استفاده از مصورسازی داده ها برای تشخیص فعالیت مشکوک
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استفاده از مصورسازی داده ها برای تشخیص فعالیت مشکوک

عنوان فارسی مقاله: مبارزه با پولشویی: استفاده از مصورسازی داده ها برای تشخیص فعالیت مشکوک
عنوان انگلیسی مقاله: Anti-Money Laundering: Using data visualization to identify suspicious activity
مجله/کنفرانس: مجله بین المللی سیستم های اطلاعات حسابداری - International Journal Of Accounting Information Systems
رشته های تحصیلی مرتبط: حسابداری، اقتصاد
گرایش های تحصیلی مرتبط: حسابداری مالی، اقتصاد مالی، اقتصاد پولی
کلمات کلیدی فارسی: مبارزه با پولشویی، مصورسازی، تجزیه و تحلیل لینک، علائم پولشویی
کلمات کلیدی انگلیسی: Anti-Money Laundering، visualization، link analysis، money laundering symptoms
نوع نگارش مقاله: مقاله پژوهشی (Research Article)
نمایه: Scopus - Master Journals List - JCR
شناسه دیجیتال (DOI): https://doi.org/10.1016/j.accinf.2019.06.001
دانشگاه: Accounting Data Analytics, Central Queensland University, Australia
ناشر: الزویر - Elsevier
نوع ارائه مقاله: ژورنال
نوع مقاله: ISI
سال انتشار مقاله: 2019
ایمپکت فاکتور: 2/645 در سال 2018
شاخص H_index: 44 در سال 2019
شاخص SJR: 0/478 در سال 2018
شناسه ISSN: 1467-0895
شاخص Quartile (چارک): Q2 در سال 2018
فرمت مقاله انگلیسی: PDF
تعداد صفحات مقاله انگلیسی: 18
وضعیت ترجمه: ترجمه نشده است
قیمت مقاله انگلیسی: رایگان
آیا این مقاله بیس است: خیر
آیا این مقاله مدل مفهومی دارد: ندارد
آیا این مقاله پرسشنامه دارد: ندارد
آیا این مقاله متغیر دارد: ندارد
کد محصول: E13090
رفرنس: دارای رفرنس در داخل متن و انتهای مقاله
فهرست انگلیسی مطالب

Abstract


1- Introduction


2- Background and related work


3- Data visualization methodology


4- ‘Proof of concept’ and validation


5- Discussion, contribution and limitations


6- Conclusion Statement of authorship


References

نمونه متن انگلیسی مقاله

Abstract


Annually, money laundering activities threaten the global economy. Proceeds of these activities may be used to fund further criminal activities and to undermine the integrity of financial systems worldwide. For these reasons, money laundering is recognized as a critical risk in many countries. There is an emerging interest from both researchers and practitioners concerning the use of software tools to enhance detection of money laundering activities. In the current economic environment, regulators struggle to stay ahead of the latest scam, and financial institutions are challenged to ensure that they can identify and stop criminal activities, while ensuring that legitimate customers are served more effectively and efficiently. Effective technological solutions are an essential element in the fight against money laundering. Improved data and analytics are key in assisting investigators to focus on suspicious activities. Continually evolving regulations, together with recent instances of money laundering violations by some of the largest financial institutions, have highlighted the need for better technology in managing anti-money laundering activities. This study explores the use of visualization techniques that may assist in efficient identification of patterns of money laundering activities. It demonstrates how link analysis may be applied in detecting suspicious bank transactions. A prototype application (AML2ink) is used for proof-of-concept purposes.


Introduction


Money laundering is the process by which criminals attempt to disguise illicit assets as legitimate assets that they have a right to possess and spend (AUSTRAC, 2011). Operations are designed to take the proceeds of illegal activity, such as profits from drug trafficking, and cause them to appear to come from legitimate sources. Once illegal money has been laundered, the perpetrator is able to spend or invest the illicit income in legitimate assets. Money laundering threatens the prosperity of the global economy, undermines the integrity of financial systems and funds further criminal activity which impacts on community safety and wellbeing (ACFE, 2016). Money laundering is a big business, however, since it is illegal and falls outside the realm of official economic statistics, any estimate is based on a combination of experience, extrapolation, and intuition. The International Monetary Fund (IMF) estimates that the aggregate level of money laundering is between 2 and 5% of the world's annual gross domestic product or approximately 1.5 trillion US dollars (FATF, 2014). In Australia this figure amounts to approximately $10–$15 billion per annum (AUSTRAC, 2011). However, the aforementioned estimates should be treated with caution. They are intended to give an estimate of the magnitude of money laundering only.

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