Box 1: Decomposition of the Performance of Credit Vintages - Financial Stability Report - Second Half of 2024
The main purpose of these documents is to provide semiannual information on the vulnerabilities and risks of the financial system. The views presented and potential errors are the sole responsibility of the authors and their contents do not compromise the Board of Directors of Banco de la República
The cohort of credits that are originated in a particular period (generally one month) and are related to each other as they have been originated by credit institutions (CIs) under particular allocation conditions, risk appetite of the institutions, and economic situation1 is called vintage. Generally, the evolution of the non-performing loans indicator (NPL), which is defined as the percentage of loan portfolio in default over gross loan portfolio, is used to evaluate the payment trend of debtors who obtained credits in each of these vintages and to anticipate the future performance of the aggregate indicator. In this context, the analysis of vintages is a tool that could produce early warnings that would support the making of possible decisions for the activation of prudential measures by local financial authorities2.
The Financial Stability Report for the first half of 2024 mentioned a deterioration in the credit risk indicators of the consumer and microcredit loan portfolio, which were the credit modalities that registered the highest NPLs of all loan portfolios (8.4% and 10.1%, in their order); even for the microcredit loan portfolio this indicator has been the highest observed in its history. This box seeks to identify the determinants of the deterioration of these types of loans, through the decomposition of the NPL into factors that can help to understand their performance; in this way, it is possible to observe whether the dynamics of the NPL that have been observed in recent months are due to a particular factor or if it is an aggregation of different determinants, which can help financial authorities to better understand the dynamics of the indicator and take targeted measures to reduce the materialization of credit risk.
























