Credit Card Fraud Detection: A Novel Approach Using Aggregation Strategy and Feedback Mechanism

Jiang, Changjun; Song, Jiahui; Liu, Guanjun; Zheng, Lutao; Luan, Wenjing

doi:10.1109/jiot.2018.2816007

Cited by 127 publications

(57 citation statements)

References 22 publications

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“…This proposed credit card fraud detection system marks each behavioral pattern by specifically mining the behavioral patterns from the gathered data. Finally, based on the above strategy a time-to-time updated cardholder's profile is generated [21]. This method contains four steps as shown in Fig.…”

Section: Proposed Methodologymentioning

confidence: 99%

“…The yield of preprocessing could be a normal data matrix; i.e. a vector of instances or tuples (objects) where every instance indicates a set of attribute values [21]. If there are n instances with p attributes each, then there will be n rows and p columns in the standard data matrix.…”

Section: A Preprocessing Datamentioning

confidence: 99%

“…To prevent fraud events there are generally two procedures: Classification method and Abnormality identification. In the first method, a classifier is trained with the given patterns of normal and fraud transactions using supervised procedures [6].Second method is having the ability to filter the new deals that is uneven against the profile of the card holder by computing the distance of the inward transactions with the profile [5]. The two above methods have its own drawbacks.…”

Section: Introductionmentioning

confidence: 99%

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Fuzzy Particle Swarm Optimization Feature Selection and Aggrandized Classifier for Uncovering Frauds in Credit Card Deals

Kumar

2020

IJEAT

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In today's economy, credit card plays a very important role. The rise of credit card customers improved, credit card scam cases were also on the rise. Numerous procedures are anticipated to challenge the evolution of the frauds in credit cards. In this research work, proposed an innovative fraud detection method which utilizes the similar cardholder’s behavioral patterns to construct a current cardholder’s interactive profile in order to stay away from the credit card scams. However, the selection of optimal features from the samples and the decision cost for accuracy becomes main important problem. To illuminate these issues this proposed research work presents an innovative fraud detection technique that makes out of four phases: 1. To augment a cardholder’s behavioral styles, first we divide all cardholders into distinctive groups making use of the cardholder’s historical transaction data such that the members of each group have the similar transaction behavior by K-means. 2. Introduces a new Fuzzy Particle Swarm Optimization (FPSO) feature selection for the amplification of fraud detection in credit cards. 3. By means of a prolonged wrapper method, an ensemble classification are performed by Aggrandized Kernel based Support Vector Machine (AKSVM).4.Refreshing the cardholder’s social profile with an input system. This Proposed work adopts the external quality metrics as Accuracy, Recall, Concept drift rate and Fraud feature rate. The UCI dataset is used and is done in MATLAB framework. The analytical measures were used to estimate the routine of the mentioned fraud detection technique. The simulation results show that this proposed innovative fraud detection method provides better accuracy results than other fraud detection techniques. The low concept drift rate results the gain of the innovative method to classify the transactions accurately.

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Section: Proposed Methodologymentioning

confidence: 99%

Section: A Preprocessing Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Fuzzy Particle Swarm Optimization Feature Selection and Aggrandized Classifier for Uncovering Frauds in Credit Card Deals

Kumar

2020

IJEAT

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“…Here they proposed a technique for solving an adaptive capacity to adjust its parameters to cardholder's timely actions. These writers ' potential objective is to develop an employee regular time frame to enhance cheating prevention performance [3]. The real-world fraud detection system (FDS) consists of two main aspects [4]: a.…”

Section: Devika S P Nisarga K S Gagana P Rao Chandini S B Rajkumar Nmentioning

confidence: 99%

“…The HMM based credit card fraud detection analyzed the fraudulent by transaction patterns on each card. The HMM based credit card fraud detection analyzed the fraudulent by transaction patterns on each card [3]. The high transactions majority dose not verifying by the investigators time and cost constraints evident this transaction endures unlabeled until customer uncover and discover the fraud report or until enough time was elapsed that considered has transaction non disputed genuinely.…”

Section: Introductionmentioning

confidence: 99%

A Research on Credit Card Fraudulent Detection System

Devika¹,

Nisarga²,

Rao³

et al. 2019

IJRTE

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Nowadays credit card is more popular among the private and public employees. By using the credit card, the users purchase the consumable durable products in online, also transferring the amount from one account to other. The fraudster is detecting the details of the behavior user transaction and doing the illegal activities with the card by phishing, Trojan virus, etc. The fraudulent may threaten the users on their sensitive information. In this paper, we have discussed various methods of detecting and controlling the fraudulent activities. This will be helpful to improve the security for card transaction in future. I.

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