ATM Transaction Status Analysis and Anomaly Detection

Abstract

This article mainly studies ATM transaction feature
analysis and anomaly detection. Select the trading success
rate, transaction response time and other characteristic
parameters, analyze the relationship between transaction
response time and transaction success rate. After comparing
the fuzzy C-means clustering algorithm and the K-means
clustering algorithm, K-means clustering algorithm was used
to classify the transaction response time. Thendesign trading
anomaly detection program. The use of naive Bayesian
classifier for data classification can determine the alarm level.
And using Gaussian distribution and Laplace smoothing
calibration to increase model accuracy to reduce false alarm.
The use of MATLAB programming to get the following
result: When the transaction response time is between 0 ~
85.57, the system predicts to be successful. When the
transaction response time between 85.57 ~ 212.31, the system
predicts a warning. When the transaction response time
between 212.31 ~ 1007.8, the system predicts that the alarm.