The use of predictive modeling to identify relevant features for suspicious activity reporting

Author:

Hayble-Gomes Emmanuel

Abstract

Purpose The purpose of this study is to explore and use artificial intelligence (AI) techniques for identifying the relevant attributes necessary to file a suspicious activity report (SAR) using historical customer transactions. This method is known as predictive modeling, a statistical approach which uses machine learning algorithm to predict outcomes by using historical data. The models are applied to a modified data set designed to mimic transactions of retail banking within the USA. Design/methodology/approach Machine learning classifiers, as a subset of AI, are trained using transactions that meet or exceed the minimum threshold amount that could generate an alert and report a SAR to the government authorities. The predictive models are developed to use customer transactional data to predict the probability that a transaction is reportable. Findings The performance of the machine learning classifiers is determined in terms of accuracy, misclassification, true positive rate, false positive rate and false negative rate. The decision tree model provided insight in terms of the attributes relevant for SAR filing based on the rule-based criteria of the algorithm. Originality/value This research is part of emerging studies in the field of compliance where AI/machine learning technology is used for transaction monitoring to identify relevant attributes for suspicious activity reporting. The research methodology may be replicated by other researchers, Bank Secrecy Act/anti-money laundering (BSA/AML) officers and model validation analysts for BSA/AML compliance models.

Publisher

Emerald

Subject

Law,General Economics, Econometrics and Finance,Public Administration

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Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Integrated Framework for Identification of Suspicious Activity for Remote Sensing Applications;Proceedings of the 5th International Conference on Information Management & Machine Intelligence;2023-11-23

2. Improving Classification Performance of Money Laundering Transactions Using Typological Features;2023 7th International Conference on Information Technology (InCIT);2023-11-16

3. Deploying artificial intelligence for anti-money laundering and asset recovery: the dawn of a new era;Journal of Money Laundering Control;2023-05-19

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