AI-Driven Risk Mitigation in Peer-to Peer Lending: A Systematic Literature Review and Bibliometric Analysis

Abstract

Peer-to-Peer (P2P) lending has evolved into a dynamic financial sector in recent year, attracting both investors and borrowers. This paper intends to perform a systematic literature review (SLR) to examine recent trends related to cutting-edge models, investigate
the influence of artificial intelligence, comprehend market dynamics, and evaluate as well as manage the risks linked to P2P lending platforms over the last decade. Several elements can impact these risks, including the platform’s design, regulatory environment, organizational structure, types of transactions, and the interrelationships among organizational components. The systematic analysis categorizes documents based on their methodological aspects and business considerations. Many proposals incorporate artificial intelligence, deep learning, or machine learning techniques; however, they often overlook the context of application, relevant variables within a business framework, explainability, and other critical factors. This study provides recommendations and outlines future research directions, emphasizing the need for further exploration in this field. The models are also applied to explore the factors that influence the success or failure of various peer to-peer (P2P) platforms, considering both financial and information systems perspectives. Additionally, we aim to suggest strategies to mitigate the potential risks associated with P2P lending platforms.
Keywords: Risk Management, Bibliometric Analysis, Systematic Literature Review, Risk Categorization, Artificial Intelligence Methods