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Fintech AI Review #8
Back to school, learning about learning, and the massive opportunities and real-world challenges of applying AI to financial services
Happy September to all of our AI explorers and fintech aficionados. The Fintech AI Review returns after a brief summer hiatus! September means “back to school”, and while my 6 year old son could barely sleep for days given his excitement, this season heralds a rededication to learning and growth for all of us adults as well.
One of the enduring themes of this newsletter has been both the importance of constant learning given the incredible pace of AI development but also what the study of AI can teach us about how humans encode and use knowledge. As AI models and the applications built on top of them achieve ever more impressive feats, we feel compelled to ask deep questions. Should we reconsider the way we think about intelligence and creativity? What does the concept of intellectual property mean in the age of generative AI? More specific to our focus here, to what extent will AI reshape the landscape of financial services, and how quickly? What opportunities are there to use these new technologies to drive economic growth and opportunity?
Today’s newsletter covers a wide range of content. Meta’s LLaMa2 has the potential to spur plenty of follow-on, domain-specific innovation, particularly with its commercial license. Speaking of licensing, Ben Evans wrote an excellent and historically-informed primer on the issues surrounding generative AI and intellectual property. While Mint.com debuted in 2007, the search for what’s next in personal financial management still continues, with LLMs offering a potential step-function increase in usefulness and personalization.
Many large companies are hiring a “head of AI”, though the role differs massively from firm to firm. Speaking of jobs, there’s a chance that AI will create a whole new set of roles that won’t require a four-year degree, somewhat aligning with Noah Smith’s Revenge of the Normies thesis. There’s a chance a new breed of PE firm will drive massive efficiencies through buying companies and then using AI to massively restructure their operation. Read about all this and more below, as I link to 11 articles or papers and offer a brief summary and commentary on each one.
September also brings the beginning of conference season. I’ll personally be at Finovate in NYC (Sep. 11-13), Debanked in San Diego (Sep 21), and Money2020 in Las Vegas (Oct. 23-25). If you’re around for any of those, let me know!
As always, please share your thoughts, ideas, comments, and any interesting content. If you like this newsletter, please consider sharing it with your friends and colleagues. Happy reading!
Latest News & Commentary
Meta recently released a new open-source version of its LLaMa foundational language model - LLaMa2 - which now comes with a license that permits use in commercial applications. The earlier releases of LLaMa, which were not available commercially, were a boon to researchers and open-source developers, as they enabled relatively permissionless experimentation. With the new release, developers can use LLaMa2 models in actual product development, and they can even download and run the model on their own hardware (llama.cpp, for instance, makes it possible to run the model on an M1/M2 mac). Of interest is Meta’s restriction on use of the model in applications with "greater than 700 million monthly active users in the preceding calendar month", presumably to guard against its use by BigTech competitors. While the new model appears not to be as performant as GPT-4 on many tasks, the open-source architecture and permissive license are likely to enable plenty of innovation for the development of domain-specific applications.
The potential for generative AI to upend established conceptions of intellectual property has been a consistent theme in this newsletter. Ben Evans’ newest essay does a typically elegant job of laying out the issues and discussing which are already well understood by existing law or practice and which result in completely new questions. He references parallels to various debates around creativity and authenticity over the past 500 years, from Raphael and Durer to Taylor Swift and Spotify. The debate over intellectual property in the age of generative artificial intelligence is both very old and very new. Either way, it’s far from over, and your perspective will be enriched by Ben’s analysis.
In this wide-ranging research paper, the authors demonstrate how loan pricing from leading fintech lenders mostly relies on traditional methods of risk assessment, namely FICO and other widely-available consumer credit metrics. They argue that a clearly-observed discontinuity of interest rates at FICO 660 (generally considered by regulators and industry as the threshold for ‘subprime’) even for borrowers with otherwise similar levels of predicted delinquency risk amounts to a transfer of value from subprime to prime borrowers. In their words: “The pricing distortions result in substantial transfers from nonprime to prime borrowers and from low- to high-risk borrowers within segment.” While many fintech lenders tout advantages in the use of non-traditional data and advanced statistical techniques in underwriting, this particular analysis shows that FICO is still the most important determinant of interest rates, even when other data are available that would result in a more precise assessment of risk. I often tell my colleagues: “Think of the most sophisticated fintech lender you know - they are less sophisticated than you think, and there’s a ton of opportunity for innovation in risk management.”
In yet another must-read blog post for hands-on AI practitioners in fintech, the team at Ntropy discusses the potential for a new generation of personal financial management tools, built with AI. This problem has existed for some time. In 2007, I remember walking around telling my colleagues at American Express about Mint.com and how impressive it was. 16 years later, not too much has changed! Beyond the initial value of connecting your accounts and displaying them in one place, online financial management tools still struggle to dispense truly personalized and useful advice. However, the reasons for this - complexity of bank transaction descriptions, diversity of personal financial circumstances, incomplete and fragmented data - might make LLMs just the right technological advancement to break the stagnation. Robin at Ntropy goes through the opportunities and challenges here, discussing not only “modern Mints” such as Monarch Money but also Ntropy’s own open-source financial assistant proof-of-concept, Cookie. It’s still probably a while before people have access to truly widespread, reliable, and cost-effective financial advice, but it’s exciting to think about the potential for AI to make a real difference here.
In this incredibly long and thoroughly-researched article, Victor Swezey reviews the potential for AI approaches to decrease bias and increase access in lending (warning: paywall). Victor interviewed many leaders in AI and fintech for the piece, including me. He included a few quotes from my remarks, such as: “When you can make quick, data-driven decisions that don’t flow through the potentially biased black box of the human mind, then you increase the diversity of people you’re able to lend to.”
The hottest new job is “head of AI” and nobody knows what they do - Vox
Apparently, tons of companies are hiring a “head of AI”, though the role differs massively from place to place. In some firms, such a person has a tech/data background and oversees technical efforts to integrate AI into products, while at others, the individual is more of a cross-functional evangelist for the integration of AI into everything, including branding or recruiting. As with any job that carries a fancy title, the actual influence and impact of someone in the position is likely to be a function of everything but the title itself.
While the blazing-fast progress of LLMs, computer vision, GPUs, and other AI-enabling technologies owes much to PhD researchers in industry and academia, the proliferation of AI within various fields may actually create a whole new class of ‘blue-collar’ AI jobs that don’t require even a bachelor’s degree. This is the thesis put forward by Myra Roldan, who argues for the ability of community colleges to train a diverse population of future AI professionals. The vision is compelling, especially considering that many firms will really be adopters of AI rather than developers of it, more focused on the maintenance and operation of systems than their design. Interestingly, her list of example jobs falling into this category includes roles such as Machine Learning Engineer, which most tech people certainly wouldn’t call blue collar by any stretch. Perhaps this isn’t actually a matter of blue vs. white collar, a distinction that is rapidly losing meaning or relevance. Arguably, plenty of so-called traditionally blue-collar jobs are safer from AI disruption than white collar ones! There is more potential for AI to accelerate the emergence of a collar-blind economy, where the economic rewards and social status of a given job are not as tightly linked.
If AI has the potential to remake companies and industries, there will be massive opportunities for investment, some more obvious than others. In this well-structured essay, Alex Rampell at Andreessen Horowitz discusses the potential for AI to substantially impact productivity in ‘bits’ businesses before those involving ‘atoms’ and how since there are tons of even non-tech ‘bits’ companies, the potential for AI-driven transformation is enormous. Interestingly, he lays out the case for a new wave of private equity investing. As opposed to the earlier wave of leveraged buyouts driven by financial engineering and operational efficiency, this one would be driven by the potential to run companies more profitably with the judicious application of AI. In this vision, a PE group would buy an established company and then use the best AI technologies to radically reshape its product, operations, and workforce, thereby making it far more productive, profitable, and valuable. I fully expect this to happen. The question is which investment groups will be best qualified to execute an “AI takeover”. Will existing PE firms build AI practices, or will there emerge a brand new type of “AI PE” firm with a different combination of talent, technology, and capital?
Promise and pitfall, benefit and backlash, revolution and risk. Indeed, the outlook for AI in financial services is full of great upsides as well as potential downsides, particularly in the areas of fraud and compliance. While new technologies may streamline compliance processes, detect fraudulent behavior, and reduce the role of humans and their potential bias in critical financial decisions, they also create new types of risk. These include nefarious uses of the technology (e.g. deepfakes) or challenges to established practices of data governance or model risk management. This post discusses some of these risks and opportunities, many of which will surely be tackled by a wave of AI-native startups targeting the financial services market.
Checkout.com, a major online payments provider, has rolled out an identity verification solution that uses facial recognition to corroborate user identities over real-time video. In addition to capturing a static photo from an identification document such as a driver’s license, this capability prompts the user over a video connection to match identities and establish the person’s legitimacy. Ecommerce in general is a constant battle between convenience, purchase volume, and fraud prevention. As AI-enabled fraud vectors such as deepfakes proliferate, companies need to use these same AI capabilities to detect and prevent malicious use.
In this article, Axios writers reference a study from Evident, a bank-focused research firm, which studied various banks’ investments in AI. Apparently, “JPMorgan Chase & Co. is leading the field in research, Capital One is leading in patents, and Wells Fargo is leading in investments.” The speed at which the largest, most established industry players are rushing to integrate AI into their businesses is a contrast to previous technology waves (e.g. cloud, mobile) where much of the innovation was pursued by smaller firms, while the larger ones lagged behind. AI is an area where size and budget may confer real advantages, particularly if management can align on concrete goals rather than chasing trends. Right now, every large financial institution is making big promises with AI and putting serious money behind them. It will be interesting to see in a year how these investments turn out and whether anything really transformative will have been achieved.
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