کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
425577 685780 2016 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
A survey of anomaly detection techniques in financial domain
ترجمه فارسی عنوان
بررسی تکنیک های تشخیص ناهنجاری در حوزه مالی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نظریه محاسباتی و ریاضیات
چکیده انگلیسی


• Recently, in the financial sector, there has been renewed interest in research on detection of fraudulent activities.
• This paper presents an in-depth survey of various clustering based anomaly detection techniques and compares them from different perspectives.
• In addition, we discuss the lack of real world data and how synthetic data has been used to validate current detection techniques.

Anomaly detection is an important data analysis task. It is used to identify interesting and emerging patterns, trends and anomalies from data. Anomaly detection is an important tool to detect abnormalities in many different domains including financial fraud detection, computer network intrusion, human behavioural analysis, gene expression analysis and many more. Recently, in the financial sector, there has been renewed interest in research on detection of fraudulent activities. There has been a lot of work in the area of clustering based unsupervised anomaly detection in the financial domain. This paper presents an in-depth survey of various clustering based anomaly detection technique and compares them from different perspectives. In addition, we discuss the lack of real world data and how synthetic data has been used to validate current detection techniques.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Future Generation Computer Systems - Volume 55, February 2016, Pages 278–288
نویسندگان
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