Credit card fraud detection is the process of identifying and preventing unauthorized or fraudulent use of credit cards. It is a critical aspect of the financial industry, as it helps to safeguard both the cardholder and the card issuer from losses due to fraudulent activity.
Most modern solutions leverage artificial intelligence (AI) and machine learning (ML) to manage data analysis, predictive modeling, decision-making, fraud alerts and remediation activity that occur when individual instances of credit card fraud are detected.
In order to detect transactions that are fraud, here we use logistic regression as the transactions occure in binary format (0s and 1s). 0s indicate a normal transaction whereas 1s indicate the fraud credit card detections. Logistic regression works well with binary outcomes that is fraud or not fraud. It helps companies detect the pattern of fraudulent transactions or non-fraudulent transactions.
The image attached below indicates the sigmoid function used in logistic regression for the analysis of fraudulent transaction.
