A novel model for credit card fraud detection using Artificial Immune Systems
Graphical abstract
Introduction
Credit card fraud is an important issue and has considerable cost for banks and card issuer companies. Financial organizations try to prevent account misuse using different security solutions. The more complex the security solutions are, the more sophisticated fraudsters get i.e. fraudsters change their methods over time. Therefore it is crucial to improve fraud detection methods along with security modules which try to prevent fraud. Fraud detection has become a crucial activity in order to decrease the impact of fraudulent transactions on service delivery, costs, and reputation of the company. There are plenty of methods used for fraud detection each of which tries to retain maximum quality of service while keeping false alarm rate at minimum. Fraud is cost and detecting it before the transaction is registered will reduce this cost significantly, which needs a very accurate system with quite few false alarms. Edge and Falcone Sampaio [1] state that while implementation of proactive methods increases the potential for early fraud alerting, real-time processing significantly reduces the available time window within which computational analysis should be performed and an accurate decision should be made in response to newly arriving transactions. The quicker a fraud detection system responds, the better. Fraud detection systems are trained using older transactions in order to decide about new ones. This training phase is time-consuming which can be parallelized in most cases. In order to reduce computation time one can reduce the number of previous transactions processed by minimizing the time window, use less complicated methods, and etc. each of which might result in reduction in accuracy, which means more missed fraud cases and more false alarms. Accordingly, a powerful tool is needed on which the fraud detection system could run and process transactions in minimum time This paper suggests using cloud computing i.e. implementing fraud detection system on a cloud-base file system, namely Hadoop, which makes data parallelization possible in large datasets.
Different methods have been used for fraud detection including Bayesian algorithm [2], Neural network [3], Markov model [4], account signature [1], Artificial Immune Systems [5], [6], [7], [8]. AIS is based on human immune system and is similar to fraud detection system in many aspects: 1 – Both of them pursue the same goal of separating normal records from unauthorized ones. 2 – In both cases the number of normal records is much more than unauthorized ones. 3 – In both cases unauthorized records are similar to those of normal. In human body viruses and non-self cells carry protein and masquerade self cells. Fraudsters also try to have similar behavior to card owner's behavior. 4 – Both systems have to detect and learn new methods of misuse. Human body could face new types of non-self cells any time and it has to not only detect the new types, but also remember them so that they can be detected later. Similarly, a typical fraud detection system should be able to detect any type of fraud even if it had not happened before. Also the system should learn it for future cases.
AIS addresses detecting non-self cells, imitating the functions of human body which occurs during generating detector cells, detecting non-self cells, and cleaning the body from non-self while learning its pattern. Detector cells, namely lymphocytes, are self-tolerant which means they are not stimulated by self cells but by non-self cells. Immune system can learn new patterns of non-self cells that it has not come across before. Once a detector is stimulated by a non-self, the system keeps the detector as a memory cell. Therefore, if that particular non-self cell enters body later, it can be detected again. This makes AIS adaptable to its environment. Immune system starts training with no information about non-self cells. This means it is trained using only self cells.
In this paper we will use an AIS-based method for credit card fraud detection and introduce AFDM. We will improve a previously introduced algorithm [9] in various aspects to get higher precision. We will also propose a new implementation model for the method in order to reduce training time. The results are compared to a similar work [6] which has improved AIS system parameters. We use the same parameters as well as the dataset used in [6]. The remainder of this paper is structured as follows: Section 2 presents the background information. First AIS is described followed by Artificial Immune Recognition System – the algorithm which is used in this paper. Then, after a brief introduction about Cloud Computing, Hadoop file system and MapReduce API are described. Section 3 is about related work in credit card fraud detection field focusing on using AIS for fraud detection. Section 4 describes AFDM, the methodology, the improvements on AIRS, and the implementation model. Section 5 includes the results of the tests. Finally, Section 6 discusses future research directions.
Section snippets
AIS
AIS simulates human body immune system functionality. Human body detects non-self cells, which might be viruses, pathogens, germs, etc., by creating detector cells named lymphocytes. As this functionality is similar to what a typical fraud detection system does, AIS is used for fraud detection in some researches. AIS detects non-self cells using two basic functions in human body which generate and mature lymphocytes: Negative Selection and Clonal Selection. Detector cells (lymphocytes) are
Related work: credit card fraud detection
Many researchers address credit card fraud detection and many methods are developed. Yet real-time fraud detection remains an issue. Edge and Falcone Sampaio [1] survey Account Signature which steps toward real-time fraud detection. This paper mentions that methods based on data mining need flagged records, are time-consuming, and need to be updated. The only problem is Account Signatures is an inflexible behavior model as it considers the total trend for user. It is obvious that user might
Materials, methods, and theory of AFDM
The most important issue in credit card fraud detection is to achieve high detection rate while keeping false alarm rate low. It is also important to have real-time responses and the system decide about a transaction before it is registered. Therefore time-consuming algorithms should respond as soon as possible; especially those algorithms which have high training time. AFDM presents some improvements on AIRS algorithm which follows. In order to get more precise results while having fewer false
Evaluation metrics
Four parameters are used for evaluating fraud detection methods – classification methods in general: True negative (TN) – the number of normal transactions flagged as normal, false negative (FN) – the number of fraudulent transactions flagged wrongly as normal, i.e. missed fraud cases, true positive (TP) – the number of fraudulent transactions flagged as fraud i.e. detected fraud cases, false positive (FP) – the number of normal transactions flagged as fraud. Obviously, a method which offers
Conclusion
This paper addressed credit card fraud detection using AIS (Artificial Immune System), and a new model called AIS-based Fraud Detection Model (AFDM) was introduced for this purpose. The model added some improvements to AIRS (Artificial Immune Recognition System) algorithm which helped to increase the precision, decrease the cost and system training time. Affinity between antigens was calculated using a novel method in AFDM. Negative Selection was used along with Clonal Selection in order to
Discussion and future work
As we showed in this paper, further improvements on AIS can help getting better results in fraud detection. The writers believe AIS has potential for getting much better results. Following are some improvements which could be done on this paper:
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Weighting dataset fields in distance function. There are plenty of data fields in a transaction database some of which are more important than the others considering fraud detection. However, some others are least important i.e. there is no meaningful
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.
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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.
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