FraudPredictor: A Real-Time Transaction Analysis Machine Learning Model

The main issue facing the digital banking sector at present is the double-edged sword of using transactions that can be redeemed and credit cards that are fraudulent. In order to be fraud-free, this paper suggests a two-tier AI system, which uses the Random Forest algorithm and is able to give very quick feedback on suspicious transactions. The system checks user habits, transaction types, and amounts that are off the norm to make a decision whether a transaction is legit or a hoax. It has a modular structure, which includes among others, the steps of data preparation, construction of relevant features and execution of the model learning phase, followed by result validation and final system deployment, all using cost-efficient tools like Python and Flask. The system also offers very clear visualizations of the results that enhance comprehension of the findings. The study addresses the issues of online detection and proposes the development of adaptive learning and hybrid methods as the next step during evolution of fraud discovery.