FinTechs are pulling significantly ahead of traditional financial institutions when it comes to turning AI investments into measurable productivity gains, but a new global study reveals a troubling blind spot: neither group has figured out how to make AI work at the leadership and corporate strategy level. A survey of 203 FinTechs and 149 traditional financial institutions across 151 countries found that while AI is delivering real benefits across most business functions, the gains are far from evenly distributed, and the weakest results appear precisely where strategic decisions get made.

Where Is AI Actually Delivering Results in Financial Services?

The Cambridge Centre for Alternative Finance at the University of Cambridge conducted the 2026 Global AI in Financial Services Report, drawing responses from 628 participants across 151 countries, including FinTechs, traditional financial institutions, AI vendors, and regulators. The findings paint a picture of genuine progress, but with striking differences between firm types and business functions.

FinTechs report the strongest AI productivity gains in technology, data, and product roles, with 86% of respondents saying AI has delivered positive impact in these areas. Traditional banks lag considerably at 68%, an 18-point gap that represents the widest divergence in the entire study. This advantage makes intuitive sense: FinTechs tend to have more agile operating models and greater appetite for experimentation, allowing them to deploy AI faster in customer-facing and revenue-generating functions.

Back office and operations work shows near-parity between the two groups, with 76% of FinTechs and 72% of traditional banks reporting productivity gains. This consistency suggests that operational automation benefits are accessible regardless of firm size or legacy infrastructure, making back office automation one of the most reliable AI use cases across the financial services industry.

Why Are FinTechs Winning in Customer-Facing AI?

The divergence widens again when looking at front office and client-facing roles. FinTechs report 76% positive impact, while traditional banks report only 59%, a 17-point gap that mirrors the technology function in scale. This suggests that newer, digitally native companies are translating their technological advantages into better customer experiences and more effective client interactions powered by AI.

Risk management and compliance is the one area where traditional banks edge ahead, with 63% reporting positive AI impact compared to 62% for FinTechs, effectively level across both groups. This near-parity is notable and perhaps unsurprising: regulatory pressure applies equally to both firm types, creating a shared incentive to deploy AI in compliance and risk functions.

How to Assess AI Productivity Across Your Organization

• Technology and Product Functions: Measure AI's impact on development speed, feature deployment, and product innovation cycles. FinTechs are seeing the strongest gains here, suggesting that organizations should prioritize AI tools that accelerate product delivery and data analysis capabilities.
• Back Office and Operations: Evaluate automation benefits in finance, HR, and administrative processes. The consistency of gains across firm types indicates that operational AI is a reliable investment regardless of your organization's size or structure.
• Front Office and Client Engagement: Track AI's effect on customer service quality, sales effectiveness, and client retention. FinTechs' advantage here suggests that agile deployment models and experimentation culture drive better customer-facing AI outcomes.
• Risk and Compliance: Assess AI's role in regulatory adherence, fraud detection, and risk monitoring. The level playing field between FinTechs and traditional banks indicates that regulatory incentives are driving comparable AI adoption in this critical function.
• Corporate and Leadership Functions: Honestly evaluate whether AI is informing strategic decisions, board-level planning, and organizational transformation. This is where both groups are weakest, suggesting it deserves immediate attention and investment.

The most concerning finding emerges at the strategic level. Corporate functions and leadership recorded the lowest positive impact across the entire study, with only 61% of FinTechs and 48% of traditional banks reporting AI productivity gains. This gap suggests that while both types of organizations have successfully deployed AI in operational and revenue-facing roles, they have not yet figured out how to leverage AI for strategic decision-making, organizational planning, or leadership functions.

The overall picture is one of genuine but uneven progress. AI is delivering productivity gains across the board, yet the scale of those gains varies considerably depending on firm type and function. FinTechs are pulling ahead most sharply in the areas closest to revenue generation and product delivery, where their more agile operating models and appetite for experimentation appear to be translating into measurable advantage.

The weakness in corporate and leadership functions represents the next frontier for AI adoption in financial services. Organizations that can successfully deploy AI to inform strategy, optimize organizational structure, and support executive decision-making will likely gain a significant competitive advantage over peers who continue to treat AI as primarily an operational or customer-service tool. The fact that traditional banks lag even more sharply than FinTechs in this area suggests that legacy organizational structures and decision-making processes may be creating barriers to strategic AI adoption.

For enterprises planning their AI strategy, the message is clear: operational and revenue-facing AI deployments are increasingly table stakes, but the real competitive advantage will go to organizations that can move AI adoption upstream into the boardroom and the C-suite. Both FinTechs and traditional banks have work to do in this area, but the opportunity to lead is significant for those willing to invest in strategic AI capabilities.