Machine learning application in online lending risk prediction

TL;DR

This paper explores the use of ensemble machine learning models, specifically random forest and XGBoost, for predicting online lending risk, highlighting the importance of external data sources in improving prediction accuracy.

Contribution

It introduces a novel application of ensemble ML models to online lending risk prediction using diverse data sources, demonstrating improved classification performance over traditional models.

Findings

XGBoost outperforms random forest in classification accuracy.

External data like zhimaScore and social network info are key predictors.

Ensemble models effectively incorporate heterogeneous data for risk assessment.

Abstract

Online leading has disrupted the traditional consumer banking sector with more effective loan processing. Risk prediction and monitoring is critical for the success of the business model. Traditional credit score models fall short in applying big data technology in building risk model. In this manuscript, data with various format and size were collected from public website, third-parties and assembled with client's loan application information data. Ensemble machine learning models, random forest model and XGBoost model, were built and trained with the historical transaction data and subsequently tested with separate data. XGBoost model shows higher K-S value, suggesting better classification capability in this task. Top 10 important features from the two models suggest external data such as zhimaScore, multi-platform stacking loans information, and social network information are important factors in predicting loan default probability.