This paper entails the comparison of four machine learning algorithms, namely Logistic Regression, Decision Tree, Random Forest, and XGBoost pertaining to loan approvals to facilitate the optimization of credit decisions made by the financial institution that shall be assisted through characteristics of applicants. This paper used the Kaggle dataset and also tried forward feature selection as well as hyperparameter tuning in an alternative way. All models are experimented; the best logistic regression test accuracy is up to 88.73%. Meanwhile, the decision tree as well as the random forest can be seen with obvious overfitting cases. XGBoost also showed a tendency towards overfitting, only a little margin from logistic regression. This Logistics Regression model will achieve overall performance which best strikes a balance between the requirements of high accuracy on the training data and generalization. The paper addresses also an issue, that fits in one area of trade-off, which is model complexity/interpretability—an extremely important issue in finance, because quite obviously, transparency is a necessity. Hence, overfitting has played a very vital role in this model; however, how it affects the former is very simple; Logistic Regression was to be a victim of overfitting at the cost of one being much more complex than the two other models namely, Random Forest and XGBoost models. Hence, the results add to the significance of model selection and validation on loan decisions taken in real-time. These results hint at a growing need for XAI since stakeholders should trust that this will be provided to an automated financial decision-making system with such transparency in place that this holds; further piling on the debate of balance in financial machine learning with better interpretability but potentially greater predictive performance in real applications.