FinTechs Bet on AI and Lending to Drive Modernization

Earnings season is showing that bank modernization demand is landing in faster payments, cloud and digital banking, and artificial intelligence tools aimed at credit, fraud and other defined operating workflows.

The pattern is visible in results and management commentary from Jack Henry, FIS and nCino. Jack Henry reported bundled core, digital and card wins and higher faster payments adoption. FIS reported continued growth across banking and payments, as well as more customers using its AI products. Cloud-based software platform nCino described AI use inside lending and credit operations.

Jack Henry said Aug. 18 that of its 58 competitive core wins during fiscal 2026, 59% included core, digital banking and card capabilities, up from 39% of its core deals a year earlier. Additionally, 14 of the 58 wins involved institutions with more than $1 billion in assets.

Payments provided another measure of adoption. Jack Henry reported fiscal-year increases of 25% for Zelle, 24% for the RTP® network and 29% for the FedNow® Service among its clients. Payment transaction volume across those channels increased 45% year over year in the fourth quarter.

Chief Financial Officer Mimi Carsley said the company is also beginning to see small business and faster payment use cases, including tokenized deposits and Open USD.

“We’re seeing not only healthy adoption in those, but increasing the dollar volume of those transactions,” Carsley said.

Cloud conversions are continuing alongside those applications. Jack Henry signed 36 contracts during the fiscal year to move institutions from in-house deployments to its private cloud, including 15 institutions with more than $1 billion in assets. The company said 79% of its core clients now operate in its private cloud.

FIS’ results indicated that more than 30 clients have signed for or are consuming modernized components of its Total Issuing Solutions platform.

AI Finds Defined Work

The AI examples from the earnings materials were more specific about the jobs the technology is being asked to perform.

FIS reported that more than 200 customers live on its AI products and there are more than 500 opportunities in its pipeline. Internally, the company said AI has increased engineering throughput by 1.5 to 2 times, reduced software defects by about 30%, cut manual tickets by roughly 70% and reduced triage time by about 75%.

Jack Henry said it has 22 AI-enabled products in market and has identified more than 20 additional capabilities for release over the next six months. One sits inside Financial Crimes Defender, where AI produces a draft suspicious activity report summary after an investigation. Management said the capability can reduce SAR drafting time by 75% to 85%.

AI is also being used in Banno conversations to translate more than 200 languages and in Jack Henry’s customer relationship management to generate relationship summaries and suggested next steps.

Adelson said some of the AI capabilities are intended to improve existing products rather than generate a separate charge.

Examples from nCino offered insight into modernization of lending activities and cost savings for financial institutions.

CEO Sean Desmond said one enterprise customer in the United States estimated that the “locate and file” capability in Banking Advisor can save 160,000 employee hours annually. Using roughly $35 per hour for median loan officer compensation, nCino calculated potential annual savings of more than $5.5 million for that customer. More than 230 customers had purchased AI Intelligence Units by the end of the quarter.

Continuous Credit Monitoring provided another lending use case. Rather than manually reviewing a commercial portfolio quarterly, semiannually or annually, nCino said the system can assess more than 40 credit and operational indicators daily, identify loans warranting attention and create documentation for review.

Desmond said the technology gives relationship managers and credit teams more time to focus on issues “requiring careful judgment,” while providing senior executives with a portfolio-level view of risk.

The use cases fit a broader pattern identified in the May PYMNTS Intelligence report, “Financial Services Pulls Ahead in the Enterprise AI Race.” Financial services and insurance companies surveyed had AI actively deployed across an average of 27 of 75 tasks, compared with 10 in healthcare. Adoption reached 65% for revenue recognition and accounting close and 60% for both credit risk assessment and sales forecasting.

Deployment was lower in several other functions. AI adoption stood at 20% for know your customer/know your business and identity verification, 25% for API orchestration and integration, and 10% for A/B testing and experimentation.

The biggest reported obstacle is not the AI budget. The report found that 30% of financial services firms cited data quality and fragmentation as their top barrier to further deployment, compared with 15% citing internal skills and talent and 10% each citing budget and resource constraints and integration with existing systems.

Taken together, the earnings material points to modernization reaching identifiable banking functions. Faster payment adoption and cloud conversions are continuing, while the clearest AI use cases are appearing in credit monitoring, document retrieval, financial crime work and internal operations. Expansion into additional functions could depend in part on addressing the data quality and fragmentation problems that financial services firms identify as their leading AI constraint.

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