CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

Computer Engineering

As the world is rapidly moving towards digitization and money transactions are becoming cashless, the use of credit cards has rapidly increased. The usage of credit cards for online and regular purchases is exponentially increasing and so is the fraud related with it. A large number of fraud transactions are made every day.Online transactions have become a significant and crucial aspect of our lives in recent years. It's critical for credit card firms to be able to spot fraudulent credit card transactions so that customers aren't charged for things they didn't buy. The number of fraudulent transactions is rapidly increasing as the frequency of transactions increases. Since credit card is the most popular mode of payment, the number of fraud cases associated with it is also rising.Thus, in order to stop these frauds we need a powerful fraud detection system that detects it in an accurate manner. Machine Learning and its algorithms can be used to solve such issues.In this paper we have explained the concept of frauds related to credit cards.Here we implement different machine learning algorithms on an imbalanced dataset such as Decision Tree,XGBoost,random forest with ensemble classifiers using boosting technique With Credit Card Fraud Detection, this project aims to demonstrate the modelling of a data set using machine learning. Modeling prior credit card transactions with data from those that turned out to be fraudulent is part of the Credit Card Fraud Detection Problem. The model is then used to determine whether or not a new transaction is fraudulent. Our goal is to detect 100% of fraudulent transactions while reducing the number of inaccurate fraud classifications. Credit Card Fraud Detection is an example of a common classification sample. This Project is focused on credit card fraud detection in real world scenarios. Nowadays credit card frauds are drastically increasing in number as compared to earlier times. Criminals are using fake identity and various technologies to trap the users and get the money out of them. Therefore, it is very essential to find a solution to these types of frauds. In this proposed project we designed a model to detect the fraud activity in credit card transactions. This system can provide most of the important features required to detect illegal and illicit transactions. As technology changes constantly, it is becoming difficult to track the behavior and pattern of criminal transactions. To come up with the solution one can make use of technologies with the increase of machine learning, artificial intelligence and other relevant fields of information technology; it becomes feasible to automate this process and to save some of the intensive amounts of labor that is put into detecting credit card fraud. Initially, we will collect the credit card usage data-set by users and classify it as trained and testing dataset using a random,XGBoost, forest algorithm and decision trees. Using this feasible algorithm, we can analyze the larger data-set and user provided current data-set.The results is indicated concerning the best accuracy for Random Forest are unit 98.6% respectively.

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