With the rise in online transactions and the growing complexity of fraud, it is crucial to create automated systems for detecting credit card fraud. This can help reduce financial losses and maintain consumer trust. This paper presents an improved framework for automated fraud detection that tackles important gaps in current research. It does this by comparing five dimensionality reduction techniques, optimizing hyperparameters, and assessing stability over time. A thorough baseline evaluation of 30-dimensional features shows that Random Forest has a performance accuracy of 93.92%. A systematic comparison with six algorithms supports the choice of Random Forest. Five dimensionality reduction methods are tested: Principal Component Analysis (PCA), Factor Analysis (FA), t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), and Autoencoders.These methods are applied to balanced, standardized transaction data to reduce dimensionality to 10 components. Hyperparameter optimization using RandomizedSearchCV addresses concerns about overfitting. A dual evaluation with stratified 5-fold cross-validation and 95% confidence intervals shows that Factor Analysis has better results. It achieves 94.26% accuracy and 94.59% recall while reducing dimensionality by 66.7%. This beats the baseline recall of 89.86% and PCA's recall of 89.19%. An analysis of concept drift shows that the model stays stable despite changes over time. Robustness tests under noise indicate that PCA is slightly better at handling noise, with 92.57% compared to 92.23% for Factor Analysis. The evaluation framework provides strong evidence that combining dimensionality reduction with improved ensemble learning offers a practical and scalable solution for automated financial fraud detection.