Extension of Traditional Credit Scoring Models Based on FICO Scores: The Impact of Loan Purpose on Default Predictive Performance

Abstract

P2P online lending is a vital component of the fintech sector, and its rapid growth has heightened demands for predicting default risks. Traditional credit scoring models primarily rely on structured metrics such as FICO scores and debt-to-income ratios (DTI), yet they often overlook the predictive value of soft information like loan purpose. Using Lending Club’s 2016 loan data as a case study, this research constructs comparative models and employs machine learning methods including decision trees, random forests, logistic regression, and XGBoost to empirically examine the impact of loan purpose on default prediction accuracy. Results reveal a significant correlation between loan purpose and default risk, demonstrating its positive role in enhancing model accuracy. Particularly under recall-prioritized risk control strategies, the logistic regression and XGBoost models exhibit superior performance in default identification coverage. This study not only enriches the theoretical dimensions of credit risk modeling but also provides empirical evidence and practical pathways for P2P platforms and credit institutions to optimize their risk control systems.