Abnormal Financial Transaction Detection via AI Technology

Abnormal Financial Transaction Detection via AI Technology

Zhuo Wang
Copyright: © 2021 |Pages: 11
DOI: 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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2. Background And Feature Representation For Financial Transactions

Generally, a financial account contains a number of user information. According to the knowledge in the financial field, the daily transaction account generally includes: account number, debit amount, credit amount, balance, transaction time, counterparty account, counterparty account name, summary, purpose etc. However, some properties of transaction account are insignificant for analyzing financial transaction. In abnormal financial transaction analysis, the subject attributes are removed. Through the analysis of financial account data records, the account's transaction behavior patterns can be summarized. Thus, those transactions with significantly different behavior patterns can be identified as the clue for suspicious transaction analysis.

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