How to Leverage Technology for Financial Innovation

AI is transforming finance — and CFOs need to be ready. In a recent interview with Adam Zaki of CFO.com, I shared some key insights from my book "AI Mastery for Finance Professionals," and how finance leaders can navigate the rapidly evolving AI landscape. Here are the highlights: 1️⃣ Data Readiness is Critical Generative AI offers incredible potential, but without mature, clean, and well-governed data, it’s not a technology that can be fully leveraged. CFOs must prioritize their data infrastructure first. 2️⃣ Start Small, Think Big Success with AI isn’t about automating everything overnight. Focus on incremental wins—projects that demonstrate impact, gain buy-in, and build momentum for broader adoption. 3️⃣ Understand the Tool, Not Just the Output AI isn’t a magic box. CFOs don’t need to be developers, but understanding how AI works is crucial to asking the right questions and trusting its insights effectively. 4️⃣ Bias Awareness Matters AI models are only as good as the data they’re trained on. Proactively test for fairness and ensure your datasets are free from bias. 5️⃣ CFOs as Strategic Leaders Today’s CFOs are more than financial stewards—they’re strategists and innovators. AI enhances this role, providing tools to forecast, predict, and guide with creativity and precision. 💡 Final Thought: AI adoption isn’t about replacing people — it’s about empowering teams and creating new efficiencies that drive long-term value. The future is here, and it’s time for finance leaders to embrace it. https://lnkd.in/emBQtfHR

Your AI project will succeed or fail before a single model is deployed. The critical decisions happen during vendor selection — especially in fintech where the consequences of poor implementation extend beyond wasted budgets to regulatory exposure and customer trust. Financial institutions have always excelled at vendor risk management. The difference with AI? The risks are less visible and the consequences more profound. After working on dozens of fintech AI implementations, I've identified four essential filters that determine success when internal AI capabilities are limited: 1️⃣ Integration Readiness For fintech specifically, look beyond the demo. Request documentation on how the vendor handles system integrations. The most advanced AI is worthless if it can't connect to your legacy infrastructure. 2️⃣ Interpretability and Governance Fit In financial services, "black box" AI is potentially non-compliant. Effective vendors should provide tiered explanations for different stakeholders, from technical teams to compliance officers to regulators. Ask for examples of model documentation specifically designed for financial service audits. 3️⃣ Capability Transfer Mechanics With 71% of companies reporting an AI skills gap, knowledge transfer becomes essential. Structure contracts with explicit "shadow-the-vendor" periods where your team works alongside implementation experts. The goal: independence without expertise gaps that create regulatory risks. 4️⃣ Road-Map Transparency and Exit Options Financial services move slower than technology. Ensure your vendor's development roadmap aligns with regulatory timelines and includes established processes for model updates that won't trigger new compliance reviews. Document clear exit rights that include data migration support. In regulated industries like fintech, vendor selection is your primary risk management strategy. The most successful implementations I've witnessed weren't led by AI experts, but by operational leaders who applied these filters systematically, documenting each requirement against specific regulatory and business needs. Successful AI implementation in regulated industries is fundamentally about process rigor before technical rigor. #fintech #ai #governance

You've probably already seen 367 posts about what people saw at Money 20/20. So here's one about what I didn't see - banks taking advantage of a huge lending opportunity. I am talking about banks using their balance sheets to fund non-bank lenders or to develop lending programs that leverage technology-enabled service providers. In other words, banks can offer a viable alternative to private credit, either through providing wholesale lines to fintechs or through direct funding of loans. Lending innovation is being held back by the lack of good funding options. New lenders frequently face interest rates in the mid-teens and an 80-85% advance rate, meaning that the company has to come up with 15-20% of every loan. All while still having to bear 100% of losses up to the point of bankruptcy. No wonder so many fintech lenders want to sell technology, not make loans themselves. Meanwhile, many banks struggle to find good loans to make or to diversify concentrated loan books. [Regulatory nerdery] Many of these banks' capital constraints come from the leverage ratio rather than risk-based capital. In these cases, the bank can start this business with zero initial impact on the capital it needs to hold. [/Regulatory nerdery] There are multiple ways to take advantage of this opportunity. Banks can: ➡️ Provide wholesale lines to non-bank lenders. For partner banks, this can be combined with sponsoring the program to add non-interest income into the mix. ➡️ Take the loans onto their own balance sheet, whether through outright purchase or risk-sharing arrangements. ➡️ Partner with technology providers to build new loan products, whether to embed with distribution partners or to market directly. There are role models that have had success in this area. CCBX a division of Coastal Community Bank, Synchrony, and Customers Bank have all been doing various versions of this for years. More recently, Bankwell looks to be building an interesting business. But the opportunity is huge, especially if you believe in embedded lending. Perhaps I just took the wrong meetings and went to the wrong parties in Vegas. Or perhaps there is a ton of greenfield space just waiting for the right builders to show up.