Detecting fraud and anomalies in financial transactions is crucial in safeguarding institutional assets, maintaining regulatory compliance and ensuring customers trust in financial system. This study investigated methods of detecting frauds or anomalies in transactions within financial institutions, a vital task to prevent financial losses, reduce investigative costs, and comply with regulatory standards. The efficiency of Logistic Regression, Linear Discriminant analysis (LDA) and Quadratic Discriminant (QDA) statistical models were compared with a view of identifying fraudulent activity. Secondary data of over 280,000 financial transactions from an online website (kaggle) was used to evaluate each model based on accuracy, precision, and error rates, for both fraudulent and non-fraudulent classifications. The results indicated that Logistic Regression outperformed LDA, and QDA, achieving the highest accuracy and lowest error rate, making it the most effective model among the models considered for fraud detection.