APPLICATION OF MACHINE LEARNING MODELS FOR FRAUD DETECTION IN SYNTHETIC MOBILE FINANCIAL TRANSACTIONS
DOI:
https://doi.org/10.33480/jitk.v10i4.6420Keywords:
anti-money laundering, fraud detection, machine learning, paysim, random forest, synthetic datasetsAbstract
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.
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References
Basel, “Basel AML Index 2023 : 12th Public Edition Ranking money laundering and terrorist financing risks around the world,” Basel, vol. 10, pp. 1–42, 2023, [Online]. Available: https://index.baselgovernance.org/api/uploads/Basel_AML_Index_2023_12th_Edition_879b07b7b2.pdf
P. M. A. C. Rakesh Pandit, Sheetal Bawane, Jayesh Surana, “Credit Risk Assessment and Fraud Detection in Financial Transactions Using Machine Learning,” J. Electr. Syst., vol. 20, no. 3s, pp. 2061–2069, 2024, doi: 10.52783/jes.1807.
N. Naveed, S. Munawar, and A. Usman, “Intelligent Anti-Money Laundering Fraud Control Using Graph-Based Machine Learning Model for the Financial Domain,” J. Cases Inf. Technol., vol. 25, no. 1, pp. 1–21, 2023, doi: 10.4018/JCIT.316665.
M. S. Gal and O. Lynskey, “Synthetic Data: Legal Implications of the Data-Generation Revolution,” Iowa Law Rev., vol. 109, no. 3, pp. 1087–1156, 2024, doi: 10.2139/ssrn.4414385.
T. Wiehn, “Synthetic Data: From Data Scarcity to Data Pollution,” Surveill. Soc. , vol. 22, no. 4, pp. 472–476, 2024, doi: 10.24908/ss.v22i4.18327.
M. E. Lokanan, “Predicting mobile money transaction fraud using machine learning algorithms,” Appl. AI Lett., vol. 4, no. 2, pp. 1–16, 2023, doi: 10.1002/ail2.85.
S. M. N. Nobel et al., “Unmasking Banking Fraud: Unleashing the Power of Machine Learning and Explainable AI (XAI) on Imbalanced Data,” Inf., vol. 15, no. 6, pp. 1–23, 2024, doi: 10.3390/info15060298.
O. I. Alex, “Development and Implementation of a Machine Learning-Based Framework for Credit Card Fraud Detection : A Comparative Study of Random Forest and Logistic Regression Models,” vol. 9, no. 3, pp. 52–66, 2025.
P. Hajek, M. Z. Abedin, and U. Sivarajah, “Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework,” Inf. Syst. Front., vol. 25, no. 5, pp. 1985–2003, 2023, doi: 10.1007/s10796-022-10346-6.
A. Alwadain, R. F. Ali, and A. Muneer, “Estimating Financial Fraud through Transaction-Level Features and Machine Learning,” Mathematics, vol. 11, no. 5, 2023, doi: 10.3390/math11051184.
M. Elkabalawy, A. Al-Sakkaf, E. Mohammed Abdelkader, and G. Alfalah, “CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors,” Sustainability, vol. 16, no. 17, p. 7249, 2024, doi: 10.3390/su16177249.
A. Ostonov and M. Moshkov, “Comparative Analysis of Deterministic and Nondeterministic Decision Trees for Decision Tables from Closed Classes,” Entropy, vol. 26, no. 6, 2024, doi: 10.3390/e26060519.
A. Abdulla, G. Baryannis, and I. Badi, “An Integrated Machine Learning and MARCOS Method for Supplier Evaluation and Selection,” Decis. Anal. J., vol. 9, Oct. 2023, doi: 10.1016/j.dajour.2023.100342.
Z. Ali and A. Burhan, “Hybrid machine learning approach for construction cost estimation: an evaluation of extreme gradient boosting model,” Asian J. Civ. Eng., vol. 24, pp. 1–16, Apr. 2023, doi: 10.1007/s42107-023-00651-z.
J. H. Chen, X. L. Wang, and F. Lei, “Data-driven multinomial random forest: a new random forest variant with strong consistency,” J. Big Data, vol. 11, no. 1, 2024, doi: 10.1186/s40537-023-00874-6.
P. Handayani and A. Charis Fauzan, “KLIK: Kajian Ilmiah Informatika dan Komputer Machine Learning Klasifikasi Status Gizi Balita Menggunakan Algoritma Random Forest,” Media Online), vol. 4, no. 6, pp. 3064–3072, 2024, doi: 10.30865/klik.v4i6.1909.
M. E. Hasan and A. Wagler, “New Convolutional Neural Network and Graph Convolutional Network-Based Architecture for AI Applications in Alzheimer’s Disease and Dementia-Stage Classification,” AI, vol. 5, no. 1, pp. 342–363, 2024, doi: 10.3390/ai5010017.
J. de Pedro-Carracedo, J. Clemente, D. Fuentes-Jimenez, M. F. Cabrera-Umpiérrez, and A. P. Gonzalez-Marcos, “Photoplethysmographic Signal-Diffusive Dynamics as a Mental-Stress Physiological Indicator Using Convolutional Neural Networks,” Appl. Sci., vol. 13, no. 15, 2023, doi: 10.3390/app13158902.
S. M. Mbiva and F. M. Correa, “Machine Learning to Enhance the Detection of Terrorist Financing and Suspicious Transactions in Migrant Remittances,” J. Risk Financ. Manag., vol. 17, no. 5, 2024, doi: 10.3390/jrfm17050181.
V. Plotnikova, M. Dumas, and F. P. Milani, “Applying the CRISP-DM data mining process in the financial services industry: Elicitation of adaptation requirements,” Data Knowl. Eng., vol. 139, p. 102013, 2022, doi: https://doi.org/10.1016/j.datak.2022. 102013.
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