Internal risk rating model for finance institutes based on customer payment behaviour

Abstract

With the implementation of International Financial Reporting Standards (IFRS) 9, Licensed Finance Companies (LFCs) are required to adopt a risk-averse approach to conservatively predict customer risk. As per the Central Bank of Sri Lanka’s (CBSL) directives on credit risk management for LFCs [1], credit facilities must be classified as either performing loans (PLs) or non-performing loans (NPLs) based on two criteria: (1) days past due (DPD) and (2) potential risk. Currently, LFCs predominantly rely on the DPD-based method. While this approach is simple to compute and interpret, it overlooks a customer’s payment history and behavioral trends. The second approach, classification based on potential risk—also known as Internal Risk Rating (IRR)—is not widely practiced. Organizations that have attempted this method primarily rely on demographic and geographical attributes. A significant drawback of this approach is that the data remains static, as it is recorded only at the time the credit facility is granted and does not capture subsequent changes. Additionally, LFCs collect vast volumes of business transaction data daily, including detailed payment histories. However, this rich dataset is primarily utilized for business monitoring through reports and dashboards rather than for predictive modeling. This study aims to develop an IRR model leveraging customer payment history data to improve risk assessment.