Classification based credit risk analysis: The case of Lending Club

TL;DR

This paper analyzes credit risk using machine learning on Lending Club data, and designs a credit derivative to hedge default risk, demonstrating the effectiveness of classification algorithms in financial risk assessment.

Contribution

It introduces a novel application of credit default swap concepts to retail credit risk analysis using machine learning models.

Findings

Logistic Regression and Random Forest achieved high accuracy in default prediction.

The proposed credit derivative effectively hedges against default risk.

Performance measures confirm the models' robustness on test data.

Abstract

In this paper, we performs a credit risk analysis, on the data of past loan applicants of a company named Lending Club. The calculation required the use of exploratory data analysis and machine learning classification algorithms, namely, Logistic Regression and Random Forest Algorithm. We further used the calculated probability of default to design a credit derivative based on the idea of a Credit Default Swap, to hedge against an event of default. The results on the test set are presented using various performance measures.