A Novel Cardholder Behavior Model for Detecting Credit Card Fraud

Kultur, Yigit; Caglayan, M.U.

doi:10.1080/10798587.2017.1342415

Cited by 8 publications

(10 citation statements)

References 16 publications

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“…Ji et al [18] proposed a method for ecommerce user abnormal behavior detection research, based on user historical behavior data to establish user's normal behavior pattern, and finally used the pattern comparison method to determine user's transactions. K'ult'ur et al [19] proposed a new cardholder behavior model for credit card fraud detection, which used this model to detect fraud transactions in combination with user historical consumption behavior. Zheng et al [20] proposed a new credit card fraud detection system based on behavior certificate.…”

Section: Related Workmentioning

confidence: 99%

“…The second is to use expert system based on expert rules to achieve fraud detection by anti-fraud experts [3]. However, expert system can't adapt to new fraud methods, and there will be rule redundancy [19]. The third is to analyze group trading users and use machine learning and deep learning methods to mine common behaviors of users, such as neural networks [4], random forests [7], [8], relationship networks [13], HMM [14], and so on.…”

Section: Introductionmentioning

confidence: 99%

“…However, these methods train models based on transaction data for all users. They can only mine the common characteristics of all users, it is difficult to learn the individual behavior of each user [15], [24], and such models cannot effectively identify new types of fraud [19]- [21]. The fourth is to extract user behavior pattern from the perspective of individual user, and then determine whether user's current behavior is fraud, including user's browsing behavior [15], mouse and keystrokes behavior [16], transaction behavior [17], [20], [21], etc.…”

Section: Introductionmentioning

confidence: 99%

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A Fraud Detection Method for Low-Frequency Transaction

et al. 2020

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The effectiveness of transaction fraud detection methods directly affects the loss of users in online transactions. However, for low-frequency users with small transaction volume, the existing methods cannot accurately describe their transaction behaviors for each user, or lead to a high misjudgment rate. So we propose a new method for individual behavior construction, which can make the behavior of low-frequency users more accurate by migrating the current transaction group behavior and transaction status. Firstly, we consider the user's only historical transactions, combined with the optimal risk threshold determination algorithm, to form the user's own transaction behavior benchmark. Secondly, through the DBSCAN clustering algorithm, the behavior characteristics of all current normal samples and fraud samples are extracted to form the common behavior of the current transaction group. Finally, based on historical transaction records, the current transaction status is extracted using a sliding window mechanism. The combination of the three constitutes a new transaction behavior of the user. On this basis, a multi-behavior detection model based on new transaction behavior is proposed. According to the result of each behavior, Naive Bayes model is used to calculate the probability that current transaction belongs to fraud, and finally determine whether current transaction is fraud. Experiments prove that the method proposed in this paper can have a good effect on low-frequency users, which can accurately identify fraud transactions and has a low misjudgment rate for normal transactions.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Fraud Detection Method for Low-Frequency Transaction

et al. 2020

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“…Kultur et al [16] propose a novel cardholder behavior model for detecting credit card fraud. They propose building a model by clustering transaction amounts of a user, with respect to merchant category code (MCC) of the transaction, using the Expectation Maximization algorithm.…”

Section: Fraud Discovery Approachesmentioning

confidence: 99%

Fraud Detection in Payments Transactions: Overview of Existing Approaches and Usage for Instant Payments

Diadiushkin

Sandkuhl

Maiatin

2019

J. Complex Syst. Inform. Model. Q.

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Financial industries are undergoing a digital transformation of their products, services, overall business models. Part of this digitalization in banking aims at automating most of the manual work in payment handling and integrating the workflows of involved service providers. The focus of the work presented in this paper is on fraud discovery and steps to fully automate it. Fraud discovery in financial transactions has become an important priority for banks. Fraud is increasing significantly with the expansion of modern technology and global communication, which results in substantial damages for the banks. Instant payment (IP) transactions cause new challenges for fraud detection due to the requirement of short processing time. The paper investigates the possibility to use artificial intelligence in IP fraud detection. The main contributions of our work are (a) an analysis of problem relevance from business and literature perspective, (b) a proposal for technological support for using AI in fraud detection of instant payment transactions, and (c) a feasibility study of selected fraud detection approaches.

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“…Author has compared this approach against traditional classification models on real life data. Yiğit Kültür [15] has focused on analyzing the cardholder spending behavior and proposes a novel cardholder behavior model for detecting credit card fraud. The model is named Cardholder Behavior Model (CBM).…”

Section: Related Workmentioning

confidence: 99%

Database Implementation and Testing of Dynamic Credit Card Fraud Detection System

Jog¹,

Chandavale²

2017

IJCA

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Credit card frauds are increasing with the increase in use of plastic money. These frauds include the transactions done either by stealing the physical card or using card data such as card number, expiry date and pin number. There is a need to recognize customer spending pattern and apply validations for incoming transaction. Suspicious transactions can go under rigorous security checks. This paper describes the database implementation of credit card fraud detection system which is adaptive to concept drift environment. The system is designed using PL-SQL stored procedures and JAVA. The validation procedure and testing results are included in this paper.

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A Novel Cardholder Behavior Model for Detecting Credit Card Fraud

Cited by 8 publications

References 16 publications

A Fraud Detection Method for Low-Frequency Transaction

A Fraud Detection Method for Low-Frequency Transaction

Fraud Detection in Payments Transactions: Overview of Existing Approaches and Usage for Instant Payments

Database Implementation and Testing of Dynamic Credit Card Fraud Detection System

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