Where the Real AI-Fintech Advantage Will Be Won

Artificial intelligence is moving from experiment to infrastructure in financial services, but the gap between promise and execution is still wide. McKinsey estimated in 2025 that generative AI alone could add as much as $340 billion a year in value to banking, even as most banks remain stuck in pilot mode rather than scaling real commercial impact.

That matters because the sector is not only chasing upside. It is also managing new forms of risk. The US Treasury said in late 2024 that AI is being used increasingly across financial services, while respondents also warned that newer tools are expanding use cases and introducing fresh risks, especially in customer-facing applications. The Financial Stability Board has likewise said AI use in finance is becoming more widespread and more diverse, with the potential to amplify vulnerabilities if firms get governance wrong.

That is exactly why Tomi Popoola is such a credible voice on this space. A respected fintech speaker, she combines hands-on technical depth with commercial understanding, having started her career at JP Morgan, later worked at AWS as a Solutions Architect supporting Fortune 500 cloud programs, and gone on to found Slash Finances, an AI-led company focused on financial inclusion. Her background in Computer Science and Finance gives her a rare ability to connect infrastructure, product thinking and business value.

In this exclusive interview with the AI Speakers Agency, she breaks down where the real leverage in AI-fintech will sit, which metrics actually show value, and why the winners will be the firms that pair technical capability with structural advantage.

1. Which layers of the AI-fintech stack will capture the most strategic leverage over the next 12 to 18 months?

Tomi Popoola: “Strategic leverage will concentrate where AI meets irreplaceable structural advantages, and three layers stand out.

The first is proprietary data. The durable moat in financial AI isn't the model. It's the underlying transaction and behavioral data that trains and continuously refines it. Banks, payment networks, and large fintech platforms already hold datasets that compound in value as AI becomes more deeply embedded in operations. AI can commoditise models, but it cannot commoditise the data feeding them.

The second is fraud and risk infrastructure. This layer will monetize fastest because the ROI is immediate and measurable. Even marginal improvements in fraud detection translate directly into loss reduction, while better risk scoring expands approvals without inflating defaults. These tools also slot into existing workflows with relatively low friction, which accelerates adoption.

The third, and most durable, is distribution. The greatest value in financial services has always accrued to whoever owns the customer relationship. Payments apps, neobanks, and embedded finance platforms can deploy AI across lending, insurance, payments, and financial advice, compounding its impact across the entire customer lifecycle.”

2. What metrics should investors track to assess whether AI is genuinely moving the needle?

Tomi Popoola: “The most revealing metrics are those where machine intelligence should produce outcomes that humans or rules-based systems simply cannot match at scale.

Fraud loss rate is often the cleanest signal of model effectiveness. Look for sustained reductions without a corresponding spike in false positives that reject legitimate transactions.

Cost-to-serve per account captures the operational impact of automation across support, compliance, and underwriting. If AI is working, this number should fall without a degradation in service quality.

Credit approval rates alongside default metrics are particularly instructive together. Rising approvals paired with stable or declining charge-offs suggest better risk discrimination, not just looser standards.

CAC to LTV ratios can improve meaningfully if AI enables more precise targeting, better product-market fit at the customer level, and higher cross-sell conversion.

Manual review rates are perhaps the most underused metric. The proportion of transactions still requiring human intervention in fraud, compliance, or underwriting is a direct measure of automation effectiveness. Consistent declines here translate almost mechanically into margin expansion.”

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This exclusive interview with Tomi Popoola was conducted by Tabish Ali of the Motivational Speakers Agency.