"In the last season, when they're trying to merge the two companies, there's one scene that always makes me giggle. I don't think this is fully accurate, but I do think it's funny — they're in a conference room, and Kendall says, 'Just make it up!' and they're all with their laptops sitting in the middle, and the consultants are looking at him like, what do you mean, make it up?" Cardona said.
"There have been instances where it sometimes feels that way — where you're in a time crunch and it's 3 o'clock in the morning."
"Scrubs"
"Scrubs" follows a group of medical students learning the ropes.
ABC/Photofest
Ben Carper, a 34-year-old managing director at Jefferies, pointed to the medical comedy sitcom "Scrubs" as a better representation than anything that features board rooms and trading floors.
He said the show had a "similar high-pressure environment where there are some opportunities for amusement and humor, but generally a pretty vigorous focus on doing a job well done."
Movies: "Margin Call"
"Margin Call" takes viewers inside a nameless financial institution.
Roadside Attractions
The 2011 drama "Margin Call" follows the 24 hours after an analyst at an investment bank discovers it has taken on more debt than it can handle — illustrating the early stages of the 2008 financial crisis.
"I think it picks up the cadence of working at a big bank the best," said Austin Anton, 32, a principal at Apollo Global Management.
"The Wolf of Wall Street"
Leonardo DiCaprio plays Jordan Belfort in the Martin Scorsese-directed film.
Paramount Pictures
"The Wolf of Wall Street" follows the story of Jordan Belfort, who actually only worked at a Wall Street firm for a few months before the 1987 stock-market crash. He goes on to run his own brokerage, which ultimately scams several people, but the movie highlights the debauchery, opulence, and excess that ensued during his run.
"This almost sounds weird, but I'm going to say 'The Wolf of Wall Street,'" Matt Gilbert, a managing director at Thoma Bravo said. "The absurdity of that movie, to some extent, I do think, kind of incorporates some aspect of our job."
While finance is the backbone of the economy and certainly has global implications, what bankers and investors do on a day-to-day basis isn't saving lives, the 35-year-old added.
"I think the fact that you could have a comedy wrapped around the finance world is important, and it always makes me take a step back and think through, sure, I want to win every deal," he said. "Our fiduciary duty at Thoma Bravo is to produce the best returns for LPs, but this job is supposed to be fun. I'm supposed to work with great people. We're supposed to laugh together. I think if people take this job too seriously, that's when burnout and other things happen."
"The Big Short"
"The Big Short" follows several Wall Street players as they begin to piece together what was happening to the American housing market.
Paramount Pictures
"The Big Short," the movie based on the financial journalist Michael Lewis' book, chronicles how Wall Street helped fuel the US housing crisis in 2008 and the investors who profited from it.
"It's not our day-to-day, but I think it is an OK representation of what happened at the time," said Chi Chen, 34, a portfolio manager at BlackRock. " Maybe it is not all factual, but it is a good one that is representative."
"The Internship"
Starring Owen Wilson and Vince Vaugh, "The Internship" actually shot some scenes at Google's headquarters.
20th Century Fox
Patrick Lenihan, a portfolio manager at JPMorgan Asset Management, said "The Internship," which features two old-school salesmen trying to restart their careers through an internship at Google, reminds him of the importance of having and supporting a diverse team.
"I feel like that team with Owen Wilson, Vince Vaughn, the rest of them, and how they come together at first, you see there's just a variety of different people that you're like, 'Oh, this is going to fail,'" he said. "But I think a large part of my success is going back to that teamwork, getting the right people in, and ensuring that diversity of opinions."
Books: "Market Wizards"
Amazon
BlackRock's Chen, who focuses on fixed income, said that to really gain insight into the investing industry, it's best to read the "Market Wizards" book series, which features interviews with top traders.
"A lot of those investing stories for that book series are more from two, three decades ago, when market volatility was much higher. But we have seen a comeback of market volatility since 2020," she said. "So I have always enjoyed that whole series of books."
"Free Food for Millionaires"
Amazon
Elliott, the Bank of America VP, recommends Min Jin Lee's novel "Free Food for Millionaires."
"It's about a Korean woman navigating life who ends up on Wall Street in an admin capacity. But really, it's a story about belonging and identity — about trying to make it in a world and industry you didn't initially know much about," he said.
"To me, it's a lot more humanistic. It gives me a bit more of a personal perspective when I think about my journey on Wall Street. When I think about the people — and understanding people is so much of this job — I go back to 'Free Food for Millionaires.'"
"The Man Who Solved the Market"
Amazon
There's no fictional piece of media Bridgewater's Blake Cecil has found to reflect life in finance; he said shows and movies "feel quite distant" from his day-to-day.
A biography of the late hedge-fund billionaire Jim Simons, "The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution," reflects how the deputy chief investment officer and his colleagues approached challenges.
"It resonated with my experience of working with people who are using algorithms to solve problems that often hadn't been asked before," Cecil said.
"The Inner Game of Tennis"
Amazon
Harrison DiGia, a vice president at General Atlantic, had another book recommendation: "The Inner Game of Tennis" by W. Timothy Gallwey.
"This book is all about the mental game and trusting your intuition and yourself. You use practice and your preparation before a competition so that when the time is right, or you have a big opportunity, you're ready, and your mental game is as strong as it can be," DiGia, 31, said.
"When I think about investing, a lot of it is setting yourself up to get that big opportunity and making sure you're prepared and can have a clear mind when that pressure situation comes. I'm a huge tennis fan, so I think about this when I'm on the tennis court, but I think about it in a professional setting as well."
"Unreasonable Hospitality"
Amazon
In the book "Unreasonable Hospitality: The Remarkable Power of Giving People More Than They Expect" by Will Guidara, the co-owner and general manager of Eleven Madison Park describes how he manages his business, his customer-service style, and the things he'd do at Eleven Madison Park to go above and beyond.
Craig Kolwicz, an investment banker at Moelis, said the "unreasonable hospitality" described in the book (such as having an employee run out to get a hot dog for a customer who you overheard saying they hadn't had one in New York yet) isn't dissimilar to the type of service that could differentiate an investment banker.
"It depicts a restaurant that's an extremely expensive restaurant where there's an extremely discerning clientele base. They could go to all these other really fancy, really nice three-Michelin-star restaurants in New York or in the world," the 35-year-old managing director said.
"How do you differentiate yourself? There's a lot of investment bankers out there and there's a lot of really smart clients and folks that we work with all the time — and how do we get them to stay with us? How do we get them to hire us on the next deal? It's some of the stuff that we do," he said. For example, he'd recently flown to Los Angeles for an 11:30 a.m. pitch meeting and flown back.
"It's like hospitality, but it's kind of an unreasonable client customer service to do something like that," Kolwicz said.
Banks are keen to develop AI agents to assist human employees.
mikkelwilliam/Getty Images
Finance firms are keen on AI agents that can automate combinations of tasks.
Demand for AI agents is giving birth to a new class of startups and VCs hungry to invest in them.
It was a topic of conversation at the Evident AI Symposium in New York on Thursday.
"Talk to this like a teammate and treat it like a teammate."
That's Danny Goldman's guidance to private-equity customers of his startup, Mako AI, which offers a generative AI assistant for junior finance professionals and is backed by Khosla Ventures, an early investor in OpenAI.
His hope is that "engaging with Mako looks much more like engaging with a real human associate than a software tool," he previously told BI. Goldman, who worked in private equity before cofounding Mako AI, predicts that in a year or two, every junior on Wall Street will have their own AI direct report.
It's not just juniors, either. JPMorgan CEO Jamie Dimon, is a "tremendous user" of the bank's generative AI assistant suite. Teresa Heitsenrether, JPMorgan's chief data and analytics officer, said at a conference last week that JPMorgan is working toward giving employees AI assistants that are specific to them and their jobs.
Wall Streeters, say hello to your new coworker. Across the industry, AI agents are beginning to permeate the labor force as assistants who can help humans prep for meetings, write their emails, and wade through troves of information to answer questions almost instantaneously.
In many cases, AI agents are still limited to specific, individual tasks like querying internal data and creating PowerPoints and emails. To take AI agents a step further, technologists and startup investors are fueling a shift to so-called multi-agent systems that coordinate several AI agents to complete more complex tasks more autonomously.
Some tech executives at the Evident AI Symposium said they could see a world with more artificial intelligence agents than humans by 2025. But what will work and life look like in an increasingly hybrid world with humans and bots? Well, that's still being worked out, according to a number of tech executives at the Evident AI Symposium Thursday.
"What's really exciting about agents is that we are still figuring out the tasks they're actually good at, the tools they know how to use, the tools we have to teach them how to use," said Gabriel Stengel, cofounder and CEO of Rogo, which is building the generative AI equivalent of a junior banker.
Another question that still needs to be answered is how to define when an agent is smarter or not than a human, said Kristin Milchanowski, chief AI and data officer of BMO Financial Group.
To some extent, benchmarking humans against AI agents is already happening. In a recent University of Cambridge study that compared who could run a business better, AI outperformed humans on most metrics including profitability, product design, and managing inventory. But they fell short when it came to making decisions on the fly.
Heitsenrether, speaking at the Evident AI conference, told the audience that, over time, she expects AI to be seamlessly embedded in an employee's workflow. By this time next year, she said that she hopes to have a clearer picture of what a more personalized AI assistant for each employee might look like.
But unlocking more autonomous uses of AI is going to require more than technological breakthroughs.
"We don't have a lot of trust right now in these systems," Sumitra Ganesh, a member of JPMorgan's AI research team, said at the symposium.
"We have to slow-walk it to release it to people who are experts who can verify the output and go, 'Okay, that looks fine, you can take that action,'" Ganesh said. "But that's kind of babysitting these agents at this point," she added. "But hopefully, it's like training wheels — at some point, we will be confident enough to let them go."
Coding languages are a foundational element of any tech job, but not all are made equal.
Python and SQL are among the most popular languages; C++ and Tableau are more specialized.
Business Insider spoke with recruiters and tech workers to identify the top eight languages to know.
Big Tech firms like Apple and Amazon have signaled a move away from the complicated coding language C++, but there's still a place for engineers who know the coding language on Wall Street.
Apple created Swift to replace its use of C++, the company's primary coding language for its devices. Amazon recently awarded a Stony Brook University professor an approximate $100,000 research grant to continue his work to automate converting existing C++ code to Rust, a coding language created in 2006. Even the White House has joined the conversation around C++, urging software developers to move away from the language due to cybersecurity concerns in a February report earlier this year.
But the financial space is still "one of the heavy users of C++ that's really doing cutting-edge stuff," one industry executive told Business Insider. High-frequency trading firms and exchanges rely on C++, a notoriously complicated language that can offer more control over the underlying hardware. It's also prevalent in the video game industry.
Citadel Securities, for one, recently hired a C++ expert from Microsoft to lead training initiatives on the language. Looking at current open technology positions, trading firms Virtu Financial and Hudson River Trading are among the firms also seeking out C++ experience.
It's also good to know in quantitative finance, one of the few bright spots in the current technology hiring slump, Matt Stabile, a tech recruiter who works with buy-side firms including Two Sigma and Susquehanna International Group, told BI.
In today's machine-to-machine world, having some experience with programming languages is a must. Coding languages, like Python and Java, are how humans can communicate with computers by providing a set of instructions for a system to execute. As it turns out, not all programming languages are made equal and some are more relevant to certain corners of Wall Street than others.
Business Insider spoke with recruiters, Wall Street tech execs, and industry insiders and analyzed job postings to learn about in-demand skill sets.
Here are the programming languages to know:
Python
ATHVisions/Getty Images
Areas of interest: Applicable across finance firms, job titles, and levels
Firms using it: Banks, hedge funds, and investment firms
As the fundamental language for engineering work across Wall Street, Python has long been at the top of the skills list for buy- and sell-side firms alike. It has been a favorite at Capital One and Man Group.
From visualization to statistical analyses to modeling and machine-learning applications, Python has multiple use cases. It also lends itself to those who don't have deep coding backgrounds because it is flexible and applies to a wide range of users, Ori Ben-Akiva, director of portfolio management at Man Numeric, a quantitative-focused division of the publicly traded hedge fund Man Group, previously told BI.
When it comes to data science and machine learning roles, "Python is king of the road," said Stabile, who runs his own recruiting shop called Stabile Search.
SQL
EvalCo/Getty Images
Areas of interest: Anyone who works with databases, data
Firms using it: Almost every financial firm
As data becomes more centralized in financial firms' strategies — from marketing to identifying new deal opportunities and analyzing risk — it's helpful to know SQL, which is one of the most common and basic ways to query or pull information from a database.
SQL is a relational database language, meaning it's designed to be able to tie different data tables together. For any tasks that have to do with analytics, you'll likely find SQL, Deepali Vyas, global head of fintech, payments, and crypto at Korn Ferry, told BI.
C++
Tetra Images/Getty Images
Areas of interest: Low-latency applications
Firms using it: High-frequency trading players and exchanges
For applications and systems where speed (or a fast response time) is the name of the game, experience as a C++ developer is going to come in handy. That's especially true at high-frequency trading firms and exchanges, where companies edge each other out by being microseconds faster than the competition.
The coding language has a reputation for being trickier to master than others, and its ability to interact more closely with technical hardware can lead to nasty coding bugs, but it also generally affords the user more control and speed.
Tableau and Power BI
gorodenkoff/Getty Images
Areas of interest: Data visualization, front-office analysts
Firms using it: Wealth managers, banks
When Wall Street tech execs talk about data, it's often broken up into organizing it and finding insights within it.
Korn Ferry's Vyas said the latter benefits from tools like Tableau and Power BI, which visualize and contextualize data. These types of graphics are especially useful if you work in wealth management or advisory, where dashboards and data tables are regularly used.
Java
funky-data/Getty Images
Areas of interest: Big banks with more legacy technology
Firms using it: Banks and some buy-side firms
Like Python, Java is widely used on Wall Street. The coding language secured an early foothold in the world of banking because it was believed to have security features that restricted data access, while also offering portability, or the ability to be transferred between machines.
As a result, many big banks are tethered to Java, but other firms like Two Sigma have also relied on the coding language.
Rust and Go
Oscar Wong/Getty Images
Areas of interest: App development
Firms using it: Fintechs, banks
Technically, many of the coding languages on this list — like Python, for example — are open source, or available for developers to use without a proprietary license.
But several open-source languages have become more in-demand in recent years, including Go and Rust. When the banking fintech Stash built much of its core banking offering from the ground up in 2022, tech leaders at the company highlighted the use of Go — which they said was picked up quickly by engineers and cut the implementation time for "substantial" new pieces of code to roughly 3.5 days.
Fintechs aren't the only financial firms embracing Go and other open source tools. At Blackrock, much of the firm's cloud work was built upon open-source software. Wells Fargo in recent years has embraced Rust and Go as languages the bank is becoming more comfortable.
Editor's note: This article was originally published in 2022 and has been updated with new information.
Former BI reporter Carter Johnson also contributed to the previous reporting.
JPMorgan tech exec Teresa Heitsenrether talked about the bank's ongoing adoption of generative AI.
The bank has rolled out its "LLM suite" to 200,000 employees.
Speaking at the Evident AI Symposium, Heitsenrether explained how it's been taken up and by who.
Before a business review with JPMorgan CEO Jamie Dimon, Teresa Heitsenrether runs her presentation through one of the bank's generative AI tools to help her pinpoint the message she wants to convey to the top boss.
"I say, what is the message coming out of this? Make it more concise. Make it clear. And it certainly has helped with that," Heitsenrether, who is responsible for executing the bank's generative AI strategy, told a conference in New York on Thursday.
Dimon himself is a "tremendous user," she said, and is waiting for the ability to use the bank's tools on his phone.
"He's been desperate to get it on his phone and so that's a big deliverable before the end of the year, " Heitsenrether added.
JPMorgan, America's largest bank, has now rolled out the LLM Suite, a generative AI assistant, to 200,000 of its employees.
The tools are the first step in adopting AI technology across the firm. Heitsenrether, JPMorgan's chief data and analytics officer, speaking at the Evident AI Symposium, said that the next generation would go beyond helping employees write an email or summarize a document and link the tools with their everyday workflow to help people do their jobs.
"Basically go from the five minutes of efficiency to the five hours of efficiency," she added, saying it will take time to reach that goal.
'The flywheel effect'
The response to the LLM rollout has been "enthusiastic" and has created "healthy competition" between teams, she said. The wealth and asset management arm was the first division to use generative AI, piloting a generative AI "copilot" for its private bank this summer.
"When the investment bank found out they said 'Well, wait a minute, we want to be on there too,'" she said. "It does create a flywheel effect."
JPMorgan offers courses and in-person training for employees to use the firm's generative AI tools, such as how to prompt a chatbot properly, but the bank is also leaning on superusers, or the 10% to 20% of employees who are "really keen" to help with adoption.
"We embed people within different groups to be the local source of expertise to be able to help people that they work with understand how to adopt it," Heitsenrether explained.
The most common superusers seem to be those who clearly see the benefits of generative AI, such as a lawyer who saves hours by getting a synopsis of contracts or regulations instead of reading them all.
Despite Wall Street's interest in generative AI, getting workers to actually adopt the technology has been a key hurdle for finance firms, Accenture Consultant Keri Smith previously told BI. As a result, training and reskilling efforts have come under the spotlight, she said.
Heitsenrether said that they're trying to engage with the "pockets of resistance" now because it will be harder to convert them once the technology becomes intertwined with workflows.
She also said that the sooner people engage with AI, the less skeptical they are, and they see how it can augment, not replace, their jobs.
"Having it in your hands I think demystifies it quite a lot," Heitsenrether said. She used the example of a developer using it to more quickly write a test case. She said if they see the benefits, they realize "this is not something that's going to be done without me, but it's just a way to make my work that much more effective."
What's next: AI assistants
By this time next year, Heitsenrether told the audience that she hopes she'll be talking about "enabling employees with their own assistant" that's specific to them and their jobs.
Some of the legwork needed to develop those more autonomous forms of AI is currently being done in pilots, Sumitra Ganesh, a member of JPMorgan's AI research team, said during another panel.
Even still, the early use cases for AI workers will likely be constrained because these systems still need a human in the loop to ensure the reliability needed in such a regulated industry.
"We don't have a lot of trust right now in these systems," she said. Having an expert in the loop who can verify AI outputs is "kind of babysitting these agents at this point, but hopefully, it's like training wheels — at some point we will be confident enough to let them go," Ganesh said.
Jeff McMillan is Morgan Stanley's head of firmwide AI.
His team vets all new AI pitches, but business leaders ultimately decide what to use.
He outlines the multiple steps involved from pitching an idea to getting it into production.
If Jeff McMillan does his job right, it will look very different in three years.
"Think about it. We don't have a head of PowerPoint at Morgan Stanley or Excel," McMillan told Business Insider. "These are just enabling technologies," he added.
He was named Morgan Stanley's head of firmwide AI in March to help integrate the technology across the firm. While much of his job these days is focused on getting businesses up to speed with AI and implementing it efficiently across the bank, he said his ultimate goal is for the technology to be ingrained into workers' everyday lives.
McMillan encourages employees to pitch new AI solutions. His firmwide team acts as a filter and vets the ideas, which can come from practically anyone who's done the required training at the bank. To avoid creating an unruly situation where thousands of technologists, analysts, and bankers are building their own AI tools, he's devised a rigorous multi-step process that involves pitching solutions to some of the firm's top execs and devising a business value proposition.
Jeff McMillan was the head of wealth-management tech until his promotion in March.
Courtesy of Morgan Stanley
As part of his role, McMillan co-chairs an AI steering group formalized earlier this year, with Global Director of Research Katy Huberty. The steering committee, which has representatives from each department, vets all AI use cases pitched by employees.
The steering group is working through more than 30 use cases that are in various stages on the way to launch, McMillan said. AskResearch, an assistant that gives investment bankers, salespeople, and traders information found buried in tens of thousands of research reports, is the latest generative AI product to make it through the process since launching McMillan's team.
Many of the pitches the steering committee sees fall into two buckets: use cases that are relevant to several groups, or that matter to a specific team or group of users. For the former, McMillan is able to coordinate teams across the firm to collaborate and build solutions together, with the aim of increasing reusability.
By structuring the AI approval process this way, McMillan hopes to enable the bank to innovate without sacrificing safety.
"While there might be creative tension between experimentation and process, I believe that a rigorous process will ultimately allow us to develop and deploy technology faster and more efficiently," McMillan said.
Inside the 8-step process
Although pitching AI solutions is open to anyone at the firm, there is some leg work involved. Mainly, workers have to complete specific training on governance and AI principles and meet standards around information security.
The AI steering group meets every other week to listen to the pitches, usually going through five or six proposals. The steering group usually either approves or approves with conditions, like rethinking an aspect of the solution or coming together with other teams that pitched similar ideas. In some cases, pitches are rejected — something McMillan says he generally tries not to do.
"I don't want to be in a position where I'm telling people no. I want to tell people yes, and this is the best way to get to it," McMillan said.
For presentations that are approved, the next steps typically involve identifying the people who will execute and figuring out who from tech, legal, compliance, and risk needs to be involved. Workers going through this process also have to articulate deliverables and identify the risks, as well as having a plan for mitigating those risks. That might be a standard set of questions and answers used in testing or making certain teams aware of the potential risk.
They will also have to put together a business value proposition that outlines the benefits, which could be quantifiable, such as decreasing margin or operating costs, finding new revenue streams, or decreasing risk.
Every other week, the AI steering committee meets to review the status of these projects. At the end of the process, the group pitching presents a final time to the steering group for go-live approval to ensure all the conditions are met. Finally, use cases go into production.
"What we're doing is we're helping them prioritize. We're grouping them, and then my team, we handhold you. We say, okay, what are you trying to do? We help you set up the environment. We make sure you've got the right level of APIs, we are by your side as you work with our legal, compliance, and risk process," McMillan said of his business partners.
