Who gets the credit? Digitizing psychometric and character-based credit scoring in fintech microlending

This study addresses a critical gap in inclusive finance by developing a framework for digitizing character-based credit assessment in Indonesia's fintech microlending ecosystem. Traditional credit scoring systems have systematically excluded individuals without formal credit histories, particularly in emerging markets where approximately 97.74 million Indonesians remain unbanked. While character-based assessments have proven effective in manual microlending contexts, transforming these qualitative evaluations into scalable digital metrics remains inadequately explored. Through a qualitative research design employing the Delphi method with 22 cross-sectoral stakeholders, this study identifies and validates 18 key components for character-based credit scoring across six thematic areas: financial behavior metrics, digital KYC implementation, behavioral scoring indicators, character assessment algorithms, community-based scoring elements, and financial stress testing. Grounded in Information Asymmetry Theory, our proposed “Character-Based Risk Mapping” framework integrates these components across pre-decision, decision, and post-decision phases of the lending process to address distinct forms of information asymmetry. Unlike conventional models that prioritize data centralization and algorithmic abstraction, this framework emphasizes socio-cultural legitimacy, interpretability, and regulatory compliance while enabling more inclusive, accurate, and ethically sound credit assessment for underserved populations. The framework provides fintech companies, regulators, and policymakers with a structured approach to balance innovation with social responsibility in digitizing trust-based lending practices within Indonesia's diverse financial landscape.