Finastra launches AI repair tool for payment banks

Finastra has launched Repair Recommendations within its AI OperatorAssist product for payments processing. The feature targets banks that handle payment exceptions.

It provides scheme-aware guidance to operations teams handling errors, disruptions, and other exceptions across the payments lifecycle. It is designed to identify discrepancies, analyse likely causes and suggest corrections that align with network and scheme rules.

Payment repair remains a labour-intensive part of banking operations, particularly as institutions face rising transaction volumes across cross-border, real-time and digital payment flows. Errors and failed transactions can increase costs, delay settlement and put pressure on specialist staff, who often must investigate incomplete or inconsistent data.

The new function is intended to reduce the time spent by wire-room operators, exception and repair specialists, and technical support teams reviewing and fixing problem payments. It could also help standardise how banks handle complex repair cases and reduce dependence on staff with highly specialised knowledge.

Scheme rules

The system validates recommendations against industry payment schemes and network rules, including Swift, Fedwire, SEPA, UPI and Nexus. It presents guidance to human operators for review and approval rather than applying it automatically.

Human oversight matters as banks and software suppliers introduce artificial intelligence into regulated operational environments without removing accountability for final decisions. Finastra said the recommendations are intended to support human decision-making and be refined over time.

The product will sit alongside Global PAYplus and Payments To Go, two of Finastra's payments offerings, as an extension of AI OperatorAssist's existing investigative and analytical functions. The move places the company among financial technology providers using AI in back-office banking tasks, where efficiency gains can be measured through lower exception volumes, faster handling and fewer manual checks.

Barry Rodrigues, Executive Vice President, Payments, at Finastra, outlined the company's view of the launch.

"With Repair Recommendations, we're empowering banks to upgrade their payment operations - reducing resolution times and minimizing errors. As payment volumes increase, AI can help deliver immediate value by accelerating repairs, streamlining onboarding, and strengthening resilience. We're helping banks to scale their operations, and deliver faster, more reliable services to their customers," said Rodrigues.

Operational pressure

The announcement comes as banks continue to modernise payments infrastructure while coping with more demanding customer expectations around speed and availability. Operations teams often must manage a mix of legacy systems, newer instant payment rails and a growing range of domestic and international message formats.

In that environment, exception handling has become an area where software vendors see scope for AI tools that can reduce repetitive work without removing staff from the process. Finastra said the latest addition could also shorten onboarding for new employees by providing repair guidance that would otherwise rely more heavily on experienced specialists.

Robin LoGiudice, Strategic Advisor, Datos Insights, described the operational problem the tool is intended to address.

"Manual processes continue to pose a major operational risk for financial institutions. Despite automation advances, payment teams still spend extensive time sourcing data and diagnosing exceptions, making operations costly and slow. AI-powered, validated, scheme-aware recommendations accelerate the gathering, extraction, and analysis of payment information, enabling faster exception resolution while improving efficiency and precision," said LoGiudice.

Finastra describes itself as a global supplier of financial services software with customers in more than 100 countries. Its push to add AI functions to payments operations reflects a broader trend in banking technology, as suppliers attach practical workflow tools to established platforms rather than rely on fully autonomous decision-making.

Repair Recommendations form part of a wider effort to expand AI use across the payments lifecycle while keeping governance controls and human review in place.