Breaking the Cycle of Recurring Failures: Applying Generative AI to Root Cause Analysis in Legacy Banking Systems

TL;DR

This paper presents a novel AI-driven approach combining knowledge-based generative AI agents with the Five Whys technique to improve root cause analysis in legacy banking systems, reducing recurring failures.

Contribution

It introduces an innovative method that automates and enhances root cause analysis using GenAI, addressing fragmentation and superficial resolutions in legacy banking environments.

Findings

70% of incidents linked to internal code issues rather than management or vendor failures

Identified over 400 files with similar root causes across 5,000 projects

Automated analysis improves proactive problem resolution

Abstract

Traditional banks face significant challenges in digital transformation, primarily due to legacy system constraints and fragmented ownership. Recent incidents show that such fragmentation often results in superficial incident resolutions, leaving root causes unaddressed and causing recurring failures. We introduce a novel approach to post-incident analysis, integrating knowledge-based GenAI agents with the "Five Whys" technique to examine problem descriptions and change request data. This method uncovered that approximately 70% of the incidents previously attributed to management or vendor failures were due to underlying internal code issues. We present a case study to show the impact of our method. By scanning over 5,000 projects, we identified over 400 files with a similar root cause. Overall, we leverage the knowledge-based agents to automate and elevate root cause analysis, transforming it into a more proactive process. These agents can be applied across other phases of the software development lifecycle, further improving development processes.