Pronóstico de morosidad de cartera vencida aplicando series temporales

The delinquency forecast is fundamental for credit risk management, as it allows identifying and anticipating areas with a high probability of default. By anticipating these risks, institutions can implement preventive measures and mitigation strategies. This research focuses on forecasting delinquency of past-due loans using time series, an essential aspect in the financial accounting of banking institutions. This study analyzes the delinquency forecast of past-due loans in a savings and credit cooperative in the city of Riobamba. For this purpose, a quantitative approach was used to analyze the delinquency forecast of past-due loans by applying time series techniques. A non-experimental design was adopted, focused on the collection and analysis of historical data with a longitudinal approach. Through this study, the evolution of delinquency was examined, and predictive models were developed to identify patterns and trends in the cooperative. The results show that time-series based forecasting techniques, such as ARIMA models, are effective in generating accurate predictions of delinquent loan portfolio delinquency. In addition, the analysis revealed significant variations with a decreasing trend in consumer portfolio delinquency, as well as an increase in the microcredit portfolio and in total portfolio delinquency.