Paper Title
Probabilistic Credit Card Fraud Detection System in Online Transactions

This paper discussed the past works on fraud detection system and highlights their deficiencies. A probabilistic based model was proposed to serve as a basis for mathematical derivation for adaptive threshold algorithm for detecting anomaly transactions. The model was optimized with Baum-Welsh and hybrid posterior-Viterbi algorithms. A credit card transactional data was simulated, trained and predicted for fraud. Four profiles are considered viz; (55 35 10), (70 20 10), (95 3 2) and (34 33 33). And finally, the proposed model was evaluated with different metrics namely; True-positive, falsepositive, accuracy and ROC curve. The results showed that with the optimization of parameters, posterior-Viterbi cum new detection model performed better than Viterbi cum old detection model. Keywords- Probabilistic, Baum-Welsh, HMM, posterior-Viterbi, fraud detection, Optimization