Investing in the Future of Finance: Opportunities in Banking Software and AI-Driven FinTech
AI is fundamentally transforming banking, and it will eventually completely change how we engage with financial services. A wide range of banking services such as getting a loan, transferring money, paying with a credit or debit card, or purchasing insurance will be made faster, cheaper, better and more convenient with agentic AI. AI also has the potential to provide financial services to large swaths of the global population who are underbanked. The growth of AI presents tremendous opportunities for new startups, who can capitalize on this massive technology wave to disorient and eventually replace incumbents. But it will not be easy. Both incumbent banks and banking software companies have the benefit of significant resources, including large balance sheets, huge customer bases, and broad product portfolios. New startups have to be highly strategic in terms of their product and customer focus, learning from success stories in previous technology waves.
In the following research report, Nnamdi Okike, Co-Founder and Managing Partner of 645 Ventures, does a deep dive on the banking software market, describing its history, key learnings from previous tech waves, and implications for the AI wave. Please find key takeaways from the report below, as well as a link to the full report.
Key Takeaways for AI Banking Startup Founders:
- To Succeed, AI Banking Software Startups Must Solve Specific Pain Points in the Existing Banking Stack: Don't try to replace entire core banking systems overnight. Instead, identify a critical, narrow problem within the existing software stack (e.g., account opening, loan origination for a specific niche) and build a significantly superior AI-powered solution. This "wedge product" approach allows for easier market entry and demonstrated ROI, increasing your chances of adoption by incumbent banks.
- Leverage Deep Banking/Financial Services Experience: If you have an "earned secret" from working in banking and understanding its inefficiencies, this is a major advantage. Your insights into real-world challenges can lead to highly relevant and effective software solutions. Consider partnering with individuals who have this deep domain expertise if it's not your own.
- Consider Building a Full AI-Native Banking Application (Neobank 2.0): The largest exits in the previous wave were neobanks that built their own tech. If you have a bold vision and can raise significant capital, building an AI-first bank or banking application could offer a radically better cost structure and directly serve underserved segments, leading to massive market opportunities.
- Focus on AI's Transformative Power:
- Hyper-Personalization: Develop AI that can proactively manage a customer's finances, making recommendations and eventually acting on their behalf (e.g., canceling subscriptions, switching providers).
- Automate Manual Processes: Target areas like loan origination and underwriting, which are currently slow and human-intensive. AI can reduce these processes from days to minutes, offering immense value to both banks and end-users.
- Improve Customer Service: Leverage AI to provide instant, effective customer support that actually resolves issues, moving beyond traditional chatbots.
- Address Underserved Markets: Identify customer segments with unmet financial needs (e.g., gig workers, immigrants, SMBs without CFOs) and build AI solutions to provide them access to credit, wealth management, or financial planning.
- Be Prepared for Long Product-Market Fit Journeys: Some successful banking tech companies took several years to find their stride. Don't be deterred if immediate widespread adoption isn't achieved, especially if you're tackling a complex, entrenched problem. Time can be your friend as AI crosses the chasm and reaches mass adoption.
- Explore AI Middleware/API Layers: Consider building infrastructure that enables agentic commerce or bridges the gap between financial institutions and applications, similar to how Plaid filled a market void for APIs.
Key Takeaways for Investors in AI Banking Startups:
- Prioritize Founders with "Earned Secrets": Look for founders who have first-hand experience with the problems they are solving within the banking sector, developed either through experiences as a customer or as a provider of banking services or technology. Their deep understanding can lead to more robust and adoptable solutions.
- Assess "Wedge Product" Potential for Banking Software Startups: For investments in banking software companies selling to incumbents, evaluate if the startup has a clear, highly differentiated "wedge product" that can gain initial traction without requiring a bank to rip out its entire core system. This de-risks early adoption.
- Be Realistic about Disruption of Incumbents: Understand that dislodging major incumbent banking software providers is extremely difficult and time-consuming. While multi-billion dollar exits are possible for software companies (e.g., nCino, Q2), they often take a long time to achieve significant market share relative to the giants. Adjust your return expectations and entry prices accordingly.
- Evaluate Neobanks/AI-Native Banking Applications for Outsized Returns: The largest banking tech exits historically have been direct-to-consumer neobanks. AI has the potential to create a new wave of even more cost-efficient and personalized "AI banks." These opportunities carry higher risk and require more capital but offer significantly larger TAMs and potential for multi-billion dollar exits.
- Look for AI Solutions that Drastically Improve Cost Structures or Reach New Customers: Focus on AI applications that can dramatically reduce operational costs for financial institutions or enable them to serve previously inaccessible or underserved customer segments. These are the areas where AI can drive the most significant value creation.
- Consider International Opportunities: Companies like Nubank, founded in Brazil and now one of the largest fintechs in the world, show that highly scalable banking companies are being built outside the U.S., especially in emerging markets. Be open to non-U.S. startups targeting large markets, focusing on developing a deep understanding of market and product nuances.
You can read the full report here: