Digital Lending Platforms: Creditworthiness Assessment Using AI

Abstract

The explosive growth in digital lending platforms is changing the old credit world as we knew it and making available to the borrowers quicker rates of access to financial services and posing new challenges to lenders regarding risk measurement. This paper describes the use of artificial intelligence (AI) in credit risk assessment, highlighting the possibility of its use in improving decision-making and default prevention along with promoting financial inclusion. As digital lenders use machine learning, natural language processing and predictive analytics to digest ample amounts of diverse data, including transactional history and social behavior as well as alternative data sources, more precise, real-time analyses of borrower reliability can be made. In this study, the performance of different AI models, such as decision trees, random forests, neural networks and ensembles approaches, in warning credit risk and streamlining approval of loans is investigated. A comparison feature illustrates the performance, interpretability, and scalability of such models that meet operational efficiency as well as regulatory compliance. Also, the paper explores the issues surrounding AI-based credit assessment, like data privacy, AI-bias and ethics in AI, offering several frameworks in reducing threats whilst maintaining transparency and fairness. Evidence suggests that AIbased credit scoring models can be more accurate, faster, and dynamic than the existing methods of credit evaluation, especially in the emerging economies where formal credit histories are scant. It ends with a hire of provocative conclusions in the form of strategic tips that financial institutions should adhere to incorporate AI in a responsible way with a combination of creating a continuous monitoring of the models, variety of data sources (inclusion), and compliance to ethical lending concepts. Through technological innovations and in-depth risk management, AI-led digital lending services can transform the model of credit evaluation and enhance better access to financial opportunities, as well as contribute to sustainable development of the digital economy.