Mitigating Financial Fraud in Indonesia: A Grounded Theory Approach to Fraud Detection in P2P Lending, Rural Banks, and Conventional Banks

Abstract

The rapid digitalization of Indonesia’s financial sector, driven by the growth of Peer-to-Peer Lending (P2PL) platforms, Rural Credit Banks (BPR), and conventional banks, has expanded financial inclusion but also heightened the risk of fraud. This study investigates the fraud detection systems (FDS) employed by these diverse financial institutions using a grounded theory approach. By conducting semi-structured interviews with key stakeholders—including directors, compliance managers, and fraud control officers—the research identifies critical factors influencing fraud detection in Indonesia’s financial ecosystem. The study reveals that while larger institutions leverage advanced machine learning models and real-time transaction monitoring, smaller entities like BPRs rely heavily on manual oversight due to resource constraints. Furthermore, the integration of multi-subject perception differences is highlighted as a novel and effective approach for fraud detection, particularly in P2PL platforms, by analyzing multiple data sources to reduce bias and increase accuracy. However, the study also uncovers significant challenges in implementing robust FDS, including high false-positive rates, regulatory complexities, and resource limitations, particularly in smaller institutions. The findings emphasize the need for a tailored fraud detection framework that addresses the unique needs of each sector, balancing technological advancements with operational feasibility.