Fraud Detection in Banking Transactions Using Ensemble Learning
Saravanan Kishan, Kavya Alluru · 2023
The banking and financial industry has been expanding and technological advancements have played a large role. This gave rise to a larger number of frauds in banking transactions as no security is bullet-proof. This causes panic among the users and thus hampers the usage of online banking techniques. Thus, it is of utmost importance that these frauds are detected and prevented. Machine learning has helped to solve various real-world problems and it will play a major role in rectifying this issue as well. Together with machine learning algorithms, ensemble learning will help to construct hybrid algorithms that can tackle this problem more efficiently. This work will be executed on both a real and synthetic bank dataset. Several algorithms will be applied to both datasets individually and then based on the initial results obtained, hybrid algorithms will be devised using stacking. Through this methodology, a powerful and efficient technique will be formed to detect fraud in banking transactions.