 Abnormal Financial Transaction Detection via AI TechnologyZhuo Wang
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 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:igg:jdst00:v:12:y:2021:i:2:p:24-34 Access Statistics for this article International Journal of Distributed Systems and Technologies (IJDST) is currently edited by Nik Bessis More articles in International Journal of Distributed Systems and Technologies (IJDST) from IGI Global |
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