Analysis of Credit Card Fraud detection using Machine Learning models on balanced and imbalanced datasets (original) (raw)
With the advent of modern transaction technology, many are using online transactions to transfer money from one person to another. Credit Card Fraud, a rising problem in the financial department goes unnoticed most of the time. A lot of research is going on in this area.The Credit Card Fraud Detection project is developed to spot whether a new transaction is fraudulent or not with the knowledge of previous data. We use various predictive models to ascertain how accurate they are in predicting whether a transaction is abnormal or regular. Techniques like Decision Tree, Logistic Regression, SVM and Naïve Bayes are the classification algorithms to detect non-fraud and fraud transactions. In modern conditions where data may vary in a matter of minutes or even seconds, conventional classification techniques may not perform well. When dataset involves huge numbers of differences in data distribution and also changing data with high dimensionality and volume issues supervised learning comes up short. Hence we may resort to unsupervised learning, semi-supervised or any other means to cope with that. The number of online transactions has grown enormously these days and credit card transactions hold an enormous share of these transactions. More numbers of people are using a credit card for shopping, e-commerce, e-wallets and even for education purposes. Therefore, banks and other stakeholders give fraud detection applications priority and value. Fraudulent transactions can be in different categories. They may be through Online or Offline. Our paper deals with the online category and one of many methods to handle them, which is the machine learning way.
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