A New User-Based Model for Credit Card Fraud Detection Based on Artificial Immune System (original) (raw)
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A novel model for credit card fraud detection using Artificial Immune Systems
The amount of online transactions is growing these days to a large number. A big portion of these transactions contains credit card transactions. The growth of online fraud, on the other hand, is notable, which is generally a result of ease of access to edge technology for everyone. There has been research done on many models and methods for credit card fraud prevention and detection. Artificial Immune Systems is one of them. However, organizations need accuracy along with speed in the fraud detection systems, which is not completely gained yet. In this paper we address credit card fraud detection using Artificial Immune Systems (AIS), and introduce a new model called AIS-based Fraud Detection Model (AFDM). We will use an immune system inspired algorithm (AIRS) and improve it for fraud detection. We increase the accuracy up to 25%, reduce the cost up to 85%, and decrease system response time up to 40% compared to the base algorithm. Neda Soltani Halvaiee is graduated from the College of Computer Engineering and Information Technology at Amirkabir University of Technology. Her research focuses on credit card fraud detection. She also has a background of Cloud Computing, and usage of Artificial Immune Systems. She is currently doing PhD at Amirkabir University of Technology. She will extend her study to the field of Pervasive computing during her PhD.
Fraud Detection System based on Artificial Immune System
Nowadays, one of the most important problems for financial companies is fraud related to online transactions. It is becoming increasingly sophisticated and advanced, leading to financial losses on the part of both customers and companies. Based on this, my company was tasked with creating a fraud detection system that is scalable and adaptable to change. This research aims to create a solution that can be used to identify differences in customer behavior patterns and detect fraud. The artificial immune system model proposed in this article, combined with certain informative features, is simple to implement and can describe customer behavior patterns.
Identifying online credit card fraud using Artificial Immune Systems
2010
Significant payment flows now take place on-line, giving rise to a requirement for efficient and effective systems for the detection of credit card fraud. A particular aspect of this problem is that it is highly dynamic, as fraudsters continually adapt their strategies in response to the increasing sophistication of detection systems. Hence, system training by exposure to examples of previous examples of fraudulent transactions can lead to fraud detection systems which are susceptible to new patterns of fraudulent transactions. The nature of the problem suggests that Artificial Immune Systems (AIS) may have particular utility for inclusion in fraud detection systems as AIS can be constructed which can flag `non standard' transactions without having seen examples of all possible such transactions during training of the algorithm. In this paper, we investigate the effectiveness of Artificial Immune Systems (AIS) for credit card fraud detection using a large dataset obtained from an on-line retailer. Three AIS algorithms were implemented and their performance was benchmarked against a logistic regression model. The results suggest that AIS algorithms have potential for inclusion in fraud detection systems but that further work is required to realize their full potential in this domain.
Information Systems Journal, 2012
Some biological phenomena offer clues to solving real-life, complex problems. Researchers have been studying techniques such as neural networks and genetic algorithms for computational intelligence and their applications to such complex problems. The problem of security management is one of the major concerns in the development of eBusiness services and networks. Recent incidents have shown that the perpetrators of cybercrimes are using increasingly sophisticated methods. Hence, it is necessary to investigate non-traditional mechanisms, such as biological techniques, to manage the security of evolving eBusiness networks and services. Towards this end, this paper investigates the use of an Artificial Immune System (AIS). The AIS emulates the mechanism of human immune systems that save human bodies from complex natural biological attacks. The paper discusses the use of AIS on one aspect of security management, viz. the detection of credit card fraud. The solution is illustrated with a case study on the management of frauds in credit card transactions, although this technique may be used in a range of security management applications in eBusiness.
… 2003. CEC'03. The 2003 Congress …, 2003
The retail sector often does not possess sufficient knowledge about potential or actual frauds. This requires the retail sector to employ an anomaly detection approach to fraud detection. To detect anomalies in retail transactions, the fraud detection system introduced in this work implements various salient features of the human immune system. This novel artificial immune system, called CIFD (Computer Immune system for Fraud Detection), adopts both negative selection and positive selection to generate artificial immune cells. CIFD also employs an analogy of the self-Major Histocompatability Complex (MHC) molecules when antigen data is presented to the system. These novel mechanisms are expected to improve the scalability of CIFD, which is designed to process gigabytes or more of transaction data per day. In addition, CIFD incorporates other prominent features of the HIS such as clonal selection and memory cells, which allow CIFD to behave adaptively as transaction patterns change.
Consumer credit scoring using an artificial immune system algorithm
2007 IEEE Congress on Evolutionary Computation, 2007
Credit scoring has become a very important task in the credit industry and its use has increased at a phenomenal speed through the mass issue of credit cards since the 1960s. This paper compares the performance of current classifiers against an artificial intelligence technique based on the natural immune system, named simple artificial immune system (SAIS). Experiments were performed on three benchmark credit datasets and SAIS was found to be a very competitive classifier.
Fraud Detection and Prevention in Smart Card Based Environments Using Artificial Intelligence
Lecture Notes in Computer Science, 2008
This paper discusses the development and research for the detection of fraud in Smart-Card environments by using artificial intelligence. The current research deals with behaviour based detection engine, which will detect abnormalities by learning the usual behaviour of the user and detecting new unusual behaviours. The behaviour-based detection engines is based on 'Neural Networks'. This work considers the feasibility of implementing 'Neural Network' fraud engine on a Smart card platforms.
IJERT-Credit Card Fraud Detection using Novel Learning Strategy
International Journal of Engineering Research and Technology (IJERT), 2018
https://www.ijert.org/credit-card-fraud-detection-using-novel-learning-strategy https://www.ijert.org/research/credit-card-fraud-detection-using-novel-learning-strategy-IJERTCONV6IS07004.pdf Credit card fraud detection with online shopping sector is performed here. A Credit card transactional data was simulated, trained and predicted and then the transaction blocking rules will check those details. The fault phase is data driven; this is purely data driven and adopts a classifier or another statistical model to estimate the probability for each feature vector being a fraud. This entire process is handled by investigator. With the investigated user information we perform the fraud detections. Investigators call cardholders and, after having verified , assign the label "genuine" or "fraudulent" to the alerted transaction, and return this information to the FDS. In the following, we refer to these labeled transactions as feedbacks and use the term alert-feedback interaction to describe this mechanism yielding supervised information in a real-world FDS.
Credit Card Fraud Detection & Prevention – A Survey
A credit card is a convenient tool that allows you to buy items now and pay for them later. Banking Sector involves a lot of transactions for their day to day operation and they have now realized that their main disquietude is how to detect fraud as early as possible. The credit card has become the most popular mode of payment for both online as well as regular purchase. Credit card frauds are increasing day by day regardless of various techniques developed for its detection. Fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Artificial Immune System (AIS), Hidden Markov Model (HMM), Neural Network, Genetic Algorithms, Decision Tree and Support Vector Machine (SVM). These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. The main objective of this paper is to review methodology of different detection methods based on credit card in terms of Parameter like Speed of detection, Accuracy and cost the comparison of mentioned approaches based on survey. This paper presents a survey of various techniques used in credit card fraud detection and prevention.