Financial supply chain analysis with borrower identification in smart lending platform

Abstract

The popularity of the online peer-to-peer (P2P) lending platform in the financial supply chain (FSC) has grown tremendously in the past few years. However, it is pretty challenging to develop an efficient financial supply chain for Micro, Small, and Medium-Sized Enterprises (MSMEs) by providing credits and services. In this research, an FSC model analyzes by emphasizing the retailer and supplier relationship. The retailer predicts the market demand to maximize profit, and accordingly, orders to the suppliers and suppliers utilize the production capabilities in limited monetary value. In such a case, the supplier also needs to apply for loans to meet the production commitment. With an intelligent lending platform, applicants can get loans quickly and more conveniently from the lenders. Still, identifying defaulter borrowers is a difficult task on the peer-to-peer lending platform. The main focus of the lenders is to maximize profit and minimize risk by giving the loan to non-defaulter suppliers. The present study describes the importance of the financial parameters (λ) of the retailers and suppliers (MSMEs) and their impact on the related supply chain. We provide the proof of dependency of the financial parameter on the cost of debt and the ratio of the debt-to-equity by developing two propositions. We propose an innovative k-Random Boosting Classifiers (k-RBC) algorithm for identifying potential good and bad borrowers to capture this case. The time complexity of the k-RBC algorithm is O(n2log(n)). The results obtained from the study show a significant improvement in comparison to the outputs from existing approaches on the same datasets. Our algorithm gives 90% accuracy to identify potential good borrowers, whereas existing algorithms achieve up to 87% accuracy. Furthermore, the importance of the borrowers’ features and its impact are analyzed of the lending platform in FSC.