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The Top 7 Use Cases for Generative AI in Fintech

From reshaping tax operations to fraud detection and invoice processing, generative AI is revolutionizing fintech capabilities.

The fintech industry is leveraging GenAI across a number of novel use cases, though some have proven too risky thus far.

The technology is helping companies to streamline operations, automate risk management procedures, and enhance institutional knowledge.

For consumers, GenAI in fintech personalizes customer experiences and democratizes financial literacy.

“Show me the money” is more than an iconic line from Jerry Maguire—it’s now also a prompt for generative AI. The fintech industry has already proven an ideal match for the rapidly expanding technology. In 2023, the generative-AI-in-fintech market was valued at $1.1 billion globally, per a report from DataHorizzon Research. By 2032, this figure is expected to expand to nearly $20 billion. Meanwhile, McKinsey believes GenAI will add between $200 and $340 billion in annual value to the banking sector. Show me the money, indeed.

With that said, the technology faces challenges in the financial services industry given its highly regulated nature. Banks are skeptical of generative AI models sharing advice on investing without human oversight, as chatbots carry the risk of hallucinating—i.e., accidentally relaying false information. “People have this kind of dream of [GenAI models as] a PFM, a personal financial manager,” said Immad Akhund, the co-founder and CEO of fintech leader Mercury, but “it just hasn’t worked out so far.” Akhund believes the risk level is simply too high for banks to take on at the present.

For now, companies are utilizing generative AI to transform the fintech industry through other innovative methods. Here are seven of our favorites:

1. Customer service

Standard LLMs like ChatGPT help make sense of the financial world but have limitations based on their input data and level of nuance. To solve this need, Kasisto built KAI-GPT, the financial industry’s “first LLM purpose-built for banking.” Industry players can utilize the conversational AI platform to streamline customer service interactions, lowering call center traffic. The AI chatbot offers advice on financial decisions, facilitating “human-like” conversations that boost the customer experience.

Kasisto spent a decade researching and constructing KAI-GPT, ensuring unmatched financial literacy and secure handling of sensitive financial information. The platform can also transform business operations through the KAI Banker’s Portal, which generates insights into strategy effectiveness and provides banks with opportunities to expand offerings, enter new markets, and more.  

2. Financial reporting, research, and data analysis

GenAI models make it easier than ever to sort through massive quantities of data to find patterns and distill key learnings—a major piece of what the financial industry does. Kensho provides a suite of AI-powered data and analytics tools used by hedge funds, asset managers, and other types of investors. The Kensho insights platform sorts through gobs of financial news, research, and social media to share insights on company performance, market trends, and risk factors.

An AI model is only as good as its training, so Kensho partners with S&P Global to train its machine learning algorithms on the latest, most comprehensive data. The company also partnered with S&P Global on the release of S&P Marketplace Generative AI Search, an AI-powered search engine that allows users to ask questions about a dataset’s geographic coverage, history, and more.

3. Fraud detection and prevention

Crime doesn’t always pay, but financial crime surely can. According to FIBE, American fintech firms lose an average of $51 million annually due to fraud. Unlike traditional fraud detection systems that rely on static rules and models, GenAI learns and adapts from the data the models process, continuously improving the knowledge base and output. As such, these models can evolve to recognize new types of fraud as they emerge without human intervention, allowing companies to more rapidly flag and block suspicious financial activity.

American fintech firms lose an average of $51 million annually due to fraud.

American Express is one such company leveraging GencAI to enhance its fraud detection services. The company has complemented GenAI modeling techniques to produce synthetic data, such as fake credit card numbers, to monitor similarities and discrepancies against actual fraudulent transactions and make tailored fraud predictions—making it easier to discern patterns and more effectively combat credit card fraud.

4. Predicting customer creditworthiness

When consumers apply for loans, lenders generally must review credit reports, credit scores, and payment histories to deduce whether an applicant is a safe bet or at risk of default. Zest AI is working to automate that process by utilizing AI to assess and predict creditworthiness. Founded in 2009 by former Google and PayPal executives, Zest is growing quickly—investors include Andreessen Horowitz, General Catalyst, and Kleiner Perkins.

Models built using Zest’s AI-powered platform effectively swap out risky borrowers for more creditworthy applicants, helping clients achieve a 20-30% increase in approval rates without added risk. Zest also recently rolled out LuLu, a customized GenAI lending intelligence companion tool that allows lending companies to instantly capture industry insights and portfolio metrics.

 5. Knowledge sorting and synthesis

Large financial firms have hundreds of thousands of pages of information covering market research, investment strategies, analyst insights, and more. Traditionally, advisors must scan through a plethora of internal sites to find what they need, but thanks to GenAI, this process is now a breeze.

Morgan Stanley, for example, utilized OpenAI’s GPT-4 to build an internal-facing chatbot that can comprehensively search the firm’s full wealth management content to “effectively unlock the cumulative knowledge of Morgan Stanley Wealth Management,” says Jeff McMillan, head of Analytics, Data, and Innovation. “Think of it as having our Chief Investment Strategist, Chief Global Economist, Global Equities Strategies, and every other analyst around the globe on call for every advisor, every day.”

Citigroup has implemented the technology in similar ways, such as when U.S. federal regulators released 1,089 pages of new capital rules for the banking sector. The company’s risk and compliance team used GenAI to examine the document word-by-word and compose key takeaways, which they then assessed to determine the impact and adjust strategy.

6. Reshaping tax operations

GenAI allows tax professionals to improve and expedite data management processes, enhancing a range of tasks. KPMG has released Digital Gateway, an integrated platform that provides clients “with access to the full suite of KPMG tax and legal technologies, putting the full power of GenAI in your team’s hands.”

AI-based tax assistant that can more quickly transform tax data into value.

Included in Digital Gateway is an AI-based tax assistant that can more quickly transform tax data into value, streamlining the tax management function. It also supplements its findings with KPMG’s organizational experience and proprietary knowledge, while applying GenAI on top of data to more efficiently review, analyze, and synthesize insights.

 7. Invoice processing

Historically, invoice processing is a tedious yet necessary manual task that businesses carry out to ensure prompt payments. But with—the “accountable AI for invoice processing and bill pay”—companies can automate and optimize their full accounts payable workflow to take care of invoices and maximize team productivity. 

Its generative AI model doesn’t just copy and paste invoice data when ingesting information, but learns how teams code invoices regardless of format to process invoices both faster and more accurately than traditional rules-based accounting templates. After uploading an invoice, extracts key insights, identifies duplicates, and initiates an automated approval workflow, assigning each step of the approval process to the correct team member. This productivity by 355% and speeds up processing by 5x, or so the company claims.

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