Suspicious activity reporting using dynamic bayesian networks
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Yes
Document Type
Conference Paper
Publication Date
3-16-2011
Conference Name
World Conference on Information Technology (WCIT 2010)
Conference Location
6-10 October 2010
Conference Dates
Istanbul, Turkey
ISBN/ISSN
79952509664 (Scopus)
Volume
3
First Page
987
Last Page
991
Publisher
Elsevier Ltd.
Keywords
Anomaly detection, Anti-money-laundering, Clustering, Dynamic Bayesian network, Suspicious activity reporting, Suspicious financial transactions
Abstract / Description
Suspicious activity reporting has been a crucial part of anti-money laundering systems. Financial transactions are considered suspicious when they deviate from the regular behavior of their customers. Money launderers pay special attention to keep their transactions as normal as possible to disguise their illicit nature. This may deceive the classical deviation based statistical methods for finding anomalies. This study presents an approach, called SARDBN (Suspicious Activity Reporting using Dynamic Bayesian Network), that employs a combination of clustering and dynamic Bayesian network (DBN) to identify anomalies in sequence of transactions. SARDBN applies DBN to capture patterns in a customer's monthly transactional sequences as well as to compute an anomaly index called AIRE (Anomaly Index using Rank and Entropy). AIRE measures the degree of anomaly in a transaction and is compared against a pre-defined threshold to mark the transaction as normal or suspicious. The presented approach is tested on a real dataset of more than 8 million banking transactions and has shown promising results.
DOI
https://doi.org/10.1016/j.procs.2010.12.162
Recommended Citation
Raza, S., & Haider, S. (2011). Suspicious activity reporting using dynamic bayesian networks., 3, 987-991. https://doi.org/10.1016/j.procs.2010.12.162
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
