For a platform designed to optimize bank pricing in real time, it was an uncomfortable contradiction: even the smallest change to a lending rate or pricing condition could take up to six weeks to go live.
This North America-based fintech, serving top-tier banks with AI-powered pricing tools, found itself caught between market expectations and legacy architecture. While customers expected instant responsiveness, the provider’s own systems remained bound to manual release cycles, code-heavy processes, and sprawling technical debt.
Something had to give.
The fintech’s platform was respected in the market. It offered differentiated products that enabled intelligent, dynamic pricing for mortgages, deposits, and personal lending.
But inside, the logic that powered these products was rigid. Business rules, rate conditions, and exception criteria were all hardcoded. Any update—however routine—required developer intervention, full test cycles, and code deployments.
The backlog grew. Engineering teams struggled to manage over 1,000 unresolved technical issues and more than 100 outdated components scattered across five siloed workspaces. And while each bank operated on its own instance, none could make configuration changes independently.
Roadmaps stalled. Client frustration grew. Four years passed with no breakthrough.
Persistent was brought in to address what the client couldn’t fix internally. Using SASVA™, its Generative AI-powered engineering platform, the Persistent team restructured how configuration worked—starting with a simple premise: business users should never need to rely on code to control pricing.
The transformation unfolded in two focused phases:
What once took six to eight weeks could now be completed in minutes—without writing a single line of code.
The result wasn’t just a faster release process. It was a complete change in how the platform was used.
Banks could now independently manage their pricing conditions—adjusting rules, testing logic, and rolling out updates without provider-side engineering support. Internally, onboarding new developers dropped from nearly two months to under two weeks. And with SASVA automating release planning and test cycles, engineering capacity was redeployed toward product innovation.
Previously multi-week cycles were now completed in minutes, directly from the admin panel.
The numbers made the impact clear:
According to McKinsey, enterprises that embed Generative AI in software development pipelines see a 40% reduction in time-to-market and a 50% improvement in engineering throughput. This client now operates at those benchmarks—by design, not by exception.
With the core issue resolved, the fintech could finally evolve. What was once a reactive platform is now proactively extensible. The same configuration engine now enables:
The platform was no longer a bottleneck. It became a framework for intelligent change—built to evolve with market needs and user behavior. As Gartner projects, by 2026, 30% of enterprise platforms will automate more than half of their network activities, with configuration-first design playing a central role. This fintech is already on that curve.
Persistent didn’t just deliver tooling. It brought focused execution aligned with product leadership. The transformation was co-developed, not handed off. Milestones were met at 40% faster velocity than internal forecasts. Most importantly, the engagement returned control to the users who shape pricing strategy—not just the engineers who maintain it.
What had frustrated teams for four years was reimagined and resolved in six months.