DETECTION OF ANOMALOUS FUND TRANSFERS BETWEEN DIFFERENT BANKS

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2022-9-02

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Tunçay, Abdullah Mert

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There are various risks associated with fund transfers between banks, including frauds and abuse of the banking system. Any anomaly in a fund transfer network must be detected and learned, since unexpected messages or transfer events may occur. The purpose of this thesis is to detect anomalies in a fund transfer network using fund transfer packets. In such a network, different fund transfer protocols use different message types. Moreover, these messages have several different features, such as dates, participants, amounts, time intervals, etc. We utilize various statistical, machine learning and deep learning-based anomaly detection methods such as isolation forests, local outlier factors, k-nearest-neighbour and LSTM Autoencoders to detect anomalies in fund transfers. For this purpose, we collect a set of real-world fund transfer messages and utilize this set in our experiments. It is evident from our results that feature group selection has a significant impact on the accuracy of anomaly detection.

Subject Keywords

Anomaly Detection, Money Transaction, Unsupervised Learning, LSTM

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https://hdl.handle.net/11511/98764

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Graduate School of Informatics, Thesis

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Citation Formats

A. M. Tunçay, “DETECTION OF ANOMALOUS FUND TRANSFERS BETWEEN DIFFERENT BANKS,” M.S. - Master of Science, Middle East Technical University, 2022.