<![CDATA[The financial industry faces challenges in detecting fraud. The 2023 Basel Anti-Money Laundering (AML) Index report shows a worsening money laundering risk trend over the last five years in 107 countries. And according to the Financial Action Task Force (FATF) in 2023, this is exacerbated by financial institutions which have problems with low reporting of suspicious financial transactions (Suspicious Transaction Report). Limited access to confidential financial transaction data is an obstacle in developing machine learning-based fraud detection models. To overcome this challenge, the research uses PaySim synthetic datasets that mimic real financial transaction patterns. The CRISP-DM approach is used, including the Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation and Deployment stages. The algorithms used are Decision Tree, Random Forest, and XGBoost. Model evaluation is carried out using accuracy, precision, recall, F1-score, specificity, cross-validation and ROC-AUC metrics. The results show that the Random Forest algorithm has the best performance with 99% accuracy, followed by XGBoost (98.9%) and Decision Tree (97%). Data analysis shows that cash-out and transfer transactions have the highest risk of fraud. This model has proven effective in detecting suspicious financial transactions with a high level of accuracy. This research makes a significant contribution to mitigating financial risks, supporting anti-fraud policies, and encouraging innovation in fraud detection using synthetic data.]]>
