AI in Fintech

Powering the next generation of financial services

The global AI in Fintech market is projected to experience substantial growth, with estimates suggesting it could reach $53 billion by 2030, expanding at a CAGR of 24% from 2025.

From fraud prevention and document processing to automated underwriting and deal sourcing, AI has seeped into nearly every corner of the fintech stack and beyond. The convergence of AI and financial services, once a niche edge case, is now reshaping the sector from both the inside and out. As this integration deepens, venture investors and established financial institutions alike are asking: How is AI fundamentally reshaping the future of finance?

AI in Fintech 101: Origins, Evolution, and Considerations

What exactly is AI in Fintech? 

AI in fintech is best understood as software that has learned from historical financial signals and now sits directly in the decision loop, detecting patterns, proposing actions, and increasingly executing them in real time.  In practice this spans classic machine-learning scorecards, deep-learning risk models, natural-language engines that read earnings calls, and today’s generative + agentic architectures that can draft portfolio advice, negotiate terms, or wire funds on their own.

What began as a tactical efficiency play (e.g., a faster loan underwriting, automated KYC checks, fraud models that stop card skimming before it starts) is fast maturing into a strategic growth engine. Compressing whole product teams into lines of code, new AI systems parse market chatter, personalize offers at scale, and even originate trades or credit lines autonomously. That leverage comes with a spotlight: draft rules from the EU AI Act to U.S. banking agencies converge on four imperatives—fairness, explainability, security, and iron-clad data governance.

For founders and investors, the real playbook is simple: put generative and agentic AI to work where it can mint net-new revenue or widen margins first, and layer in the guardrails as you scale. Early “pioneer” institutions that follow this offense-first rhythm are already twice as likely to post double-digit ROI from their flagship gen-AI projects than more cautious peers, according to a February 2025 Deloitte survey. Teams that marry breakout growth with credible accountability won’t just outrun incumbents, they’ll help write the rulebook everyone else will have to follow.

Real-World Use Cases: Where AI Is Changing the Game

Hyper-Personalized Customer Experiences: AI is the engine behind platforms offering customized financial guidance, automated support, and tailored product recommendations based on individual user data.

• Spinwheel embeds an AI-driven “1-Click Debt Management” widget that pulls a user’s entire liability profile with just a phone number and DOB, then serves up hyper-personalized next-best actions (e.g., which loan to pay off first, how to cut payments, or when to refinance). Its January 2025 partnership with Oscilar adds real-time risk-decisioning to automate those offers, turning debt reduction into a tailored, in-app journey for each customer while giving lenders richer risk signals.

• Sheerhealth is building an AI-powered insurance concierge: its app ingests a member’s claims and EOB data, flags coding errors before you pay, and fires off appeals or resubmits paperwork automatically. Members can simply snap a bill for an instant benefits check, receive plan-optimization reports that surface unused coverage, and get prescription-saving recommendations, all personalized by the platform’s analytics engine. The service says it has already clawed back $8M+ for users, turning the headache of medical bills into a tailored, money-saving experience.

Back-Office Automation and Financial Infrastructure: AI, often combined with techniques like NLP and computer vision, automates numerous manual and data-intensive back-office tasks, reducing costs and errors.

• Brellium, our portfolio company who recently raised $16.7M Series A, plugs into a clinic’s electronic records to proof-read every medical chart, fix missing or wrong billing codes, and even guarantee against insurer clawbacks. Customers already see the payoff: Lightfully Behavioral Health cut chart-audit time by 87%, while Double Care ABA slashed QA costs by 80%.

• Sunlight is building the missing infrastructure layer between AP and AR systems, powered by Agentic AI. For Accounts Payable and Expanse Management platforms the value prop is simple: convert low-margin ACH or check transactions into high-margin card spend, without changing a line of their own code.

AI-Powered Tools for Venture Capital: AI is no longer just powering the companies venture capitalists fund — it’s reshaping how they operate.

• No Cap is called the world's first AI angel investor. This platform can autonomously evaluate startups, execute SAFE agreements, and wire funds, often within minutes. This shows AI's potential to transform the funding cycle from a multi-week process into a frictionless, digital flow.

• Harmonic is an AI-powered platform that provides a comprehensive startup database, tracking over 20 million companies and 150 million professionals. It enables venture capitalists and go-to-market teams to discover emerging startups, monitor talent movements, and receive real-time alerts on funding and hiring activities. The platform integrates with tools like CRMs and offers APIs for seamless data access. 

Enhanced Risk Management & Fraud Detection: On the traditional end, AI algorithms excel at analyzing transactional data, user behavior patterns, and diverse external data points in real-time. This allows for the detection of subtle anomalies and the prediction of fraudulent activities with significantly higher accuracy and speed than traditional systems.

• Sardine is an AI-powered risk platform designed to help financial institutions, fintechs, and online businesses detect and prevent fraud, ensure compliance, and assess credit risk in real time. It combines device intelligence, behavioral biometrics, and machine learning to analyze user actions like logins, payments, and account activity delivering instant fraud scores and automating decisions. Sardine also offers AI agents that streamline manual tasks such as sanctions screening and case management, helping risk teams operate more efficiently.

• ​Zest AI is a financial technology company that leverages artificial intelligence to enhance credit underwriting processes for lenders. By analyzing thousands of data variables, it enables more accurate, inclusive, and automated lending decisions. Zest AI's solutions help financial institutions increase loan approvals, reduce risk, and expand access to credit for underserved populations, all while maintaining compliance with regulatory standards. Their technology includes tools for fraud detection, lending intelligence, and a generative AI platform called LuLu, designed to provide actionable insights through natural language queries.

What’s Next: The Trajectory of AI in Fintech

As AI becomes embedded into the fabric of fintech, we’re seeing not just incremental improvements but structural shifts in how financial services are designed, delivered, and experienced. Four major themes are emerging:

• Explainability Becomes Table Stakes

  As models begin making regulated decisions such as lending, underwriting, and trading transparency isn’t optional. Fintechs will need explainable AI systems that regulators can audit and customers can understand. Compliance will reward clarity, not just accuracy.

• From Apps to Agents

  The fintech UX is evolving from dashboards and drop-downs to intelligent agents that users can chat with, learn from, and delegate to. Tools like Zest AI’s LuLu or No Cap’s autonomous investor are early signs of this shift. Instead of reading reports, users will ask questions. Instead of clicking buttons, they’ll issue instructions.

• AI-Native Product Design

  Fintech startups are beginning with the model, not the wireframe. Instead of retrofitting AI into traditional apps, founders are asking: What can the model do? This “AI-native” mindset results in radically different product architecture, UX, and monetization strategies. As Andrej Karpathy put it: “The hottest new programming language is English.” Generative UX, data-first infrastructure, and self-improving workflows are the hallmarks of this new wave.

• The Rise of FinAI Infrastructure

  Just as Stripe enabled payments and Plaid unlocked data, a new generation of infra startups is emerging to power AI-native fintech. These include tools for model compliance (e.g. Arthur.ai), deployment governance (e.g. TruEra), and continuous monitoring of bias, drift, and data leakage.

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Together, these themes point to a broader truth: AI isn’t a tool inside fintech, it’s becoming fintech’s operating system. And that leads us to the real takeaway. 

AI isn’t replacing investors, analysts, or underwriters. It’s reshaping what those roles look like. 

The standout firms of the next decade will be those that let AI handle the pattern-matching and paperwork while humans double-down on the one asset machines can’t replicate: relationships.

At Fiat, we’re putting that theory into practice, hosting more in-person gatherings, expanding our Fintech Summit, and meeting founders wherever they are. Underwriting can be automated; trust cannot.

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