Earning Interest: AI Fintech Tools

Generative AI has started unlocking real value in Fintech. Leading firms are already deploying the technology to drive efficiencies - Brex's AI assistant for expense management saves approximately 4,000 hours of manual effort annually. Financial giants like Bloomberg and JPMorgan are developing proprietary generative AI tools – BloombergGPT currently focuses on improving financial NLP tasks like sentiment analysis, named entity recognition, news classification, and question answering, while JP Morgan’s IndexGPT aids investors in analyzing securities.

AI is not new in Fintech, nearly 90% of fintech firms were leveraging AI/ML to some degree in 2020. Generative AI however, provides a new path to enhance productivity on the still tedious, documentation-heavy financial workflows like billing & invoicing, or compliance reviews. For instance, a large US distribution company is processing 76% of invoices without human intervention using Vic.ai, averaging 36 seconds per invoice instead of 2.3 minutes.

Despite a general downturn in global fintech investment, AI-focused fintech managed to attract over $12.1 billion in funding in 2023. Read on for an overview of the space.

Use Cases: Back-office automation and chat-based financial advice are prominent LLM use-cases

The fintech market serves a wide range of customers – from individuals to companies to governments – across various use cases. Fintech has long leveraged AI/ML to improve areas like algorithmic trading and risk assessment. Large language models (LLMs) introduce a fresh wave of innovation, especially in automating repetitive, language-focused tasks.

Finance back-office tasks involve significant manual handling of documents, bills, invoices - well suited for LLM-driven automation

The workload of a finance team involves handling a large volume of documents, including financial data, transactions, compliance documents, bills, and invoices. LLMs excel at parsing, understanding, and managing such vast amounts of textual data. They can automate the extraction of important details from financial documents, categorize and summarize transaction information, and compile detailed reports from various data points. LLMs also enhance customer interaction and query resolution, with AI chatbots and knowledge assistants interpreting and responding to customer queries – freeing up time for fintech professionals to focus on more critical tasks.

LLM adoption is easier in applications not dealing with sensitive financial data directly

Like the healthcare sector, the fintech industry handles private and sensitive financial details, necessitating regulatory oversight and compliance. Use cases that don't directly involve such sensitive information – such as personal finance management, investment research, back-office workflows like expense tracking, document processing – are more straightforward for LLM adoption, as they may require fewer regulations.

The first wave of LLM adoption is being seen in the following use cases:

CFO Tooling (accounting, bookkeeping, expense management ): Generative AI can enhance financial operations by automating tasks such as accounting, transaction coding, expense tracking, and the creation of receipts and memos. It can also understand patterns and identify exceptions, provide accurate forecasts, and offer personalized insights. This reduces the manual workload for finance professionals. A number of players such as Basis, Arkifi, Vic.ai are offering a “copilot model” for finance teams.

Gen AI can process, summarize and extract relevant data from a bulk of financial documents (e.g. annual reports, financial statements, and earnings calls), which can thereafter be ingested into databases – significantly reducing human workload in doing so. Tools like Sensible.so, Rossum.ai, Attri.ai are using Gen AI to extract data from financial documents and expedite document processing.

Onboarding, Risk & Compliance (fraud, & underwriting, KYC/KYB): Onboarding and AML processes involve a hefty amount of paperwork. Gen AI can automate compliance reviews, requests for missing docs, and sanction alerts. For instance, Greenlite utilizes generative AI to automate compliance reviews for customer onboarding. Coris AI, a Risk API for fintech companies, uses GPT-4 to automatically determine a merchant’s classification codes, which are essential for monitoring risk and compliance.

LLMs can add an additional layer of checks by leveraging language-based sources that were previously untapped or manually analyzed. For instance, Flagright employs GPT-4 to monitor merchant profiles using public platforms like Crunchbase and LinkedIn – assessing a merchant's reputation, clientele, and financial health. Additionally, Flagright uses GPT to create Suspicious Activity Reports (SARs) based on flagged activity and supports risk analysts with an AI Narrative Writer, streamlining routine documentation and communication tasks.

Investment Research: Financial research is being enhanced with Gen AI, allowing investors to carry out research on stocks, public and private markets on a chat interface. StockGPT, an AI-powered financial research tool, lets investors search through earnings releases, financial reports, and other fundamental information for all S&P 500 and Nasdaq companies. FinChat offers financial data on 100,000+ global public companies that users can search from, along with a stock screener, data visualizations, competitor comparisons, and portfolio dashboarding.

Personal Finance: Tools for personal wealth management and financial advice are using Gen AI to give more personalised communication and advice. For example, Wally uses GPT to summarize expenses, provide financial advice based on personal goals, and provides insights into financial habits. UStreet is an AI wealth advisor that creates personalised retirement plans, offers portfolio reviews and tax saving advice.

Market Map: Fintech players – from incumbents to nascent startups – are innovating with Gen AI

Incumbent firms are building proprietary AI models for their teams

Fine tuning models on relevant financial data is critical to creating relevant workflows with LLMs. This puts established firms at an advantage – BloombergGPT, JPMorgan’s IndexGPT, Intuit’s GenOS, Morgan Stanley’s AI Assistant have all been built by incumbent firms with the data and infrastructure needed for training proprietary models.

Established startups are actively enhancing their offerings using Gen AI

Established firms that have already built a customer base are improving their products with Gen AI. Companies offering CFO tooling are integrating AI Assistants into their products, enabling users to interact using natural language for financial data insights – prime examples are Vise Intelligence and PilotGPT. Investment research is being boosted with Gen AI – Alpha by Public.com provides real-time investing context and proactive alerts about market movement to investors.
 

New entrants are innovating with CFO tooling, compliance and research tools 

New entrants in fintech are primarily developing tools that streamline tasks and enhance efficiency for finance professionals, freeing up their time for higher-value work. These tools automate routine tasks like accounting, bookkeeping, and managing expenses, as well as compliance-related activities such as detecting fraud, underwriting, and customer onboarding.

AI innovation will have to navigate fintech’s high bar for data privacy and accuracy

The bar for data quality, privacy and accuracy in financial services is among the highest in the business world. Gen AI adoption, therefore, is currently taking place in selective areas which do not directly impact major financial decisions and therefore need not navigate strict regulatory processes.

Funding: Bulk of funding going to established players, speculative bets on new age tools

Despite the funding for the global fintech market hitting a five-year low in 2023, investor interest in AI within fintech has remained strong.

More than 75% of emerging startups in the space have raised money in the last 2 years

Funding for emerging players has been robust in 2022-24. There have been 30+ deals, albeit small ones, across the sector for AI-native players.

Some notable deals:

• Slope raised a $30M round in 2023 following their recent product launch of SlopeGPT, the first payments risk model powered by GPT. Sam Altman is an angel investor in Slope. 
• Federato, an AI-powered underwriting platform, raised $25M in Series B funding in 2023.

Several speculative rounds, averaging around ~5M, on emerging players in the space

Investors are being cautiously optimistic about AI in fintech, making speculative bets on emerging players. Although fintech is a high-growth area, its regulatory landscape and the nascent state of AI integration create a risk profile that favors smaller initial investments. This allows startups to prove viability and secure regulatory approval before attracting larger funding rounds.

Established startups that embedded Gen AI as a fundamental aspect of their core products secured the largest funding deals in 2022-24

• Pigment is a B2B financial planning platform that raised $88M Series C round in 2023. This funding comes a month after they launched PigmentAI, a Gen AI assistant that answers questions about the financial data, automates certain workflows, generates summaries and insights and allows scenario planning.
• Vic.ai - AI platform for autonomous accounting received $52M Series C funding in Dec’22.
• Puzzle.io, a Gen AI accounting platform, raised $30M in 2023. Puzzle transforms static accounting data into real-time financial insights.

Website Traction: Steady growth in value-driven engagement after AI hype tempered in Q3’23

Interest in AI Fintech has been commensurate with the broader AI trend of initial hype transitioning to more value-driven engagement – similar to the AI customer support tools sector or the AI healthcare tools sector. After a peak in Q1’23, website traffic across the sector has stabilized.

B2B tools have seen the most QoQ growth in website traction; B2C tools have seen great volume but deceleration

Consumer finance tools like Magnifi, FinChat saw significant but declining web traffic in Q4’23, indicating a transition from curiosity-driven to value-seeking user engagement.

Magnifi is an AI-copilot for individual investors. Magnifi analyzes connected accounts to identify excessive fees, risk exposure, and improvement opportunities. Magnifi’s conversational AI is the only SEC-regulated AI that provides assistance, education, and the ability to buy and sell securities.

FinChat is a public equity research platform for investors, similar to ChatGPT for finance. Finchat provides comprehensive insights from its database of financial information with proper citations through an interactive chat interface. It amassed 100K users within a month of its launch in April 2023.

Some startups with extraordinary QoQ growth in website traffic:

• Vise saw a QoQ web traffic growth of >500% in Q4’23. Vise employs AI to optimize investment management for financial advisors, handling all facets of advisor-client interactions, from portfolio personalization to ongoing insights, allowing advisors to focus more on client relations. The company is set to launch “Vise Intelligence”, an LLM-powered interface that generates insights based on portfolio data, trade details, client preferences, and investment strategies.
   
• Kasisto, a generative and conversational AI platform for banking, saw a QoQ growth of 101% in Q4’23. Kasisto is the creator of KAI-GPT, a foundational fintech LLM that is used to build conversational AI for knowledge management and customer service. KAI has led to 27% growth in CD accounts for First Financial Bank, and 30% growth in profitability for Meriwest Credit Union members.
   
• Unit21 is a risk & compliance infrastructure for fintech that saw 77% QoQ growth in web traffic in Q4’23. Unit21 offers an AI Copilot and conversational AI called “Ask your Data” that compliance analysts use to get direction on the most pertinent information from alerts, and get actionable insights by querying the data in simple English language. Unit21 helped LINE’s compliance team automate its false positive resolution by 60%.

Looking Forward

1. Generative AI is being steadily adopted to automate back-office workflows in finance
   
   Gen AI, especially LLMs, are significantly impacting fintech by automating repetitive, unstructured tasks such as invoice processing (Vic.ai), data extraction and document parsing (Sensible), underwriting (Sixfold.ai) and compliance reviews at customer onboarding (Greenlite). Gen AI is also being used to enhance investment research (FinChat) and personal finance advice (Magnifi) with chat-based AI assistants.
    
2. Incumbents, fintech unicorns, as well as AI-native startups are innovating with Gen AI
   
   Incumbent firms are building proprietary LLMs for their finance teams, such as BloombergGPT, JP Morgan’s IndexGPT, and Morgan Stanley’s ‘AI @ Morgan Stanley Assistant' – leveraging their vast data resources and regulatory expertise. Many established startups are adding Gen AI to their current offerings – Vise and Unit21 are well-capitalised and seeing rapid growth in market traction. New fintech startups are devising tools that automate routine finance functions such as accounting (Basis), document processing (Noetica), and compliance tasks (SixfoldAI, Greenlite), alongside enhancing investment research (Finchat, Portrait Analytics).
    
3. Expect larger share of funding to continue going towards established startups seeing strong market traction
   
   Investment in AI fintech has been substantial in 2022-24, with 50+ deals across the sector. Some of the largest rounds have gone to startups that are making AI fundamental to their offering, such as Puzzle and Pigment. Vise, Kasisto, and Unit21 are seeing strong market traction, and are likely candidates for upcoming funding rounds. Pigment is a strong contender for unicorn status in the near future.
    
4. The battle of open vs proprietary AI models will be worth watching out for
   
   Established players like Bloomberg have poured significant resources into building proprietary models like BloombergGPT, leveraging private financial data and custom training. However, the success of FinGPT, an open-source fintech AI model, throws a wrench into the equation. Despite a training cost reportedly under $100, FinGPT outperforms the 50B model BloombergGPT in certain areas. It will be interesting to see if FinGPT's cost-effectiveness can tip the scales against closed-source models like BloombergGPT that have access to high quality proprietary data.