In today’s economic scenario, credit card use has become extremely commonplace. These cards allow the user to make payments of large sums of money without the need to carry large sums of cash. They have revolutionized the way of making cashless payments and made making any sort of payments convenient for the buyer. This electronic form of payment is extremely useful but comes with its own set of risks. With the increasing number of users, credit card frauds are also increasing at a similar pace. The credit card information of a particular individual can be collected illegally and can be used for fraudulent transactions. Some Machine Learning Algorithms can be applied to collect data to tackle this problem. This paper presents a comparison of some established supervised learning algorithms to differentiate between genuine and fraudulent transactions.
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A Fraud can be described as intentional deceit that is perpetrated for some kind of gain, mostly monetary. It is an unfair practice whose occurrences are increasing by the day. There has been a sharp increase in the usage of electronic payment methods like credit and debit cards and this has in turn led to a rise in credit card frauds. These cards may be used in both online as well as offline modes to make payments . In the case of the online mode of payment, the card may not have to be physically presented. In such cases, the card data is prone to attack by hackers or cybercriminals. These kinds of frauds result in millions being lost every year. To overcome this obstacle, many algorithms have and are being developed. Various detection approaches are being worked upon to solve this issue most efficiently .
Credit card transactions are extremely commonplace now but they also come with their own set of problems. There are a lot of problems faced during fraud detection. The process of acceptance or rejection of a transaction happens within a very small time frame, which may range between micro and milliseconds. Therefore, the process adopted for the detection of a fraudulent transaction has to be extremely quick and effective. Another problem is that there are a vast number of similar types of transactions happening at the same time. This makes it difficult to monitor each and every transaction individually and hence determine a fraud. Thus, an efficient Fraud Detection System must be put into work to be able to differentiate between a genuine and a fraud transaction. Such a system works on the principle of learning user-specific card usage behavior. Thus, existing approaches of supervised as well as unsupervised machine learning techniques can be applied to the data. The objective of this paper is to evaluate an imbalanced dataset with the help of various supervised machine learning models and to determine which one of those is best suited for detecting credit card frauds. We make use of 5 supervised machine learning models to evaluate a dataset on the basis of various predefined criteria.
Specific algorithms based on artificial intelligence and neural networks are also being proposed and implemented to predict the credit card frauds with increased accuracy. The distribution of the datasets used for fraud detection is highly imbalanced. So, to overcome this obstacle, under-sampling and oversampling techniques are being designed to obtain comparatively balanced data. Data mining techniques are also being implemented in order to create a more efficient Fraud Detection System . Another important area of development is the emergence of new hybrid models. These are derived from preexisting supervised as well as unsupervised machine learning techniques. Hybrid Models may be able to produce a more accurate result as they encapture the capabilities of both supervised as well as unsupervised machine learning . It is observed that the performance of all machine learning datasets is hindered due to the skewness of available data sets which are usually unbalanced. To overcome this problem, the unbalanced datasets are to be converted to balanced ones. This can be done mainly in two ways which are the Intrinsic Method and Network-based Method. In the Intrinsic Feature Method, a pattern in the customer Activity is observed whereas, in the Network-based features Method, the network of users and the card merchants are exploited. These techniques may significantly improve the functioning of certain Models as they work on a more Balanced Dataset.
Machine Learning is basically an application of Artificial Intelligence techniques in order to make the systems learn by themselves. This means that the system automatically learns, improvises, and adapts through the experience without it being programmed for performing a particular operation. This field deals with the coming up of programs that can deal with data on their own, that is, which can access and modify the provided data according to the need of the user. Machine Learning can be classified into 3 main categories which are Supervised Learning, Unsupervised Learning and Reinforcement Learning. A. Machine Learning in Credit Card Fraud Detection Machine Learning basically provides the system with the “ability to learn”. The machine is able to use previously procured data and analyze it further without being explicitly commanded to. This feature is basically beneficial in the detection of credit card frauds. This enables machine learning algorithms to be successfully implemented in the banking domain to identify potentially risky transactions . There are more than a million transactions which occur daily, all these need to be checked for authenticity. To carry out this task, the system can be trained to separate out the fraudulent transactions from the non-fraudulent ones. This is mostly done by feeding it past transaction data, especially the ones from the non-authentic transactions so that all the newly approaching transactions can be labeled as normal or suspicious respectively.
In this study, we used an imbalanced dataset to check the suitability of different supervised machine learning models to predict the chances of occurrence of a fraudulent transaction. We used sensitivity, precision, and time as the deciding parameters to come to a particular conclusion. Accuracy as a parameter was not used as it is not sensitive to imbalanced data and does not give a conclusive answer. We analyzed the kNN, Naive Bayes, Decision Tree, Logistic Regression, and Random Forest models in this study. We used these models for predicting the chances of occurrence of a fraudulent credit card transaction out of a given number of transactions. Credit Card frauds are a modern-day issue and we came to the conclusion that the best-suited model for predicting such frauds is the Decision Tree model. The analysis shows that the sensitivity of the kNN model is greater than that of the Decision tree, but as the time taken by kNN for testing the data is very large, we choose Decision Tree over kNN. In case of fraud detection, we need to ensure that minimum time is taken for prediction, therefore, Decision Tree is the preferred model. Future researchers in this field may apply the resampling techniques to the respective datasets being used. This technique helps to reduce the imbalance ratio of datasets which in turn produces better classification results. After the comparative analysis of the various Supervised Learning models, we can infer that the Decision Tree Model is the best approach to be used for detecting credit card fraud detection. But, the performance of the Decision Tree Model must also be evaluated with the help of unsupervised machine learning models in the future to produce a more conclusive result. This tells us whether the model which is chosen is a better option or the unsupervised machine learning techniques perform better.
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FULL Paper PDF file:Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison
Supervised Machine Learning Algorithms for Credit Card Fraud Detection: A Comparison
2020 10th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2020, pp. 680-683,
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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.