Work

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

Year: 2023

Type: article

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.

Source: Register Jurnal Ilmiah Teknologi Sistem Informasi

DOI: 10.26594/register.v9i1.3060

Authors: Yosza Dasril, Much Aziz Muslim, M. Faris Al Hakim, Jumanto Jumanto, Budi Prasetiyo

Institutions: Tun Hussein Onn University of Malaysia, State University of Semarang

Language: English

Topic: Financial Distress and Bankruptcy Prediction

Subfield: Accounting

Field: Business, Management and Accounting

Domain: Social Sciences

Open Access status: diamond

Funder: Universiti Tun Hussein Onn Malaysia

OpenAlex ID: W4366133333

FWCI: 6.407

Cites: 56

Cited by: 11

Related to: 10