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Abnormal Financial Transaction Detection via AI Technology

Abnormal Financial Transaction Detection via AI Technology

Zhuo Wang
Copyright: © 2021 |Volume: 12 |Issue: 2 |Pages: 11
ISSN: 1947-3532|EISSN: 1947-3540|EISBN13: 9781799861737|DOI: 10.4018/IJDST.2021040103
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MLA

Wang, Zhuo. "Abnormal Financial Transaction Detection via AI Technology." IJDST vol.12, no.2 2021: pp.24-34. http://doi.org/10.4018/IJDST.2021040103

APA

Wang, Z. (2021). Abnormal Financial Transaction Detection via AI Technology. International Journal of Distributed Systems and Technologies (IJDST), 12(2), 24-34. http://doi.org/10.4018/IJDST.2021040103

Chicago

Wang, Zhuo. "Abnormal Financial Transaction Detection via AI Technology," International Journal of Distributed Systems and Technologies (IJDST) 12, no.2: 24-34. http://doi.org/10.4018/IJDST.2021040103

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Abstract

Financial supervision plays an important role in the construction of anti-corruption and honesty, but financial data has the characteristics of non-stationary, non-linearity, and low signal-to-noise ratio, and there is no special training set that is used to identify abnormal financial data. This paper generates time series of financial transaction data with a weekly time span, and selects the total transaction amount, transaction dispersion coefficient, and the number of transfers as the characteristics of financial account data. The features are then input in a weighted one-class support vector machine (WOC-SVM) model to determine whether the transaction is abnormal. The weighted one-class support vector machine (WOC-SVM) is learnt on a training set which consists of massive normal transaction due to the difficulty to collect abnormal transactions. The parameters in WOC-SVM are tuned by cross-validation. The experiments on simulation data demonstrate the effectiveness of the WOC-SVM model learnt on selected features to detect suspicious values.

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