مشخصات مقاله | |
ترجمه عنوان 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 |
انتشار | مقاله سال 2019 |
تعداد صفحات مقاله انگلیسی | 18 صفحه |
هزینه | دانلود مقاله انگلیسی رایگان میباشد. |
پایگاه داده | نشریه الزویر |
نوع نگارش مقاله |
مقاله پژوهشی (Research Article) |
مقاله بیس | این مقاله بیس نمیباشد |
نمایه (index) | Scopus – Master Journals List – JCR |
نوع مقاله | ISI |
فرمت مقاله انگلیسی | |
ایمپکت فاکتور(IF) |
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 |
مدل مفهومی | ندارد |
پرسشنامه | ندارد |
متغیر | ندارد |
رفرنس | دارد |
رشته های مرتبط | حسابداری، اقتصاد |
گرایش های مرتبط | حسابداری مالی، اقتصاد مالی، اقتصاد پولی |
نوع ارائه مقاله |
ژورنال |
مجله | مجله بین المللی سیستم های اطلاعات حسابداری – International Journal Of Accounting Information Systems |
دانشگاه | Accounting Data Analytics, Central Queensland University, Australia |
کلمات کلیدی | مبارزه با پولشویی، مصورسازی، تجزیه و تحلیل لینک، علائم پولشویی |
کلمات کلیدی انگلیسی | Anti-Money Laundering، visualization، link analysis، money laundering symptoms |
شناسه دیجیتال – doi |
https://doi.org/10.1016/j.accinf.2019.06.001 |
کد محصول | E13090 |
وضعیت ترجمه مقاله | ترجمه آماده این مقاله موجود نمیباشد. میتوانید از طریق دکمه پایین سفارش دهید. |
دانلود رایگان مقاله | دانلود رایگان مقاله انگلیسی |
سفارش ترجمه این مقاله | سفارش ترجمه این مقاله |
فهرست مطالب مقاله: |
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 |
بخشی از متن 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. |