Credit Risk Assessment in P2P Lending Using LightGBM and Particle Swarm Optimization

Abstract

Credit risk evaluation is a vital task in the P2P Lending platform. An effective credit risk assessment method in a P2P lending platform can significantly influence investors' decisions. Machine learning algorithm such as LightGBM can be used to evaluate credit risk. However, the results in evaluating P2P lending need to be improved. This research aims to improve the accuracy of the LightGBM algorithm by combining it with the Particle Swarm Optimization (PSO) algorithm. This research is novel as it combines LightGBM with PSO for large data from the Lending Club Dataset, which can be accessed on Kaggle.com. The highest accuracy also presented satisfactory results with 98.094% accuracy, 90.514% Recall, and 97.754% NPV, respectively. The combination of LightGBM and PSO has resulted in better outcome.