Credit Card Fraud Detection Using Machine Learning with Python
Abstract : 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. Machine Learning and its algorithms can be used to solve such issues. 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. On the PCA converted Credit Card Transaction data, we concentrated on evaluating and pre-processing data sets, as well as deploying different anomaly detection techniques such as the Local Outlier Factor and Isolation Forest algorithm.
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. I
Previous research has explored a plethora of methods to tackle fraud detection, ranging from supervised and unsupervised approaches to hybrid ones.
Consequently, under- standing the technologies associated with credit card fraud detection and gaining insights into various fraud types have become imperative.
Over time, the evolution of fraud patterns has introduced novel forms of fraudulent activities, thereby piquing the interest of researchers
Fraud detection is a problem that applies to a variety of businesses, including banking and finance, insurance, government agencies, and low enforcement, among others. Millions of transactions can be searched using advanced data mining algorithms to find patterns and detect fraudulent transactions. Credit card fraud detection is a technique for detecting fraudulent credit card transactions and preventing customers from being charged for products they did not purchase.
The major goal of our project is to make Credit Card Fraud Detection more accessible to people who are victims of credit card online fraud. The primary goal of a credit card fraud detection system is to keep our transactions and security safe. Fraudsters won't be able to make repeated transactions on a stolen or counterfeit card before the cardholder notices the fraudulent activity with this approach. 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.
This paper provides an extensive review of prior work, tracing the evolution of credit card fraud detection meth- ods and underscoring the pivotal role of machine learning in advancing this field
We delve into the intricacies of data preprocessing, highlighting the significance of ade- quately preparing datasets to extract meaningful insights. Additionally, we outline our methodology, encompassing the selection of machine learning algorithms, feature engineering, and the development of an ensemble-based ap- proach that amalgamates the strengths of multiple models.
Our findings not only demonstrate the accuracy of our machine learning models but also emphasize the importance of striking a balance between fraud detection and minimizing false positives, a critical consideration in the financial sector
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