Heri Supriyadi, Priyarsono, D.S., Noer Azam Achsani, Trias Andati
Internal fraud in microcredit business processes has caused significant losses for the banking. Internal fraud was one type of operational risk that banks/financial institutions often face that focuses on microcredit services. The most common types of fraud were Corruption and Asset Abuse (ACFE), such as ‘Tempilan’ credit (loan partly used by the debtor), ‘Topengan’ credit (misused loan), and Fictitious Credit. Machine learning that is run automatically was used to predict internal fraud in microloan business processes. This study apply Extreme Gradient Boosting (XGBoost) model to predict the possibility of fraud events. The level of possible fraud events was manifested in the form of "Risk-Scoring." This study also conducts the use of analysis with CFA (Confirmatory Factor Analysis) method affects a person to commit fraud in the process of microloan services. This research result is expected to provide input to the banking industry that serve microcredit to make efforts and make fraud prevention strategies more effective.