Articlediamond2023

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

Yosza Dasril · Much Aziz Muslim · M. Faris Al Hakim · Jumanto Jumanto · Budi Prasetiyo

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Financial Distress and Bankruptcy Prediction FinTech, Crowdfunding, Digital Finance Data Mining and Machine Learning Applications

Abstract

The 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. The machine learning algorithm that can be used to evaluate credit risk as LightGBM, however, the results in evaluating P2P lending need to be improved. The aim of this research is to improve the accuracy of the LightGBM algorithm by combining the Particle Swarm Optimization (PSO) algorithm. The novelty developed in this research is combining 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% of accuracy, 90.514% of Recall, and 97.754% of NPV respectively. The combination of LightGBM and PSO shows better results.

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References

2015Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research 2003Credit rating analysis with support vector machines and neural networks: a market comparative study 2014A data-driven approach to predict the success of bank telemarketing 2000A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers 2018Study on a prediction of P2P network loan default based on the machine learning LightGBM and XGboost algorithms according to different high dimensional data cleaning 2015Risk assessment in social lending via random forests 2015Determinants of Default in P2P Lending 2015Instance-based credit risk assessment for investment decisions in P2P lending

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