Published Paper Details:

The quick growth of digital technologies and the widespread use of online banking have made financial fraud more frequent. As more people use credit cards, mobile banking, and online shopping, it's harder for financial companies to identify fake transactions. Traditional systems that depend on fixed rules aren't effective against new and changing fraud techniques, which often causes too many false alerts and less efficient processes. In recent years, machine learning and deep learning have become key tools for detecting fraud. These methods can learn from large amounts of transaction data and adapt to new fraud tactics. This paper provides a detailed overview of different approaches used in financial fraud detection, including traditional methods like Logistic Regression, Naive Bayes, Decision Trees, and Random Forest, as well as more advanced models like Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks. The study compares these methods based on their performance, scalability, and ability to handle real-time situations. It also discusses the advantages of combining different models, especially ANN-LSTM setups, which are good at learning features and identifying patterns over time. Some major challenges mentioned include unbalanced data, high computing requirements, rapidly changing fraud trends, and the need for fast processing. The review shows that while traditional methods are simple and easy to understand, deep learning models are better at handling complex and evolving fraud situations. This paper is intended to help researchers and professionals by summarizing current techniques and identifying areas for future improvement in developing stronger and more effective fraud detection systems..