New Perspectives of Pattern Recognition for Automatic Credit Card Fraud Detection

2018 ◽  
pp. 4937-4950 ◽  
Author(s):  
Addisson Salazar ◽  
Gonzalo Safont ◽  
Alberto Rodriguez ◽  
Luis Vergara
Keyword(s):  
Pattern Recognition ◽  
False Alarm ◽  
Credit Card ◽  
New Technologies ◽  
Fraud Detection ◽  
Surrogate Data ◽  
Decision Fusion ◽  
Credit Card Fraud ◽  
Real Time Processing ◽  
Time Processing

Automatic credit card fraud detection (ACCFD) is a challenge issue that has been increasingly studied considering expanded potential of new technologies to emulate legitimate operations. Solution has to handle with fraud behavior changing in time; detection in data with very small fraud/legitimate operations ratio; and accomplish operation requirements of very low false alarm in real-time processing. In this chapter, main issues related with the problem of ACCFD and proposed solutions are discussed from theoretical and practical standpoints. The perspective of detection analyses from receiving operating characteristic curves and business key performance indicators are jointly analyzed. A new conceptual framework for ACCFD considering decision fusion and surrogate data is outlined including a case of study with different proportions of real and surrogate data. In addition, the sensitivity of the methods to different proportions of fraud/legitimate ratios is tested. Finally, theoretical and practical conclusions are provided as well as several open lines of research are proposed.

New Perspectives of Pattern Recognition for Automatic Credit Card Fraud Detection

2019 ◽  
pp. 268-283
Author(s):  
Addisson Salazar ◽  
Gonzalo Safont ◽  
Alberto Rodriguez ◽  
Luis Vergara
Keyword(s):  
Pattern Recognition ◽  
Credit Card ◽  
New Technologies ◽  
Fraud Detection ◽  
Surrogate Data ◽  
Decision Fusion ◽  
Credit Card Fraud ◽  
Real Time Processing ◽  
Time Processing ◽  
Behavior Detection

Automatic credit card fraud detection (ACCFD) is a challenge issue that has been increasingly studied considering the expanded potential of new technologies to emulate legitimate operations. Solution has to handle changing fraud behavior, detection in data with very small fraud/legitimate operations ratio, and accomplish operation requirements of very low false alarm in real-time processing. In this chapter, main issues related with the problem of ACCFD and proposed solutions are discussed from theoretical and practical standpoints. The perspective of detection analyses from receiving operating characteristic curves and business key performance indicators are jointly analyzed. A new conceptual framework for ACCFD considering decision fusion and surrogate data is outlined including a case of study with different proportions of real and surrogate data. In addition, the sensitivity of the methods to different proportions of fraud/legitimate ratios is tested. Finally, theoretical and practical conclusions are provided, and several open lines of research are proposed.

THE USE OF DATA MINING IN DETECTING CREDIT CARD FRAUD

2022 ◽  
Author(s):  
Kingsley Austin
Keyword(s):  
Machine Learning ◽  
Credit Card ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
High Detection Rate ◽  
Credit Card Fraud ◽  
Real Time Processing ◽  
Detection Systems ◽  
Hybrid Approaches ◽  
Use Of Data

Abstract— Credit card fraud is a serious problem for e-commerce retailers with UK merchants reporting losses of $574.2M in 2020. As a result, effective fraud detection systems must be in place to ensure that payments are processed securely in an online environment. From the literature, the detection of credit card fraud is challenging due to dataset imbalance (genuine versus fraudulent transactions), real-time processing requirements, and the dynamic behavior of fraudsters and customers. It is proposed in this paper that the use of machine learning could be an effective solution for combating credit card fraud.According to research, machine learning techniques can play a role in overcoming the identified challenges while ensuring a high detection rate of fraudulent transactions, both directly and indirectly. Even though both supervised and unsupervised machine learning algorithms have been suggested, the flaws in both methods point to the necessity for hybrid approaches.

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