The Determinants of Money-Laundering Behavior in Indonesia

Authors

  • Vita Fauziah Ningtyas Brawijaya University
  • Munawar Ismail Universitas Brawijaya
  • Setyo Wahyudi Universitas Brawijaya

DOI:

https://doi.org/10.21776/ub.jiae.2022.010.01.2

Keywords:

money laundering

Abstract

Banks nowadays have come with innovations in all of their operations, particularly in account application. Therefore, people can apply for their account personally from any places by using the smartphone technology. However, it increases the possible risk of money laundering, especially in banking. One of the phenomena is the high number of Suspicious Financial Transaction Reports (LTKM) received by the Financial Transaction Reports and Analysis Center (INTRAC). Hence, this study analyzes the effect of Indonesia’s social demography on the risk of each individual of committing money laundering.Using secondary data obtained from INTRAC, the derived and selected independent variables was analyzed using ordinal logistic regression to determine their effect on the dependent variable, i.e. the Money Laundering Risk Score (MLRS). This study finds that the social demographic factors affecting the risk of money laundering committed by individuals are sex, age, marital status, occupation, and the history of financial service usage, in which occupation has the highest effect.

References

Ansong, A., & Gyensare, M. A. (2012). Determinants of University Working-Students’ Financial Literacy at the University of Cape Coast, Ghana. International Journal of Business and Management, 7(9), 126–133. https://doi.org/10.5539/ijbm.v7n9p126

Ba, H., & Huynh, T. (2018). Money laundering risk from emerging markets: the case of Vietnam. Journal of Money Laundering Control, 21(3), 385–401. https://doi.org/10.1108/JMLC-09-2017-0050

Bambang Poernomo. (1992). Asas-asas Hukum Pidana. Ghalia Indonesia.

FATF. (2012). Specific Risk Factors in Laundering the Proceeds of Corruption: Assistance to Reporting Institutions. June. www.fatf-gafi.org

FATF. (2013). National Money Laundering and Terrorist Financing Risk Assessment. February.

IMF. (2011). The International Monetary Fund Staffs ML/FT NRA Methodology (Issue Ml).

Indonesia, R. (1974). Undang-Undang Tentang Perkawinan. 2.

Isa, Y. M., Sanusi, Z. M., Haniff, M. N., & Barnes, P. A. (2015). Money Laundering Risk: From the Bankers’ and Regulators Perspectives. Procedia Economics and Finance, 28(April), 7–13. https://doi.org/10.1016/s2212-5671(15)01075-8

Otoritas Jasa Keuangan. (2017). PENILAIAN RISIKO TINDAK PIDANA PENCUCIAN UANG Pada Sektor Jasa Keuangan Tahun 2017.

Otoritas Jasa Keuangan dan Pusat Pelaporan dan Analisis Transaksi Keuangan. (2019). Penilaian Resiko Tindak Pidana Pencucian Uang (TPPU) dan Tindak pidana Pendanaan Terorisme (TPPT) pada sektor jasa keuangan (Vol. 4, Issue 1). Otoritas Jasa Keuangan.

INTRAC. (2017). INDEKS PERSEPSI PUBLIK INDONESIA TERHADAP TINDAK PIDANA PENCUCIAN UANG & TINDAK PIDANA PENDANAAN TERRORISME.

INTRAC. (2019). Indeks Persepsi Publik Anti Pencucian Uang dan Pencegahan Pendanaan Terorisme di Indonesia. In Jakarta (Vol. 53, Issue 9).

INTRAC. (2021). Buletin Statistik Anti Pencucian Uang dan Pencegahan Pendanaan Terorisme. 131.

Price, C. (2008). Customer Risk Assesment. Metavante White Paper, 8.

Reganati, F., & Oliva, M. (2018). Determinants of money laundering: evidence from Italian regions. Journal of Money Laundering Control, 21(3), 402–413. https://doi.org/10.1108/JMLC-09-2017-0052

Ristanti, Y. D., & Handoyo, E. (2017). Undang-Undang Otonomi Daerah Dan Pembangunan Ekonomi Daerah. Jurnal Riset Akutansi Keuangan, 2(2), 115–122.

ROSADIANTO, E. C. (2016). PENGARUH MARITAL STATUS TERHADAP KEDISIPLINAN KERJA DAN PRODUKTIVITAS KERJA (Studi Kasus Pada Pemerintah Daerah Kabupaten Sragen).

Ross, S., & Hannan, M. (2007). Money laundering regulation and risk‐based decision‐making. Journal of Money Laundering Control, 10(1), 106–115. https://doi.org/10.1108/13685200710721890

Tanya, B. L. (2010). Pencucian Uang (Money Laundering) Dan Dampaknya Dalam Pembangunan Ekonomi. 1–30.

Tran, Huynh H B, B.-H. N. (2018). Measuring The Money Laundering Risk From Individual Customers And Its Determinants - The Case Of Vietnamese Commercial Banks. Journal of Money Laundering Control, 21(2008). https://www.researchgate.net/publication

/325950954_Money_laundering_risk_from_emerging_markets_the_case_of_Vietnam

Undang-Undang Republik Indonesia Nomor 8 Tahun 2010, 1 (2010).

Usman Kemal, M. (2014). Anti-money laundering regulations and its effectiveness. Journal of Money Laundering Control, 17(4), 416–427. https://doi.org/10.1108/JMLC-06-2013-0022

Vaithilingam, S., & Nair, M. (2007). Factors affecting money laundering: lesson for developing countries. Journal of Money Laundering Control, 10(3), 352–366. https://doi.org/10.1108/13685200710763506

Waluyo, E. (2009). Upaya Memerangi Tindakan Pencucian Uang (Money Laundring) Di Indonesia. Jurnal Dinamika Hukum, 9(3), 237–246. https://doi.org/10.20884/1.jdh.2009.9.3.235

Downloads

Published

2022-02-22