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Financial fraud is a serious threat that is expanding effects on the financial sector. The use of credit cards is growing as digitization and internet transactions advance daily. Many plastic cards in circulation throughout the world are like a gold mine. Credit card losses are predicted to cost financial service providers $40 billion globally by 2027, up from $27.85 billion in 2018. The emergence of electronic transactions is partially to blame for this increase in losses. Imagine that 1.5 billion credit cards are currently in use in the US alone, with the average American having more than three cards. While there are an amazing 22.11 billion plastic cards in use worldwide. Recognising counterfeit credit card transactions is difficult, as it prevents credit card firms' consumers from being charged for goods they did not buy. The most common issues in today's culture are credit card scams. This kind of fraud typically happens when someone uses someone else's credit card details. Credit card fraud detection uses transaction data attributes to identify credit card fraud, which can save significant financial losses and affluence the burden on the police. The detection of credit card fraud has three difficulties: uneven data, an abundance of unseen variables, and the selection of an appropriate threshold to improve the models' reliability. This study employs a modified Logistic Regression (LR) model to detect credit card fraud in order to get over the preceding difficulties. The dataset sampling strategy, variable choice, and detection methods employed all have a significant impact on the effectiveness of fraud detection in credit card transactions. This study investigates logistic …