Usage of machine learning methods for early detection of money laundering schemes

Domashova, Jenny V.; Mikhailina, Natalia

doi:10.1016/j.procs.2021.06.033

Cited by 18 publications

(6 citation statements)

References 3 publications

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“…AI technology is also being extensively used in voice-assisted customer support, detecting patterns in anti-money laundering, prevention, and detection of frauds well in time. As per the current study, the authors conclude that the two factors contributing to the new AI trends, especially in the banking sector, are societal and personal factors [33].…”

Section: Customer Orientated Servicessupporting

confidence: 72%

Is artificial intelligence and machine learning changing the ways of banking: a systematic literature review and meta analysis

Kalyani,

Gupta

2023

Discov Artif Intell

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This study aims to analyze the research work and present an SLR (Systematic Literature Review) on the initiatives taken by players in the banking sector, especially toward the technological innovations done in the field of AI (Artificial Intelligence) and ML (Machine Learning) that have contributed to the growth of the sector as well towards the ease of doing and building trust amongst consumers in the digitalized era of Banking. Growing security risks in cyberspace accompany the enormous and complex digital versions of virtual services. In total, 734 articles were reviewed to explore how Artificial Intelligence and Machine Learning have been used in various banking facets and how they have evolved to cater to different banking sector requirements. The findings reveal the areas where these modern-age technologies have been adopted and where the same can be used efficiently. Lastly, this study elaborates and evaluates the applications of these technologies on the parameters of scalability going further, keeping in mind the unprecedented dynamic times, and serving the very purpose of banks and how the banks are dealing with all the opportunities and challenges these new technologies bring in. This SLR also provides an overview of the research that can be taken up in future by the researchers in this domain.

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Section: Customer Orientated Servicessupporting

confidence: 72%

Is artificial intelligence and machine learning changing the ways of banking: a systematic literature review and meta analysis

Kalyani,

Gupta

2023

Discov Artif Intell

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“…According to the Davies-Bouldin index, K-means perform slightly better than K-medoids. Also, in [45], the authors proposed a novel machine learning method for money laundering based on the Python programming language. Detecting money laundering using neural networks in order to stop terrorism is suggested in [46].…”

Section: Electronic Banking Systemsmentioning

confidence: 99%

Maximum Entropy Principle Based on Bank Customer Account Validation Using the Spark Method

Qiu,

Xu,

Shi

et al. 2023

Journal of Computer Networks and Communications

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Bank customer validation is carried out with the aim of providing a series of services to users of a bank and financial institutions. It is necessary to perform various analytical methods for user’s accounts due to the high volume of banking data. This research works in the field of money laundering detection from real bank data. Banking data analysis is a complex process that involves information gathered from various sources, mainly in terms of personality, such as bills or bank account transactions which have qualitative characteristics such as the testimony of eyewitnesses. Operational or research activities can be greatly improved if supported by proprietary techniques and tools, due to the vast nature of this information. The application of data mining operations with the aim of discovering new knowledge of banking data with an intelligent approach is considered in this research. The approach of this research is to use the spiking neural network (SNN) with a group of sparks to detect money laundering, but due to the weakness in accurately identifying the characteristics of money laundering, the maximum entropy principle (MEP) method is also used. This approach will have a mapping from clustering and feature extraction to classification for accurate detection. Based on the analysis and simulation, it is observed that the proposed approach SNN-MFP has 87% accuracy and is 84.71% more functional than the classical method of using only the SNN. In this analysis, it is observed that in real banking data from Mellat Bank, Iran, in its third and fourth data, with a comprehensive analysis and reaching different outputs, there have been two money laundering cases.

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“…Untuk mengurangi risiko penggunaan instrumen keuangan yang tidak sah, bank secara sistematis meningkatkan persyaratan untuk aktivitas dan prosesnya, dan memperkenalkan teknologi dan metode baru untuk menghilangkan risiko, terutama di bidang pemberantasan pencucian uang, di mana tingkat keamanan yang tinggi diperlukan (Domashova & Mikhailina, 2021). Selaras dengan Penelitian Tertychnyi et al (2022), bahwasanya diperlukan sistem pemantauan Anti pencucian uang yang berdasarkan teknik pembelajaran mesin, yang dianraranya terdiri dari tiga persyaratan: (i) menghasilkan peringatan yang akurat dan tidak berlebihan; (ii) menghasilkan peringatan tepat waktu; dan (iii) mengaitkan penjelasan dan perkiraan risiko untuk setiap peringatan.…”

Section: Tantangan Dalam Pencegahan Money Launderingunclassified

Mengungkap Labyrinth Kejahatan Keuangan: Pendalaman Praktik Money Laundering dan Dampaknya Pada Stabilitas Pasar Keuangan Internasional

Nurauhan rahmad

2023

BIREV

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Penelitian ini bertujuan untuk menjelaskan dan menganalisis praktik money laundering dalam konteks kejahatan keuangan serta mengidentifikasi dampaknya terhadap stabilitas pasar keuangan internasional. Penelitian ini menggunakan pendekatan kualitatif dengan menganalisis tinjauan literatur yang relevan. Melalui pendalaman praktik money laundering, penelitian ini mengungkap strategi dan metode yang digunakan oleh para pelaku kejahatan untuk menyamarkan asal usul dana yang diperoleh secara ilegal. Langkah-langkah dalam proses money laundering, seperti penempatan, pembelokan, dan integrasi dana ilegal, dianalisis secara mendalam untuk memahami taktik yang digunakan. Selain itu, penelitian ini menyoroti dampak yang ditimbulkan oleh money laundering pada stabilitas pasar keuangan internasional. Praktik money laundering dapat mengganggu integritas sistem keuangan global, merusak kepercayaan investor, dan menciptakan ketidakseimbangan dalam perekonomian global. Penelitian ini juga mengidentifikasi upaya pencegahan dan penegakan hukum yang harus dilakukan untuk melawan money laundering serta tantangan yang dihadapi dalam mengatasi kejahatan keuangan ini. Hasil penelitian ini memberikan wawasan mendalam tentang kompleksitas money laundering serta implikasinya pada pasar keuangan internasional. Diharapkan hasil penelitian ini dapat menjadi sumbangan penting dalam upaya pencegahan dan penanggulangan money laundering, serta meningkatkan kesadaran akan pentingnya menjaga integritas sistem keuangan global.

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Usage of machine learning methods for early detection of money laundering schemes

Cited by 18 publications

References 3 publications

Is artificial intelligence and machine learning changing the ways of banking: a systematic literature review and meta analysis

Is artificial intelligence and machine learning changing the ways of banking: a systematic literature review and meta analysis

Maximum Entropy Principle Based on Bank Customer Account Validation Using the Spark Method

Mengungkap Labyrinth Kejahatan Keuangan: Pendalaman Praktik Money Laundering dan Dampaknya Pada Stabilitas Pasar Keuangan Internasional

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