The System for Detecting Credit Card Fraud Using Data Science and Machine Learning

Abstract

Due to the difficulty of detecting fraud in the credit card system in modern times, credit card theft has grown to be a big worry for banks. To solve this problem, machine learning is crucial in identifying credit card fraud in transactions. To foresee these transactions, banks use a range of machine learning techniques, gathering historical data and adding new variables to enhance prediction capabilities. To prevent credit card fraud, the suggested solution uses logistic regression to generate the classifier. To handle soiled data and guarantee high detection accuracy, a pre-processing stage is used. The mean-based technique and the clustering-based technique are two cutting-edge key tactics used in the preprocessing step to clean the data. They are frequently mistaken for legitimate methods that compare fraud and non-fraud data, although this is never sufficient to accurately detect fraud. This study demonstrates the use of machine learning to identify credit card fraud. The project Credit Card Fraud Detection demonstrates how to model a knowledge set using machine learning. The Credit Card Fraud Detection challenge includes credit card transaction modelling, which has previously been done with fraud transaction data. A new transaction will be checked by our system to see if it is fraudulent. Our goal is to identify every suspicious transaction with the least amount of false positives. Our algorithm will determine the possibility that the transaction is fraudulent.