PUBLISHED DATE: 2026-07-24 01:09:51

AI for Core Banking Modernization: Reduce Risk and Accelerate Execution | Publicis Sapient

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AI for Core Banking Modernization: Reduce Risk and Accelerate Execution

Core banking modernization is one of the hardest execution challenges banks face. Legacy systems are deeply connected to payments, lending, servicing, risk and regulatory reporting, which makes change slow and risky. AI, specifically our Sapient Slingshot, can help banks modernize core systems with greater speed, control, and validation. Access the playbook

In this report

Why do banks struggle with core banking modernization?

For more than a decade, banks have invested in modernization initiatives designed to support real-time payments, digital servicing, open banking, cloud-native scalability and AI-enabled operations. Yet many of the most critical systems inside the enterprise remain difficult to transform.
The challenge is not a lack of vision. Most banks already understand the capabilities they need to compete in an increasingly digital and AI-driven market. The challenge is execution.
Unlike digital-native organizations, banks operate highly interconnected ecosystems built on decades of business logic, regulatory controls, operational processes and technology dependencies. Core deposits, lending, payments, servicing, risk management and regulatory reporting cannot simply be paused while new platforms are built.
As a result, modernization programs frequently become larger, slower and riskier than anticipated. This creates a modernization paradox: the systems that most need to change are often the systems least tolerant of disruption.

What makes core banking modernization so difficult?

Modernization efforts often encounter three persistent barriers:
For many institutions, these challenges create a widening gap between modernization ambition and modernization delivery

How AI improves core banking modernization

Much of the industry conversation around AI has focused on customer-facing applications and productivity gains. Increasingly, however, AI is being applied directly to the software development lifecycle itself.
Rather than relying exclusively on manual discovery, documentation and code transformation efforts, organizations can now use AI to:
The result is a more governed and scalable modernization process that helps reduce risk while accelerating delivery

How Sapient Slingshot supports banking modernization execution

Modernization programs rarely fail because organizations lack strategy. They struggle because execution becomes too complex, too expensive or too risky.
Sapient Slingshot was designed to address this challenge. By combining a persistent enterprise context graph with specialized AI agents across the software development lifecycle, Slingshot helps organizations understand legacy environments, accelerate transformation and continuously validate outcomes throughout the modernization journey.
The platform supports three critical stages of modernization:

Inside AI-assisted code modernization

Discover how Slingshot agents modernize legacy code in a few short clicks in this interactive demo.

Banking modernization use cases

Banks are already exploring AI-enabled modernization across critical domains, including:
Each presents unique challenges, but all require the same fundamental capability: modernizing critical systems while maintaining control, compliance and operational resilience.