ارائه رویکردی مبتنی بر یادگیری عمیق برای کشف کلاه‌برداری در سرویس‌های پرداخت مالی

نوع مقاله: مقاله پژوهشی

نویسندگان

1 استادیار گروه سیستم‌های اطلاعاتی، پژوهشکده فناوری اطلاعات، پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک)، تهران، ایران

2 استادیار گروه مدیریت فناوری اطلاعات، پژوهشکده فناوری اطلاعات، پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک)، تهران، ایران

چکیده

کشف خودکار کلاه‌برداری در سرویس‌های پرداخت مالی یکی از موضوعاتی است که با توجه به استفاده روزافزون از این نوع سرویس‌ها و افزایش حجم نقل و انتقالات مالی انجام‌شده در سیستم‌های بانکی از اهمیت بالایی برخوردار گشته است. بدین منظور نیازمند سیستمی هوشمند هستیم که بتواند با استفاده از ویژگی‌های مختلف یک تراکنش مالی، قانونی یا غیرقانونی بودن آن را به‌صورت بلادرنگ و با دقت قابل قبولی تشخیص دهد. برای طراحی چنین سیستمی در این مقاله از الگوریتمی مبتنی بر یادگیری عمیق بهره گرفته می‌شود. پس از تشریح الگوریتم پیشنهادی، کارایی آن با استفاده از یک مجموعه از تراکنش‌های مالی واقعی ارزیابی می‌گردد که به‌عنوان مجموعه داده معیار در پیشینه پژوهش شناخته می‌شود. سپس با استفاده از ملاک‌هایی نظیر صحت، دقت، معیار F، حساسیت و منحنی دقت- یادآوری مقایسه‌ای بین الگوریتم پیشنهادی با دو الگوریتم نزدیک‌ترین همسایگی و ماشین بردار پشتیبان صورت می‌گیرد. نتایج محاسباتی ضمن تائید کارایی الگوریتم پیشنهادی در مجموعه داده معیار، حاکی از صحت 96 درصدی و دقت 98 درصدی آن است.

کلیدواژه‌ها


عنوان مقاله [English]

A Deep Learning Approach to Fraud Detection in Financial Payment Services

نویسندگان [English]

  • Amir Hossein Seddighi 1
  • Arman Sajedinejad 2
1 Assistant Prof., Information Technology Research Department, Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran
2 Assistant Prof., Information Technology Research Department, Iranian Research Institute for Information Science and Technology (IranDoc), Tehran, Iran
چکیده [English]

Widespread use of financial payment services besides the increased volume of financial transfers carried out in banking systems, have resulted in an important growing trend of automatic fraud detection. In this regard, an intelligent system is needed that can determine the fraudulence or genuineness of a financial transaction in real-time, unquestionably with an acceptable precision using the different transaction features. In order to gain the benefits of the system, a deep learning algorithm is described and proposed in this paper. The performance of the proposed algorithm is evaluated using a set of real-world financial transactions, which is known as the standard dataset in the literature. Then, the proposed algorithm is compared with k-nearest neighbors and support vector machine algorithms using different metrics such as accuracy, precision, F-measure, sensitivity, and precision-recall curve. The computational results confirmed the efficiency of the proposed algorithm on the standard dataset with 96% accuracy and 98% precision.

کلیدواژه‌ها [English]

  • Artificial intelligence
  • Deep Learning
  • fraud detection
  • Information technology
  • Machine Learning
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