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Credit card fraud detection using machine learning with integration of contextual knowledge

Abstract : The detection of credit card fraud has several features that make it a difficult task. First, attributes describing a transaction ignore sequential information. Secondly, purchasing behavior and fraud strategies can change over time, gradually making a decision function learned by an irrelevant classifier. We performed an exploratory analysis to quantify the day-by-day shift dataset and identified calendar periods that have different properties within the dataset. The main strategy for integrating sequential information is to create a set of attributes that are descriptive statistics obtained by aggregating cardholder transaction sequences. We used this method as a reference method for detecting credit card fraud. We have proposed a strategy for creating attributes based on Hidden Markov Models (HMMs) characterizing the transaction from different viewpoints in order to integrate a broad spectrum of sequential information within transactions. In fact, we model the authentic and fraudulent behaviors of merchants and cardholders according to two univariate characteristics: the date and the amount of transactions. Our multi-perspective approach based on HMM allows automated preprocessing of data to model temporal correlations. Experiments conducted on a large set of data from real-world credit card transactions (46 million transactions carried out by Belgian cardholders between March and May 2015) have shown that the proposed strategy for pre-processing data based on HMMs can detect more fraudulent transactions when combined with the Aggregate Data Pre-Processing strategy.
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Submitted on : Monday, September 28, 2020 - 4:42:09 PM
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  • HAL Id : tel-02951477, version 1


Yvan Lucas. Credit card fraud detection using machine learning with integration of contextual knowledge. Artificial Intelligence [cs.AI]. Université de Lyon; Universität Passau (Deutscheland), 2019. English. ⟨NNT : 2019LYSEI110⟩. ⟨tel-02951477⟩



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