2 Credit Card Fraud Detection using Hidden Markov ... - Wiphala.net

This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTINGTable IV. Variation of TP and FP with different sequence lengthsThresholdTP averaged over all the 6 states fordifferent sequence lengthsFP averaged over all the 6 states fordifferent sequence lengths(%) 5 10 15 20 25 5 10 15 20 2530 0.52 0.56 0.64 0.58 0.6 0.05 0.05 0.05 0.05 0.0550 0.54 0.54 0.63 0.57 0.6 0.03 0.05 0.04 0.05 0.0570 0.50 0.60 0.60 0.61 0.59 0.04 0.04 0.05 0.05 0.0590 0.42 0.52 0.59 0.58 0.57 0.02 0.04 0.05 00.05 0.05Table V. Variation of TP and FP with different number of statesThresholdTP averaged over all the 5 sequencelengths for different no. of statesFP averaged over all the 5 sequencelengths for different no. of states(%) 5 6 7 8 9 10 5 6 7 8 9 1030 0.56 0.59 0.55 0.60 0.61 0.55 0.06 0.04 0.05 0.05 0.04 0.0450 0.57 0.60 0.52 0.56 0.59 0.59 0.05 0.04 0.05 0.05 0.05 0.0470 0.57 0.59 0.56 0.58 0.56 0.62 0.04 0.05 0.04 0.05 0.04 0.0590 0.56 0.51 0.60 0.53 0.49 0.55 0.03 0.04 0.03 0.04 0.04 0.04In both Tables IV and V, the highest value of TP as well as the lowest value of FP has beenhighlighted for each row. It is seen from the two tables that FP shows a clear trend of decreasingwith higher threshold and smaller sequence lengths. However, the number of states does not havea strong influence either on TP or on FP. From Table IV it is seen that TP is high for sequencelength 15 in 75% of the cases. Also, fraud detection time increases linearly with the sequence21