LEVERAGING ALTERNATIVE DATA FOR CREDIT RISK ASSESSMENT: OPPORTUNITIES, CHALLENGES, AND REGULATORY CONSIDERATIONS

Abstract

The rapid digital transformation of financial services has fundamentally changed traditional approaches to credit risk assessment. Conventional credit scoring models have historically relied on financial statements, income verification, collateral, and credit bureau records to evaluate borrowers' repayment capacity. However, the emergence of financial technology (FinTech), artificial intelligence (AI), machine learning (ML), big data analytics, and digital banking has introduced new opportunities for assessing creditworthiness through alternative data sources. Alternative data include digital payment history, mobile phone usage, e-commerce transactions, utility payment records, social media activity, geolocation information, government databases, and other non-traditional sources that provide additional insights into an individual's financial behavior and repayment capacity.

The growing use of alternative data has significantly improved financial inclusion by enabling financial institutions to evaluate borrowers who lack formal credit histories, commonly referred to as "thin-file" or "credit invisible" customers. These innovations have expanded access to credit for individuals, small businesses, and underserved populations while improving prediction accuracy and reducing default risk. At the same time, the increasing dependence on large-scale data collection and automated decision-making raises important concerns regarding data privacy, algorithmic bias, transparency, cybersecurity, consumer protection, and regulatory compliance.

This paper examines the theoretical foundations of alternative data in creditworthiness assessment, analyzes the major categories of alternative information used by modern financial institutions, discusses the opportunities and limitations associated with AI-driven credit scoring models, and reviews international regulatory approaches designed to ensure responsible and ethical use of alternative data. The study argues that future credit risk management systems should combine technological innovation with robust governance frameworks, explainable artificial intelligence, and effective regulatory oversight to ensure fairness, transparency, and sustainable financial development.