Suspicious activity reporting using dynamic bayesian networks

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Document Type

Conference Paper

Publication Date


Author Affiliation

  • Saleha Raza is Ph.D. Scholar at the Faculty of Computer Science, Institute of Business Administration, Karachi
  • Sajjad Haider is Associate Professor at Institute of Business Administration, Karachi

Conference Name

World Conference on Information Technology (WCIT 2010)

Conference Location

6-10 October 2010

Conference Dates

Istanbul, Turkey


79952509664 (Scopus)



First Page


Last Page



Elsevier Ltd.

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.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.

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