Exploring the Machine Learning-Based Decision Tree Technique in Assessing Credit Risk Likelihood of P2P Fintech Lending for Effective Marketing

Abstract

Peer-to-Peer lending as a significant alternative finance source in reducing the credit gap worldwide. It is an innovative Fintech business model that integrates lenders and borrowers through the internet without the interference of banks. These lending platforms as Non-banking financial institutions (NBFCs) perform the credit assessment and determine borrower's eligibility to safeguard investors and reduce default risk. The study aims to provide insight into their functionality introducing a novel concept of linking borrowers and lenders by exploring the decision tree technique algorithm based on machine learning for forecasting default risk. Additionally, it highlights the parameters for judging the reliability of borrowers raising funds through such platforms. Additionally, the credibility of these techniques has a psychological effect on investors in making effective investment decisions, and it has a significant impact on corporate professionals in the effective marketing of such platforms.