The delinquency rate among clients of banking institutions in Peru has increased exponentially in recent years, due to the lack of early detection of potentially delinquent clients, mainly due to the use of inadequate prediction techniques for the identification of delinquent clients. This causes profitability to be reduced, credit risk to increase and the country's economy to be unstable. Previously, different solutions were generated to prevent non-payment, however these studies were not applied in the Peruvian environment and did not cover the personal and financial variables necessary to improve the detection of delinquent clients. In this work, a delinquency prediction system is proposed using classification algorithms such as logistic regression and Random Forest, with the aim of improving and automating the early detection of delinquent clients and counteracting the increase in de...