Advancements in fintech algorithms have improved decision-making efficiency in credit scoring, investment advice, and financial product offerings. However, these automated systems raise ethical concerns related to algorithmic bias, lack of transparency, and accountability. Social inequalities embedded in historical data risk reinforcing discrimination in digital financial services, particularly in Southeast Asia’s evolving regulatory environment. This study explores ethical dilemmas in algorithmic decision-making across fintech platforms and assesses company responses. Using a qualitative multiple-case study of three Indonesian fintech firms in peer-to-peer lending, e-wallet, and robo-advisory sectors, data were gathered through semi-structured interviews and internal document analysis. Results indicate algorithmic bias as the most critical issue, followed by transparency and accountability gaps. Peer-to-peer lending firms demonstrate better ethical readiness via regular audits, while others show limited mitigation efforts. The study proposes a conceptual model emphasizing fairness, transparency, and accountability, offering practical insights for regulators and industry to strengthen ethical governance in Indonesia’s AI-based fintech ecosystem.
