Abstract

This review paper explores the design and implementation of a credit scoring system tailored for Micro, Small, and Medium Enterprises (MSMEs) using transaction-level behavioral data. Traditional credit scoring models often rely on static financial ratios or formal credit histories, which are insufficient or unavailable for a large proportion of MSMEs—especially in developing economies. In contrast, behavioral data—such as payment patterns, sales cycles, account turnover, and supplier-customer transactions—provide dynamic, real-time indicators of financial health and creditworthiness. This paper synthesizes findings from recent empirical studies, machine learning applications, and digital finance innovations to present a comprehensive framework for MSME credit scoring based on behavioral signals. It critically assesses data sources, model training approaches, feature engineering methods, and bias mitigation strategies. Furthermore, it evaluates the implications of using alternative data for financial inclusion, risk management, and sustainable lending. The review concludes by identifying key success factors, current limitations, and future directions for adopting behavioral credit scoring at scale within fintech ecosystems, banks, and non-bank financial institutions.