Credit Card Fraud Detection Using Machine Learning
Abstract : The purpose of this project is to detect the fraudulent transactions made by credit cards by the use of machine learning techniques, to stop fraudsters from the unauthorized usage of customers’ accounts.
The increase of credit card fraud is growing rapidly worldwide, which is the reason actions should be taken to stop fraudsters.
Putting a limit for those actions would have a positive impact on the customers as their money would be recovered and retrieved back into their accounts and they won’t be charged for items or services that were not purchased by them which is the main goal of the project.
Detection of the fraudulent transactions will be made by using three machine learning techniques KNN, SVM and Logistic Regression, those models will be used on a credit card transaction dataset
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
Throughout the search I found that there were many models created by other researchers which have proven that people have been trying to solve the credit card fraud problem.
As stated before credit card fraud is increasing drastically every year, many people are facing the problem of having their credits breached by those fraudulent people, which is impacting their daily lives, as payments using a credit card is similar to taking a loan.
If the problem is not solved many people will have large amounts of loans that they cannot pay back which will make them face a hard life, and they won’t be able to afford necessary products, in the long run not being able to pay back the amount might lead to them going to jail
a proposed using KNN and Outlier detection in identifying credit card fraud, the authors found after performing their model over sampled data, that the most suited method in detecting and determining target instance anomaly is KNN which showed that its most suited in the detection of fraud with the memory limitation.
As for Outlier detection the computation and memory required for the credit card fraud detection is much less in addition to its working faster and better in online large datasets. But their work and results showed that KNN was more accurate and efficient.
proposed using Bayesian and Neural Network in the credit card fraud detection. Their results showed that Bayesian performance is 8% more effective in detecting fraud than ANN, which means that in some cases BBN detects 8% more of the fraudulent transactions.
In the Data understanding phase, it was critical to obtain a high-quality dataset as the model is based on it, the dataset was explored by taking a closer look into it which gave the knowledge needed to confirm the quality of the dataset, additionally to reading the description of the whole dataset and each attribute.
. It’s also important to have a dataset that contains several mixed transaction types “Fraudulent and real” and a class to clarify the type of transaction, finally, identifiers to clarify the reason behind the classification of 3 the transaction type.
I made sure to follow all of those points during the search for the most suited dataset.
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