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.

The advent of technologies such as artificial intelligence (AI), machine learning and the internet of things (IoT) have transformed the way in which the world operates. Customers now demand personalised experiences and firms that haven't digitised their businesses must transform in order to compete in this new world. One industry which has found it more difficult to adapt is banking, as many banks are saddled with rigid and core legacy application systems. Large banks and financial institutions continue to remain slow to adopt these emerging technologies which is preventing them from delivering an optimal customer experience, driving growth and keeping themselves relevant in a dynamic market. Banks must modernise their legacy banking systems in order to compete in this evolving marketplace.