Credit card is one of the popular modes of payment for electronic transactions in many developed and developing countries. Invention of credit cards has made online transactions seamless, easier, comfortable and convenient. However, it has also provided new fraud opportunities for criminals, and in turn, increased fraud rate. The global impact of credit card fraud is alarming, millions of US dollars have been lost by many companies and individuals. Furthermore, cybercriminals are innovating sophisticated techniques on a regular basis, hence, there is an urgent task to develop improved and dynamic techniques capable of adapting to rapidly evolving fraudulent patterns. Achieving this task is very challenging, primarily due to the dynamic nature of fraud and also due to lack of dataset for researchers. This paper presents a review of improved credit card fraud detection techniques. Precisely, this paper focused on recent Machine Learning based and Nature Inspired based credit card fraud detection techniques proposed in literature. This paper provides a picture of recent trend in credit card fraud detection. Moreover, this review outlines some limitations and contributions of existing credit card fraud detection techniques, it also provides necessary background information for researchers in this domain. Additionally, this review serves as a guide and stepping stone for financial institutions and individuals seeking for new and effective credit card fraud detection techniques.

A survey of machine-learning and nature-inspired based credit card fraud detection techniques

How Artificial Intelligence and machine learning research impacts payment card fraud detection: A survey and industry benchmark

Financial fraud detection applying data mining techniques: A comprehensive review from 2009 to 2019

fraud is increasing significantly with the development of modern technology and the global superhighways of communication, resulting in the loss of billions of dollars worldwide each year. The companies and financial institution loose huge amounts due to fraud and fraudsters continuously try to find new rules and tactics to commit illegal actions. Thus, fraud detection systems have become essential for all credit card issuing banks to minimize their losses. The most commonly used fraud detection methods are Neural Network (NN), rule-induction techniques, fuzzy system, decision trees, Support Vector Machines (SVM), Artificial Immune System (AIS), genetic algorithms, K-Nearest Neighbor algorithms. These techniques can be used alone or in collaboration using ensemble or meta-learning techniques to build classifiers. This paper presents a survey of various techniques used in credit card fraud detection and evaluates each methodology based on certain design criteria.

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Analysis on Credit Card Fraud Detection Techniques: Based on Certain Design Criteria

With the popularization of online shopping, transaction fraud is growing seriously. Therefore, the study on fraud detection is interesting and significant. An important way of detecting fraud is to extract the behavior profiles (BPs) of users based on their historical transaction records, and then to verify if an incoming transaction is a fraud or not in view of their BPs. Markov chain models are popular to represent BPs of users, which is effective for those users whose transaction behaviors are stable relatively. However, with the development and popularization of online shopping, it is more convenient for users to consume via the Internet, which diversifies the transaction behaviors of users. Therefore, Markov chain models are unsuitable for the representation of these behaviors. In this paper, we propose logical graph of BP (LGBP) which is a total order-based model to represent the logical relation of attributes of transaction records. Based on LGBP and users’ transaction records, we can compute a path-based transition probability from an attribute to another one. At the same time, we define an information entropy-based diversity coefficient in order to characterize the diversity of transaction behaviors of a user. In addition, we define a state transition probability matrix to capture temporal features of transactions of a user. Consequently, we can construct a BP for each user and then use it to verify if an incoming transaction is a fraud or not. Our experiments over a real data set illustrate that our method is better than three state-of-the-art ones.

Transaction Fraud Detection Based on Total Order Relation and Behavior Diversity

The most accepted payment mode is credit card for both online and offline in today's world, it provides cashless shopping at every shop in all countries. It will be the most convenient way to do online shopping, paying bills etc. Hence, risks of fraud transaction using credit card has also been increasing. In the existing credit card fraud detection business processing system, fraudulent transaction will be detected after transaction is done. It is difficult to find out fraudulent and regarding loses will be barred by issuing authorities. Hidden Markov Model is the statistical tools for engineer and scientists to solve various problems. In this paper, it is shown that credit card fraud can be detected using Hidden Markov Model during transactions. Hidden Markov Model helps to obtain a high fraud coverage combined with a low false alarm rate.

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Study of Hidden Markov Model in Credit Card Fraudulent Detection

In modern retail market environment, electronic commerce has rapidly gained a lot of attention and also provides instantaneous transactions. In electronic commerce, credit card has become the most important means of payment due to fast development in information technology around the world. As the usage of credit card increases in the last decade, rate of fraudulent practices is also increasing every year. Existing fraud detection system may not be so much capable to reduce fraud transaction rate. Improvement in fraud detection practices has become essential to maintain existence of payment system. In this paper, we show how Hidden Markov Model (HMM) is used to detect credit card fraud transaction with low false alarm. An HMM based system is initially studied spending profile of the card holder and followed by checking an incoming transaction against spending behavior of the card holder, if it is not accepted by our proposed HMM with sufficient probability, then it would be a fraudulent transaction.

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Application of Hidden Markov Model in Credit Card Fraud Detection

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Study of Hidden Markov Model in credit card fraudulent detection

In modern world, the most accepted payment mode is credit card for both online and offline, it gives cash less shopping at almost every shop in developed country. Hence, fraud transaction using credit card also increases. In the existing credit card fraud detection business processing system, fraud is detected after fraud is done in the credit card transaction processing. Hidden Markov Model is the statistical tools for engineer ad scientists to solve various problems. In this paper, it is shown that credit card fraud can be detected using hidden Markov model during transactions. In present models, fraudulent transaction will be detected after transaction is done. Hidden Markov model helps to obtain a high fraud coverage combined with a low false alarm rate.

Detection of Credit Card Fraud Transaction using Hidden Markov Model

The most accepted payment mode nowadays used is credit card for both online and offline. Credit card provides cash less shopping at every shop in all countries. It is the most convenient way to do online shopping, paying bills etc. Hence fraud detection related with it is also growing. In the existing credit card fraud detection system, the fraud is detected after it is done in the credit card transaction processing. As many of online purchases are made it is very difficult to know the person who is doing fraud. Due to this credit card transaction there is development of new concepts for secure transaction. This paper shows how Hidden Markov Model (HMM) can be used successfully to obtain a high fraud coverage combined with a low false alarm rate.

S. Patil

Papers

Study of Hidden Markov Model in Credit Card Fraudulent Detection

Application of Hidden Markov Model in Credit Card Fraud Detection

Study of Hidden Markov Model in credit card fraudulent detection

Detection of Credit Card Fraud Transaction using Hidden Markov Model

Detailed Discussion on Hidden Markov Model in Credit card Fraudulent Detection