In the data-driven era, applications that generate vast amounts of data have become a central focus. The exponential growth in data generation poses significant challenges in data analysis. Financial transactions, in particular, have become increasingly complex, necessitating effective methods for detecting anomalies. Unnoticed irregularities can lead to substantial problems for banks and other financial institutions, including financial losses and eroded trust. In many real-world applications, dealing with imbalanced data is a critical concern. While most classification methods focus on two-class data problems, addressing a solution for class-imbalanced scenarios is equally essential. This work proposes a methodology that applies the SMOTE algorithm to various Machine Learning (ML) and Deep Learning (DL) models, aiming to balance the imbalanced data and improve classification performance. In the practical comparison of various ML and DL models with and without the SMOTE technique, this work also experimentally examines and then discusses the challenges in identifying fraudulent transactions over two different financial transactional datasets. Finally, by using SOMTE with various ML and DL models, this work presents 37% – 91% improvement on the banking transactional dataset and 57%-98% improvement on the online shopping transactional dataset in terms of F1-Score.