Legal Accountability for Automated Credit Scoring in Indonesian Financial Technology Lending
Keywords:
Automated credit scoring, Algorithmic accountability, Fintech lending, Explainable artificial intelligence, Consumer protectionAbstract
Purpose – This study examines legal accountability for automated credit scoring in Indonesian fintech lending, focusing on responsibility allocation, data accountability, explainability, fairness, human oversight, and consumer redress across interconnected institutional actors.
Methodology – The study uses a qualitative socio-legal design and secondary thematic analysis of published focus group data involving 36 Indonesian stakeholders. The qualitative evidence is examined together with relevant regulatory materials and recent scholarship using deductive and inductive coding.
Findings – Automated scoring can expand financial inclusion and accelerate assessment, but it also creates fragmented responsibility, data-quality risks, algorithmic opacity, potential discriminatory effects, uneven human oversight, and limited opportunities to contest adverse decisions.
Implications – Fintech providers and regulators should strengthen data governance, algorithmic auditing, fairness monitoring, understandable explanations, meaningful human review, and accessible correction and redress procedures.
Originality – The study extends algorithmic accountability into Indonesian fintech lending by integrating legal, institutional, and technological dimensions throughout the automated credit-decision lifecycle.
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