Artificial intelligence and machine learning have been used in the financial services industry for more than a decade, enabling enhancements that range from better underwriting to improved foundational fraud scores. Generative AI via large language models (LLMs) represents a monumental leap and is transforming education, games, commerce, and more. While traditional AI/ML is focused on making predictions or classifications based on existing data, generative AI creates net-new content.

This ability to train LLMs on vast amounts of unstructured data, combined with essentially unlimited computational power, could yield the largest transformation the financial services market has seen in decades. Unlike other platform shiftsinternet, mobile, cloudwhere the financial services industry lagged in adoption, here we expect to see the best new companies and incumbents embrace generative AI, now.

Financial services companies have vast troves of historical financial data; if they use this data to fine-tune LLMs (or train them from scratch, like BloombergGPT), they will be able to quickly produce answers to almost any financial question. For example, an LLM trained on a companys customer chats and some additional product specification data, should be able to instantly answer all questions about the companys products, while an LLM trained on 10 years of a companys Suspicious Activity Reports (SARs) should be able to identify a set of transactions that indicate a money-laundering scheme. We believe that the financial services sector is poised to use generative AI for five goals: personalized consumer experiences, cost-efficient operations, better compliance, improved risk management, and dynamic forecasting and reporting.

In the battle between incumbents and startups, the incumbents will have an initial advantage when using AI to launch new products and improve operations, given their access to proprietary financial data, but they will ultimately be hampered by their high thresholds for accuracy and privacy. New entrants, on the other hand, may initially have to use public financial data to train their models, but they will quickly start generating their own data and grow into using AI as a wedge for new product distribution.

Lets dive into the five goals to see how incumbents and startups could leverage generative AI.

While consumer fintech companies have achieved an enormous amount of success over the past 10 years, they havent yet fulfilled their most ambitious promise: to optimize a consumers balance sheet and income statement, without a human in the loop. This promise remains unfulfilled because user interfaces are unable to fully capture the human context that influences financial decisions or provide advice and cross-selling in a way that helps humans make appropriate tradeoffs.

A great example of where non-obvious human context matters is how consumers prioritize paying bills during hardship. Consumers tend to consider both utility and brand when making such decisions, and the interplay of these two factors makes it complicated to create an experience that can fully capture how to optimize this decision. This makes it difficult to provide best-in-class credit coaching, for example, without the involvement of a human employee. While experiences like Credit Karmas can bring customers along for 80% of the journey, the remaining 20% becomes an uncanny valley where further attempts to capture the context tend to be overly narrow or use false precision, breaking consumer trust.

Similar shortcomings exist in modern wealth management and tax preparation. In wealth management, human advisors beat fintech solutions, even those narrowly focused on specific asset classes and strategies, because humans are heavily influenced by idiosyncratic hopes, dreams, and fears. This is why human advisors have historically been able to tailor their advice for their clients better than most fintech systems. In the case of taxes, even with the help of modern software, Americans spend over 6 billion hours on their taxes, make 12 million mistakes, and often omit income or forgo a benefit they were not aware of, such as potentially deducting work-travel expenses.

LLMs provide a tidy solution to these problems with a better understanding and thus a better navigation of consumers financial decisions. These systems can answer questions (Why is part of my portfolio in muni bonds?), evaluate tradeoffs (How should I think about duration risk versus yield?), and ultimately factor human context into decision making (Can you build a plan thats flexible enough to help financially support my aging parents at some point in the future?). These capabilities should transform consumer fintech from a high-value, but narrowly focused set of use cases to another where apps can help consumers optimize their entire financial lives.

Anish Acharya and Sumeet Singh

In a world where generative AI tools can permeate a bank, Sally should be continuously underwritten so that the moment she decides to buy a home, she has a pre-approved mortgage.

Unfortunately, this world doesnt yet exist for three main reasons:

Generative AI will make the labor-intensive functions of pulling data from multiple locations, and understanding unstructured personalized situations and unstructured compliance laws, 1000x more efficient. For example:

These are all steps that will lead to a world where Sally can have instant access to a potential mortgage.

Angela Strange, Alex Rampell, and Marc Andrusko

Future compliance departments that embrace generative AI could potentially stop the $800 billion to $2 trillion that is illegally laundered worldwide every year. Drug trafficking, organized crime, and other illicit activities would all see their most dramatic reduction in decades.

Today, the billions of dollars currently spent on compliance is only 3% effective in stopping criminal money laundering. Compliance software is built on mostly hard-coded rules. For instance, anti-money laundering systems enable compliance officers to run rules like flag any transactions over $10K or scan for other predefined suspicious activity. Applying such rules can be an imperfect science, leading to most financial institutions being flooded with false positives that they are legally required to investigate. Compliance employees spend much of their time gathering customer information from different systems and departments to investigate each flagged transaction. To avoid hefty fines, they employ thousands, often comprising more than 10% of a banks workforce.

A future with generative AI could enable:

New entrants can bootstrap with publicly available compliance data from dozens of agencies, and make search and synthesis faster and more accessible. Larger companies benefit from years of collected data, but they will need to design the appropriate privacy features. Compliance has long been considered a growing cost center supported by antiquated technology. Generative AI will change this.

Angela Strange and Joe Schmidt

Archegos and the London Whale may sound like creatures from Greek mythology, but both represent very real failures of risk management that cost several of the worlds largest banks billions in losses. Toss in the much more recent example of Silicon Valley Bank, and it becomes clear that risk management continues to be a challenge for many of our leading financial institutions.

While advances in AI are incapable of eliminating credit, markets, liquidity, and operational risks entirely, we believe that this technology can play a significant role in helping financial institutions more quickly identify, plan for, and respond when these risks inevitably arise. Tactically, here are a few areas where we believe AI can help drive more efficient risk management:

David Haber and Marc Andrusko

In addition to being able to help with answering financial questions, LLMs can also help financial services teams improve their own internal processes, simplifying the everyday work flow of their finance teams. Despite advancements in practically every other aspect of finance, the everyday work flow of modern finance teams continues to be driven by manual processes like Excel, email, and business intelligence tools that require human inputs. Basic tasks have yet to be automated due to a lack of data science resources, and CFOs and their direct reports consequently spend too much time on time-consuming record-keeping and reporting tasks, when they should be focused on top-of-pyramid strategic decisions.

Broadly, generative AI can help these teams pull in data across more sources and automate the process of highlighting trends and generating forecasts and reporting. A few examples include:

That said, its important to be mindful of the current limitations of generative AIs output herespecifically around areas that require judgment or a precise answer, as is often needed for a finance team. Generative AI models continue to improve at computation, but they cannot yet be relied on for complete accuracy, or at least need human review. As the models improve quickly, with additional training data and with the ability to augment with math modules, new possibilities are opened up for its use.

Seema Amble

Across these five trends, new entrants and incumbents face two primary challenges in making this generative AI future a reality.

The advent of generative AI is a dramatic platform change for financial services companies with the potential to give rise to personalized customer solutions, more cost-efficient operations, better compliance, and improved risk management, as well as more dynamic forecasting and reporting. Incumbents and startups will battle for mastery of the two critical challenges we have outlined above. While we dont yet know who will emerge victorious, we do know there is already one clear winner: the consumers of future financial services.

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