The Impact of Key Economic Indicators on Money Laundering Risk in Malaysia: A Comparative Study of Machine Learning Algorithms
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Abstract
This study investigates the impact of key economic indicators on money laundering risk in Malaysia through a comparative analysis of machine learning (ML) algorithms. The study utilises three ML techniques, namely Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to predict Malaysia’s money laundering risks based on a dataset from 2000 to 2024. The dependent variable, money laundering risk, is analysed against a set of independent economic indicators, including Gross Domestic Product (GDP) per capita, exchange rate, annual inflation, Consumer Price Index (CPI), net secondary income, trade-related metrics, tax revenue and unemployment rate. To address class imbalance and enhance model robustness, data pre-processing, resampling, cross-validation and hyperparameter tuning were systematically implemented. Model performance was evaluated using multiple classification metrics to capture different aspects of predictive accuracy. Results demonstrate that NB consistently outperformed the other algorithms, achieving the strongest overall classification accuracy, agreement beyond chance, lowest prediction error and highest discriminatory power. Feature importance analysis using Recursive Feature Elimination (RFE) with LR algorithm identified the unemployment rate, import volume index, and exchange rate as the most influential predictors of money laundering risk. These findings underscore the value of probabilistic ML approaches and feature selection techniques in enhancing anti-money laundering (AML) risk assessment and provide actionable insights for policymakers and regulatory authorities in Malaysia.
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References
Ahmed, J., Mughal, M., & Martínez‐Zarzoso, I. (2021). Sending money home: Transaction cost and remittances to developing countries. The World Economy, 44(8), 2433–2459. https://doi.org/10.1111/twec.13110
Alotibi, J., Almutanni, B., Alsubait, T., Alhakami, H., & Baz, A. (2022). Money laundering detection using machine learning and deep learning. International Journal of Advanced Computer Science and Applications, 13(10), 732–738. https://doi.org/10.14569/IJACSA.2022.0131087
Avdjiev, S., Bruno, V., Koch, C., & Shin, H. S. (2019). The dollar exchange rate as a global risk factor: Evidence from investment. IMF Economic Review, 67(1), 151–173. https://doi.org/10.1057/s41308-019-00074-4
Awang Abu Bakar, N., Yahya, N., Idris, N., Ali, E., Zain, J., Khairuddin, E., Abidin, A., Murtaj, S., & Maidin, S. (2024). The determinant factors for the issuance of Central Bank Digital Currency (CBDC) in Malaysia using machine learning framework. Journal of Applied Data Sciences, 5(2), 808-821. https://doi.org/10.47738/jads.v5i2.176
Basel Institute on Governance. (2024). Basel AML index 2024. Retrieved 9 August 2025, from https://index.baselgovernance.org
Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. Boateng, N. V., Amoako, N. E. K., Ajay, N. O., & Adukpo, N. T. K. (2025). Harnessing Artificial Intelligence for combating money laundering and fraud in the US financial industry: A comprehensive analysis. Finance & Accounting Research Journal, 7(1), 37–49. https://doi.org/10.51594/farj.v7i1.1814
Canhoto, A. (2020). Leveraging machine learning in the global fight against money laundering and terrorism financing: An affordances perspective. Journal of Business Research, 131, 441–452. https://doi.org/10.1016/j.jbusres.2020.10.012
Cardoso, M., Saleiro, P., & Bizarro, P. (2022). LaundroGraph: Self-supervised graph representation learning for anti-money laundering [Paper presentation]. Proceedings of the Third ACM International Conference on AI in Finance, 130–138. https://doi.org/10.1145/3533271.3561727
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/10.1613/jair.953
Department of Statistics Malaysia. (2025). Balance of payments 2025. Retrieved 9 August 2025, from https://www.dosm.gov.my
Domashova, J., & Mikhailina, N. (2021). Usage of machine learning methods for early detection of money laundering schemes. Procedia Computer Science, 190, 184–192. https://doi.org/10.1016/j.procs.2021.06.033
Domingos, P., & Pazzani, M. (1997). On the optimality of the simple Bayesian classifier under zero-one loss. Machine Learning, 29, 103–130. https://doi.org/10.1023/A:1007413511361
Fallahi, F., Pourtaghi, H., & Rodríguez, G. (2012). The unemployment rate, unemployment volatility, and crime. International Journal of Social Economics, 39(6), 440–448. https://doi.org/10.1108/03068291211224937
Fernández-Delgado, M., Cernadas, E., Barro, S., & Amorim, D. (2014). Do we need hundreds of classifiers to solve real-world classification problems? Journal of Machine Learning Research, 15, 3133–3181.
FiroozAbadi, S. R., Razmi, M. J., & Bahmani, M. N. D. (2015). Examining the effective social and economic factors on underground economy. Mediterranean Journal of Social Sciences, 6(3), 525–534. https://doi.org/10.5901/mjss.2015.v6n3p525
Ghulam, Y., & Szalay, B. (2024). Investigating the determinants of money laundering risk. Journal of Money Laundering Control, 27(1), 139–157. https://doi.org/10.1108/JMLC-01-2023-0001
Gupta, A., Dwivedi, D. N., & Jain, A. (2022). Threshold fine-tuning of money laundering scenarios through multi-dimensional optimization techniques. Journal of Money Laundering Control, 25(1), 72–78. https://doi.org/10.1108/JMLC-12-2020-0138
Harris, D. A., Pyndiura, K. L., Sturrock, S. L., & Christensen, R. A. G. (2021). Using real-world transaction data to identify money laundering: Leveraging traditional regression and machine learning techniques. STEM Fellowship Journal, 7(1), 21–32. https://doi.org/10.17975/sfj-2021-006
He, H., & Garcia, E. A. (2009). Learning from imbalanced data. IEEE Transactions on Knowledge and Data Engineering, 21(9), 1263–1284. https://doi.org/10.1109/TKDE.2008.239
Hendriyetty, N., & Grewal, B. S. (2017). Macroeconomics of money laundering: Effects and measurements. Journal of Financial Crime, 24(1), 65–81. https://doi.org/10.1108/JFC-01-2016-0004
Huong, H., Nguyen, X., Dang, T. K., & Tran-Truong, P. T. (2024). Money laundering detection using a transaction-based graph learning approach [Paper presentation]. 2024 18th International Conference on Ubiquitous Information Management and Communication (IMCOM), 1–8. https://doi.org/10.1109/IMCOM60618.2024.10418307
Jarmulska, B. (2022). Random forest versus logit models: Which offers better early warning of fiscal stress? Journal of Forecasting, 41(3), 455–490. https://doi.org/10.1002/for.2806
Jayasekara, S. D. (2023). Trade-based money laundering and informal remittance services: Implications on the sustainability of the balance of payments of a small open economy. Journal of Money Laundering Control, 26(4), 877–891. https://doi.org/10.1108/JMLC-03-2022-0042
Jensen, R., & Iosifidis, A. (2022). Fighting money laundering with statistics and machine learning. IEEE Access, 11, 8889–8903. https://doi.org/10.1109/ACCESS.2023.3239549
Jin, M., & Wang, Z. (2021). Research on the relationships between FATF assessment effectiveness, compliance and money laundering risk [Paper presentation]. 2021 International Conference on Computer, Blockchain and Financial Development (CBFD), 533–536. https://doi.org/10.1109/CBFD52659.2021.00113
Jullum, M., Løland, A., Huseby, R. B., Ånonsen, G., & Lorentzen, J. (2020). Detecting money laundering transactions with machine learning. Journal of Money Laundering Control, 23(1), 173–186. https://doi.org/10.1108/JMLC-07-2019-0055
Karim, M., Hermsen, F., Chala, S., De Perthuis, P., & Mandal, A. (2024). Scalable semi-supervised graph learning techniques for anti money laundering. IEEE Access, 12, 50012–50029. https://doi.org/10.1109/ACCESS.2024.3383784
Ketenci, U. G., Kurt, T., Onal, S., Erbil, C., Akturkoglu, S., & Ilhan, H. S. (2021). A time-frequency based suspicious activity detection for antimoney laundering. IEEE Access, 9, 59957–59967. https://doi.org/10.1109/ACCESS.2021.3072114
Khan, A., & Akhtar, N. (2022). A critical appraisal of tax evasion as predicate offence for money laundering. Pakistan Journal of Multidisciplinary Innovation, 1(1), 37–44. https://doi.org/10.59075/pjmi.v1i1.32
Khelil, I., Khlif, H., & Achek, I. (2024). The economic consequences of money laundering: A review of empirical literature. Journal of Money Laundering Control, 27(5), 901–916. https://doi.org/10.1108/JMLC-09-2023-0143
Kumar, A., Das, S., Tyagi, V., Shaw, R. N., & Ghosh, A. (2021). Analysis of classifier algorithms to detect anti-money laundering. In J. C. Bansal, M. Paprzycki, M. Bianchini, & S. Das (Eds.), Computationally intelligent systems and their applications (Vol. 950, pp. 127–140). Studies in Computational Intelligence. Springer. https://doi.org/10.1007/978-981-16-0407-2_11
Kute, D. V., Pradhan, B., Shukla, N., & Alamri, A. (2021). Deep learning and explainable artificial intelligence techniques applied for detecting money laundering: A critical review. IEEE Access, 9, 82300–82317. https://doi.org/10.1109/ACCESS.2021.3086230
Labanca, D., Primerano, L., Markland-Montgomery, M., Polino, M., Carminati, M., & Zanero, S. (2022). Amaretto: An active learning framework for money laundering detection. IEEE Access, 10, 41720–41739. https://doi.org/10.1109/ACCESS.2022.3167699
Liu, P., & Chen, T. (2024). A genetic algorithm-based method for detecting intermediate accounts in money laundering through analysis of temporal graphs [Paper presentation]. 9th International Conference on Intelligent Computing and Signal Processing (ICSP), 263–269. https://doi.org/10.1109/ICSP62122.2024.10743874
Lokanan, M. E. (2019). Data mining for statistical analysis of money laundering transactions. Journal of Money Laundering Control, 22(4), 753–763. https://doi.org/10.1108/JMLC-03-2019-0024
Lokanan, M. E. (2024a). Predicting money laundering sanctions using machine learning algorithms and artificial neural networks. Applied Economics Letters, 31(12), 1112–1118. https://doi.org/10.1080/13504851.2023.2176435
Lokanan, M. E. (2024b). Predicting money laundering using machine learning and artificial neural networks algorithms in banks. Journal of Applied Security Research, 19(1), 20–44. https://doi.org/10.1080/19361610.2022.2114744
Mahmud, A. L. B. & Ismail, A. H. (2023). The effectiveness of anti-money laundering regulations of Malaysian commercial banks. International Journal of Academic Research in Accounting Finance and Management Sciences, 13(1), 104–114. https://doi.org/10.6007/IJARAFMS/v13-i1/15853
Masum, M. H., Banik, A., Hasan, M. T., Zolkaflil, S., Mustapha Naz, S. N. F. S., Razali, F. M., & Tarmizi, M. A. (2024). Anti-money laundering and emerging economy: Evidence from Bangladesh. Journal of Infrastructure, Policy and Development, 8(6), 3720. https://doi.org/10.24294/jipd.v8i6.3720
Mohan, A., Karthika, P.V., Sankar, P., Maya Manohar, K., & Peter, A. (2023). Improving anti-money laundering in bitcoin using evolving graph convolutions and deep neural decision forest. Data Technologies and Applications, 57(3), 313–329. https://doi.org/10.1108/DTA-06-2021-0167
Nazar, S., Raheman, A., & Anwar ul Haq, M. (2024). The magnitude and consequences of money laundering. Journal of Money Laundering Control, 27(5), 808–824. https://doi.org/10.1108/JMLC-09-2022-0139
Nazri, S. N. F. S. M., Zolkaflil, S., & Omar, N. (2019). Mitigating financial leakages through effective money laundering investigation. Managerial Auditing Journal, 34(2), 189–207. https://doi.org/10.1108/MAJ-03-2018-1830
Nguyen, L. C. (2013). The growing threat of money laundering to Vietnam: The necessary of intensive countermeasures. Journal of Money Laundering Control, 16(4), 321–332. https://doi.org/10.1108/JMLC-05-2013-0014
Phillips, J., & Land, K. C. (2012). The link between unemployment and crime rate fluctuations: An analysis at the county, state, and national levels. Social Science Research, 41(3), 681–694. https://doi.org/10.1016/j.ssresearch.2012.01.001
Phyu, T. H., & Uttama, S. (2024). Enhancing money laundering detection addressing imbalanced data and leveraging typological features analysis [Paper presentation]. 21st International Joint Conference on Computer Science and Software Engineering (JCSSE), 330–336. https://doi.org/10.1109/JCSSE61278.2024.10613724
Reza, F., Islam, M. S., & Anando, K. S. (2024). Money laundering hinders the growth of the economy of a country. Saudi Journal of Economics and Finance, 8(01), 9–13. https://doi.org/10.36348/sjef.2024.v08i01.002
Rider, B. (2019). Editorial. Journal of Financial Crime, 26(1), 2–4. https://doi.org/10.1108/JFC-09-2018-0103
Sairin, N., Salisi, M., & Bujang, I. (2020). Determining macroeconomic factor of financial distress in Malaysia. Malaysian Journal of Business and Economics, 2, 29–36. https://doi.org/10.51200/mjbe.v0i0.2107
Sarigul, H. (2013). Money laundering and abuse of the financial system. The International Journal of Business and Management, 2(5), 51–65.
Schlenther, B. (2013). The taxing business of money laundering: South Africa. Journal of Money Laundering Control, 16(2), 126–141. https://doi.org/10.1108/13685201311318485
Schneider, F. (2017). The dark side: Crime has gone global. In A closer look at globalization: The positive facets and the dark faces of a complex notion (Trilogue Salzburg 2017) (pp. 72–101). Bertelsmann Stiftung.
Schneider, F., & Windischbauer, U. (2008). Money laundering: Some facts. European Journal of Law and Economics, 26(3), 387–404. https://doi.org/10.1007/s10657-008-9070-x
Shah, I. H., & Aish, K. (2022). A nexus between corruption, money laundering (ML) and inflation: Evidence from South Asian countries. Journal of Money Laundering Control, 25(4), 730–741. https://doi.org/10.1108/JMLC-09-2021-0096
Shen, S., & Dong, X. (2019). The structural relationship between Chinese money supply and inflation based on VAR Model. Applied Mathematics, 10(07), 578–587. https://doi.org/10.4236/am.2019.107041
Sintayehu, K., & Seid, H. (2023). Developing anti money laundering identification using machine learning techniques. Irish Interdisciplinary Journal of Science & Research, 07(01), 64–74. https://doi.org/10.46759/IIJSR.2023.7110
Summe, K. A. (2000). The battle against money laundering: An examination of US Law, international cooperative efforts and corporate governance issues. Journal of Money Laundering Control, 3(3), 236–244. https://doi.org/10.1108/eb027235
Tanzi, V. (1997). Macroeconomic implications of money laundering. In E. U. Savona (Ed.), Responding to money laundering: International perspectives (pp. 91–104). Harwood Academic Publishers.
Tasan, M. P., Balasingam, S., Suppiah, K., & Arumugam, D. (2023). Determinants of money laundering: a study among commercial banks in Malaysia. E3S Web of Conferences 389, 09032. https://doi.org/10.1051/e3sconf/202338909032
Tiwari, M., Gepp, A., & Kumar, K. (2020). A review of money laundering literature: The state of research in key areas. Pacific Accounting Review, 32(2), 271–303. https://doi.org/10.1108/PAR-06-2019-0065
Tran, Q. P. (2024). Is remittance cost a driver of trade misinvoicing? A case study of Vietnam. Journal of Economics and Development, 26(4), 362–382. https://doi.org/10.1108/JED-04-2024-0158
UNODC. (n.d.). United Nations Convention Against Illicit Traffic in Narcotic Drugs and Psychotropic Substances (Vienna Convention). United Nations Office on Drugs and Crime. Retrieved 4 May 2025, from https://www.unodc.org
Vagaská, A., Gombár, M., & Korauš, A. (2022). Mathematical modeling and nonlinear optimization in determining the minimum risk of legalization of income from criminal activities in the context of EU member countries. Mathematics, 10(24), 4681. https://doi.org/10.3390/math10244681
Walker, J., & Unger, B. (2009). Measuring global money laundering: The Walker gravity model. Review of Law & Economics, 5(2), 821–853. https://doi.org/10.2202/1555-5879.1418
Yang, G., Liu, X., & Li, B. (2023). Anti-money laundering supervision by intelligent algorithm. Computers & Security, 132, 103344. https://doi.org/10.1016/j.cose.2023.103344
Yasaka, N. (2017). Data mining in anti-money laundering field. Journal of Money Laundering Control, 20(3), 301–310. https://doi.org/10.1108/JMLC-09-2015-0041
Yusoff, Y. H., Mohamad Azhar, A. S., Rafidi, F. I., Yunus, N., Azlan, N. J., & Yusop, R. (2023). Money laundering: Factors leading to money laundering in gold investment company in Malaysia. International Journal of Academic Research
in Business and Social Sciences, 13(5), 940–947. https://doi.org/10.6007/IJARBSS/v13-i5/16599
Zhang, G., Gao, Z., Dong, J., & Mei, D. (2023). Machine learning approaches for constructing the national anti-money laundering index. Finance Research Letters, 52, 103568. https://doi.org/10.1016/j.frl.2022.103568
Zhang, Y., & Trubey, P. (2019). Machine learning and sampling scheme: An empirical study of money laundering detection. Computational Economics, 54(3), 1043–1063. https://doi.org/10.1007/s10614-018-9864-z