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To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model

To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model

Harmeet Kaur Khanuja, Dattatraya Adane
Copyright: © 2019 |Pages: 32
ISBN13: 9781522573562|ISBN10: 1522573569|ISBN13 Softcover: 9781522586043|EISBN13: 9781522573579
DOI: 10.4018/978-1-5225-7356-2.ch012
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MLA

Khanuja, Harmeet Kaur, and Dattatraya Adane. "To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model." Organizational Auditing and Assurance in the Digital Age, edited by Rui Pedro Marques, et al., IGI Global, 2019, pp. 224-255. https://doi.org/10.4018/978-1-5225-7356-2.ch012

APA

Khanuja, H. K. & Adane, D. (2019). To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model. In R. Marques, C. Santos, & H. Inácio (Eds.), Organizational Auditing and Assurance in the Digital Age (pp. 224-255). IGI Global. https://doi.org/10.4018/978-1-5225-7356-2.ch012

Chicago

Khanuja, Harmeet Kaur, and Dattatraya Adane. "To Monitor and Detect Suspicious Transactions in a Financial Transaction System Through Database Forensic Audit and Rule-Based Outlier Detection Model." In Organizational Auditing and Assurance in the Digital Age, edited by Rui Pedro Marques, Carlos Santos, and Helena Inácio, 224-255. Hershey, PA: IGI Global, 2019. https://doi.org/10.4018/978-1-5225-7356-2.ch012

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Abstract

The objective of this chapter is to monitor database transactions and provide information accountability to databases. It provides a methodology to retrieve and standardize different audit logs in a uniform XML format which are extracted from different databases. The financial transactions obtained through audit logs are then analyzed with database forensic audit. The transactions are examined, detected, and classified as per regulations and well-defined RBI antimony laundering rules to obtain outliers and suspicious transactions within audit logs. Bayesian network is used in this research to represent rule-based outlier detection model which identifies the risk level of the suspicious transactions.

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