Financial fraud poses a significant threat to both consumers and financial institutions, leading to substantial financial losses and erosion of trust in the banking system. Credit card fraud, in particular, represents a prevalent form of financial crime due to the widespread use of credit cards for transactions. To combat this menace, machine learning techniques such as logistic regression coupled with advanced data analytics have emerged as powerful tools for detecting fraudulent activity involving transactions related to credit cards. This paper explores an application related to logistic regression, a widely used statistical method for binary classification, in the realm of financial fraud detection. Leveraging a dataset comprising historical characteristics of credit card transactions, including transaction amount, location, time, and user behavior are analyzed to identify patterns indicative of fraudulent behavior. The effectiveness of the suggested strategy is evaluated using actual world credit card transaction data, demonstrating its capacity to effectively differentiate between legitimate and dishonest business dealings. Results indicate that logistic regression model, in conjunction with data analytics techniques, offers a promising solution for detecting financial fraud in credit card transactions, thereby safeguarding the interests of consumers and financial institutions alike.