RBI mulls over easing model risks in bank credit

REs generally use various models as part of their credit management, including for credit appraisal, borrower scoring, pricing, and risk management, among others. Inherently, model outputs are exposed to uncertainties. This potentially exposes REs to model risk, which has implications on prudential aspects

Mumbai: The Reserve Bank on Monday proposed to lay down principles for management of model risks in credit for banks and other regulated entities with a view to ensuring prudence and robustness.Regulated Entities (REs) generally use various models as part of their credit management, including for credit appraisal, borrower scoring, pricing, and risk management, among others. Inherently, model outputs are exposed to uncertainties as they are based on assumptions which may not manifest in the envisaged ways and may take different forms in a real-world scenario, the RBI said in a draft circular on ‘Regulatory Principles for Management of Model Risks in Credit’.

This potentially exposes REs to model risk, which has implications on prudential aspects of credit risk management, compliance and reputational risk. With a view to ensuring prudence and robustness, in the use of such models, RBI proposed to lay down certain broad regulatory principles which should be adopted by REs. “REs shall put in place a detailed Board approved policy with regard to model risk management framework for all models deployed, covering the entire model life cycle,” the draft said. The policy should cover details of governance and oversight aspects commensurate with model materiality; processes around model development or selection; and documentation for models deployed. It should also cover independent vetting / ongoing validation or review processes; change control; and the monitoring and reporting framework including role of internal audit function.

‘Credit risk model’ refers to any quantitative method that applies statistical, economic, financial, or mathematical principles and assumptions to process data into an output to be used for credit decisions.