FlexiSecure - AI-Powered Fraud Detection with Real-Time Alerts and Customizable Security

The rapid expansion of online financial transactions has escalated the risk of fraud, posing significant challenges for banks and financial institutions, particularly in Sri Lanka. Traditional rule-based fraud detection systems often find it challenging to balance speed and accuracy, rendering them less effective against advanced and evolving fraud schemes. This study proposes a hybrid machine learning model combining decision trees and neural networks to enhance fraud detection performance. The model incorporates advanced preprocessing techniques, including feature scaling and outlier detection, to improve data quality. It employs supervised learning to achieve high accuracy and adapt to emerging fraud patterns. Additionally, explainable AI (XAI) is incorporated to ensure transparency in detection outcomes, where the user would be entrusted to make the final decision on the transaction, thereby fostering user trust and compliance with regulations in the financial sector. The model is designed to process large volumes of transactions efficiently, which demonstrates its scalability and suitability for high-volume banking environments.