Psychology of the Borrower and Behavioral Scoring: New Approaches to Credit Risk Analysis

In the context of rapid digital transformation and the large-scale expansion of consumer lending volumes, traditional credit risk assessment models, which rely primarily on the analysis of the borrower’s financial indicators, are losing their adequacy. This study proposes a rethinking of risk management through the integration of the borrower’s psychological characteristics and behavioral scoring methods. The aim of the work is to propose a multi-level behavioral scoring architecture that combines automated collection of heterogeneous external data, advanced intelligent filtering mechanisms, and predictive analysis of behavioral patterns. The methodological basis consisted of a systematic review of existing scientific publications from recent years, supplemented by an empirical examination of the implementation of an automated scoring platform. The results demonstrate that enriching classical scoring models with non-financial behavioral markers—such as the borrower’s legal status, the dynamics of employment relationships, and geographic segmentation of application submissions—provides a significant improvement in default prediction accuracy and enables the detection of sophisticated fraud schemes. The practical implementation of the proposed solution led to a reduction in the share of manual application reviews from 100 % to approximately 10 %, which made it possible to scale the processing of the increased volume of requests without a proportional increase in labor costs. The materials presented in this work will be of interest to risk management specialists, credit institution employees, and other researchers engaged in behavioral economics and the application of machine learning methods in the financial sector.