Executive summary

Driven by regulatory persuasion and a volatile macro environment, there is emphasis across banks to evolve from a traditional passive monitoring approach to a modern dynamic credit risk monitoring set-up characterized by a robust Early Warning System – ‘EWS’. This would imply a shift towards a systematic data driven approach that also incorporates forward-looking data, a reduction in false positives through more evolved model-based triggers and continuous learning, and an empathetic and contemporary UI/UX integrated with the credit risk workflow.

Regulatory suasion, technological advances mean  it’s time for advanced, forward-looking EWS

Credit monitoring infrastructure at banks has not kept pace with tightening regulatory requirements. Regulators  increasingly expect banks to take a more proactive approach towards monitoring credit risk, including focusing on forward looking data and deploying a broader set of indicators in their early warning systems. With the shift in interest rate cycle and regulators becoming more stringent when evaluating credit monitoring set-ups at banks, existing monitoring systems will require an overhaul to be in sync with regulatory expectations.

The European Banking Authority (EBA) guidelines on loan origination and monitoring call for regular evaluation of  relevant quantitative and qualitative early warning indicators, supported by an appropriate information technology  and data infrastructure. This has now been backed by the Prudential Regulation Authority’s (PRA) 2023 supervisory  priorities, where it stated the need to evaluate EWS frameworks at banks “given many credit risk metrics are backward looking”. Leveraging forward-looking data and computing power has rarely been so clearly emphasised.