Recognizing debit card fraud transaction using CHAID and K-nearest neighbor: Indonesian Bank case
Indrajani INDRAJANI, Yaya Heryadi, Lili Ayu Wulandhari, Bahtiar Saleh Abbas · 2016
This paper presents a preliminary study on debit card fraud transaction recognition, whose cards are issued by Indonesian bank, based on actual ATM transaction records. The premise of this research is fraudulent transaction contains ‘anomaly’ from the pattern of non-fraudulent transactions so that the anomalous pattern can be detected and separated at some point using classification models. Less availability dataset for research, non-stationary distribution of the data, highly imbalanced class distributions, and continuous streams of transactions become the main driven of using CHAID and k-NN classification method. Empiric result using actual debit card transaction using ATM services shows that Accuracy of CHAID model is 0.8 and F = 0.7; and k-NN model (for k=3) is 0.7 and F = 0.6 These results are comparable to previous studies using Hidden Markov Models.