Abnormal Financial Transaction Detection via AI Technology

Zhuo Wang
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Zhuo Wang: Jilin Normal University, China

International Journal of Distributed Systems and Technologies (IJDST), 2021, vol. 12, issue 2, 24-34

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.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:igg:jdst00:v:12:y:2021:i:2:p:24-34

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