Classification of Suspicious Financial Transactions using Light Gradient Boosting Machine Method (LGBM) based on Social Network Analysis (SNA) Indicators

Ayu Fara Paramitha, Yuti Dewita Arimbi, Slamet Riyanto, Niken Fitria Apriani, Al Hafiz Akbar Maulana Siagian


Money laundering is an act committed by individuals or a group to conceal or disguise the origin of wealth obtained from illegal activities into assets that appear to have been acquired through legal means. Generally, there are three money laundering processes: placement, layering, and integration. The complexity of these money laundering processes described above makes it difficult to trace suspicious financial transactions and identify the parties involved and which transactions are connected to the suspected money laundering network. To address this issue, Social Network Analysis (SNA) is implemented to generate SNA features. In the following stage, these SNA features are employed as indicators to detect suspicious financial activities. The gathered indicator data is utilized to build a classification model using the Light Gradient-Boosting Machine (LGBM) approach. The results of this study show that the model created using SNA and LGBM methods achieved an accuracy of 97%. The precision, recall, and F1-Score values for non-suspicious transaction data were 98%, 97%, and 97%, respectively, while for suspicious transaction data, they were 97%, 98%, and 97%, respectively. The achieved accuracy values were quite high indicating that the used approach was capable of effectively classifying suspicious financial activities. We believe that the findings of this study could be an alternative method for detecting suspicious financial transactions in order to avoid money laundering operations.

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N. Nugroho, S. Sunarmi, M. Siregar, and R. Munthe, “Analisis terhadap Pencegahan Tindak Pidana Pencucian Uang oleh Bank Negara Indonesia,” ARBITER: Jurnal Ilmiah Magister Hukum, vol. 2, no. 1, pp. 100–110, May 2020, doi: 10.31289/arbiter.v2i1.126.

Indonesia, Pemerintah Pusat, “Undang-undang (UU) Nomor 15 Tahun 2002 tentang Tindak Pidana Pencucian Uang.” Apr. 17, 2002. Accessed: Jul. 18, 2023. [Online]. Available:

A. R. Handoko, “Perancangan Sistem Pakar Analisa Transaksi Keuangan Mencurigakan Menggunakan Metode Forward Chaining,” Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer, vol. 10, no. 2, pp. 701–712, Nov. 2019, doi: 10.24176/simet.v10i2.3523.

M. Mahootiha, A. H. Golpayegani, and B. Sadeghian, “Designing a New Method for Detecting Money Laundering based on Social Network Analysis,” in 2021 26th International Computer Conference, Computer Society of Iran (CSICC), Mar. 2021, pp. 1–7. doi: 10.1109/CSICC52343.2021.9420621.

R. M. Aziz, M. F. Baluch, S. Patel, and P. Kumar, “A Machine Learning based Approach to Detect the Ethereum Fraud Transactions with Limited Attributes,” Karbala International Journal of Modern Science, vol. 8, no. 2, pp. 139–151, May 2022, doi: 10.33640/2405-609X.3229.

M. Massaoudi, S. S. Refaat, I. Chihi, M. Trabelsi, F. S. Oueslati, and H. Abu-Rub, “A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for Short-Term Load Forecasting,” Energy, vol. 214, p. 118874, Jan. 2021, doi: 10.1016/

A. K. Shaikh, M. Al-Shamli, and A. Nazir, “Designing a Relational Model to Identify Relationships between Suspicious Customers in Anti-Money Laundering (AML) Using Social Network Analysis (SNA),” Journal of Big Data, vol. 8, no. 1, p. 20, Jan. 2021, doi: 10.1186/s40537-021-00411-3.

T. Zhao, X. Zhang, and S. Wang, “GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks,” in Proceedings of the 14th ACM International Conference on Web Search and Data Mining, in WSDM ’21. New York, NY, USA: Association for Computing Machinery, Mar. 2021, pp. 833–841. doi: 10.1145/3437963.3441720.

A. A. Taha and S. J. Malebary, “An Intelligent Approach to Credit Card Fraud Detection Using an Optimized Light Gradient Boosting Machine,” IEEE Access, vol. 8, pp. 25579–25587, Jan. 2020, doi: 10.1109/ACCESS.2020.2971354.

N. R. Al-Molhem, Y. Rahal, and M. Dakkak, “Social network analysis in Telecom data,” Journal of Big Data, vol. 6, no. 1, p. 99, Nov. 2019, doi: 10.1186/s40537-019-0264-6.

M. K. Anam, T. P. Lestari, M. B. Firdaus, and S. Fadli, “Analisis Kesiapan Masyarakat Pada Penerapan Smart City di Sosial Media Menggunakan SNA | Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi),” Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi), vol. 5, no. 1, pp. 69–81, Feb. 2021, doi: 10.29207/resti.v5i1.2742.

Eriyanto, Analisis Jaringan Media Sosial: Dasar-dasar dan Aplikasi Metode Jaringan Sosial untuk Membedah Percakapan di Media Sosial. Jakarta: Kencana (Prenadamedia Group), 2021.

J. M. P. Sanchez, B. A. Alejandro, M. M. J. Olvido, and I. M. V. Alejandro, “An Analysis of Online Classes Tweets Using Gephi: Inputs for Online Learning,” International Journal of Information and Education Technology, vol. 11, no. 12, pp. 583–589, 2021, doi: 10.18178/ijiet.2021.11.12.1568.

Y. Ju, G. Sun, Q. Chen, M. Zhang, H. Zhu, and M. U. Rehman, “A Model Combining Convolutional Neural Network and LightGBM Algorithm for Ultra-Short-Term Wind Power Forecasting,” IEEE Access, vol. 7, pp. 28309–28318, 2019, doi: 10.1109/ACCESS.2019.2901920.

J. Fan, X. Ma, L. Wu, F. Zhang, X. Yu, and W. Zeng, “Light Gradient Boosting Machine: An efficient soft computing model for estimating daily reference evapotranspiration with local and external meteorological data,” Agricultural Water Management, vol. 225, p. 105758, Nov. 2019, doi: 10.1016/j.agwat.2019.105758.

I. Alarab, S. Prakoonwit, and M. I. Nacer, “Competence of Graph Convolutional Networks for Anti-Money Laundering in Bitcoin Blockchain,” in Proceedings of the 2020 5th International Conference on Machine Learning Technologies, in ICMLT ’20. New York, NY, USA: Association for Computing Machinery, Jul. 2020, pp. 23–27. doi: 10.1145/3409073.3409080.

J. Alotibi, B. Almutanni, T. Alsubait, H. Alhakami, and A. Baz, “Money Laundering Detection using Machine Learning and Deep Learning,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 13, no. 10, Art. no. 10, 46/31 2022, doi: 10.14569/IJACSA.2022.0131087.


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