مروری بر روش های هوشمند داده‌کاوی جهت مبارزه با پولشویی و استقرار حاکمیت داده در شبکه بانکی
محورهای موضوعی : پردازش چند رسانه ای، سیستمهای ارتباطی، سیستمهای هوشمند
هدایت علیمرادی دوکوهی
1
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عرفانه نوروزی
2
1 - گروه مهندسی کامپیوتر، واحدقشم، دانشگاه آزاد اسلامی،قشم،ایران
2 - گروه مهندسی کامپیوتر، واحدقشم، دانشگاه آزاد اسلامی،قشم،ایران
کلید واژه: مبارزه با پولشویی, داده&rlm, کاوی, حاکمیت داده, تراکنش&rlm, &rlm, های بانکی, یادگیری ماشین,
چکیده مقاله :
با رشد خدمات الکترونیک در حوزه مالی و قدرت گرفتن درگاه‌های پرداخت غیر حضوری در دنیا، روش های مبارزه با پول شویی نیز رشد چشم گیری داشته است، به نحوی که امروزه با نرم افزار های هوشمند، ردگیری تراکنش های رمز ارزها نیز امکان پذیر می‌باشد. حاکمیت داده جامع یکی از روشهای سرعت بخشیدن به فرآیند مبارزه با پول شویی در شبکه بانکی است. در این مقاله برخی از چالش‌های پیاده سازی حاکمیت داده و داده کاوی در شبکه بانکی بمنظور مبارزه با پولشویی و همچنین روشهای یادگیری ماشین بانظارت، یادگیری ماشین بدون نظارت و روش های پیشرفته هوش مصنوعی یادگیری ماشین و کاستی های روشهای موجود و پیشنهاداتی برای پژوهشگران ارائه خواهد شد. از آنجایی که پولشویی همچنان یک مشکل رایج در اقتصاد جهانی امروزی است، دولت ها در سراسر جهان اقدامات سخت گیرانه ای برای جلوگیری از جرایم مالی اجرا مینمایند. این موضوع تقاضای فزاینده‌ای برای سیستمهای ضد پولشویی در صنایع مختلف از جمله بانکداری، بورس ، بیمه ، خدمات مالی و بازار سرمایه ایجاد کرده است.
Abstract
Introduction: Money laundering refers to the process of concealing the illicit origins of dirty money and presenting it as legitimate funds. Dirty money signifies sums obtained from criminal or illegal activities such as drug trafficking, human trafficking, bribery, and tax evasion. Money laundering can be defined as the process of cleansing dirty money. Detecting financial crimes has become a crucial priority for governments, and with the advancement of modern technologies and global communications, fraudulent methods are also significantly growing and evolving, leading to substantial damages to legitimate businesses. With the growth of electronic services in the financial sector and the increasing prevalence of non-face-to-face payment gateways worldwide, methods to combat money laundering have also experienced significant advancement. To the extent that nowadays, through intelligent software, tracking cryptocurrency transactions has become feasible.
Method: This study is a literature review that focuses on keywords such as Anti-Money Laundering, Money Laundering, suspicious transaction, machine learning, and Data Mining. The research involves searching for relevant articles related to these keywords, and suitable articles will be selected for further analysis.
Results: This study will present a classification based on approaches to combat money laundering in the electronic banking industry. Furthermore, it highlights the issues and challenges of these approaches in tackling money laundering within the electronic banking sector.
Discussion: This article presents a comprehensive analysis of the existing literature on combating money laundering, focusing on the use of machine learning, deep learning, data mining, and big data techniques. The reviews indicate that the lack of comprehensive data governance prevents the effective utilization of data from various institutions and centers for anti-money laundering purposes. Additionally, multiple graph algorithms, including centrality algorithms and embedded graph algorithms, have been employed to identify money laundering networks and organizations. Not utilizing these algorithms leaves a significant gap. Researchers can enhance the detection of illicit activities in anti-money laundering systems by employing community detection algorithms. Furthermore, the analyses reveal that unsupervised learning techniques can be more efficient in identifying money laundering due to the absence of labeled datasets and imbalanced data. Consequently, unsupervised methods can serve as a suitable tool for anti-money laundering systems.
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