On the dynamics of credit history and social interaction features, and their impact on creditworthiness assessment performance

TL;DR

This study analyzes how credit history, repayment behavior, and social network features influence creditworthiness assessment over time using a large machine learning dataset, revealing key periods where social data impacts scoring.

Contribution

Introduces a machine learning framework analyzing 97,000 cases to understand the dynamic influence of social and historical data on credit scoring performance.

Findings

Credit history improves performance rapidly in first 6 months then stabilizes.

Social network features significantly impact credit scoring at application and during the first months.

Business scoring benefits from social data throughout the entire 12-month period.

Abstract

For more than a half-century, credit risk management has used credit scoring models in each of its well-defined stages to manage credit risk. Application scoring is used to decide whether to grant a credit or not, while behavioral scoring is used mainly for portfolio management and to take preventive actions in case of default signals. In both cases, network data has recently been shown to be valuable to increase the predictive power of these models, especially when the borrower's historical data is scarce or not available. This study aims to understand the creditworthiness assessment performance dynamics and how it is influenced by the credit history, repayment behavior, and social network features. To accomplish this, we introduced a machine learning classification framework to analyze 97.000 individuals and companies from the moment they obtained their first loan to 12 months afterward. Our novel and massive dataset allow us to characterize each borrower according to their credit behavior, and social and economic relationships. Our research shows that borrowers' history increases performance at a decreasing rate during the first six months and then stabilizes. The most notable effect on perfomance of social networks features occurs at loan application; in personal scoring, this effect prevails a few months, while in business scoring adds value throughout the study period. These findings are of great value to improve credit risk management and optimize the use of traditional information and alternative data sources.