Data mining techniques in detecting and predicting cyber crimes in banking sector
K. Chitra Lekha, Shanmugapriya Prakasam · 2017
Data mining applications are utilized in many banking sectors for client segmentation and productivity, credit scores and authorization, predicting payment default, advertising, detecting fake transactions, etc. This paper presents a general idea about the model of Data Mining techniques and diverse cyber crimes in banking applications. It also provides an inclusive survey of competent and valuable techniques on data mining for cyber crime data analysis. The objective of cyber crime data mining is to recognize patterns in criminal manners in order to predict crime anticipate criminal activity and prevent it. This paper implements a novel data mining techniques like K-Means, Influenced Association Classifier and J48 Prediction tree for investigating the cyber crime data sets and sorts out the accessible problems. The K-Means algorithm is being utilized for unsupervised learning cluster within influenced Association Classification. K-means selects the initial centroids so that the classifier can mine the record and formulate predictions of cyber crimes with J48 algorithm. The collective knowledge of K-Means, Influenced Association Classifier and J48 Prediction tree tends certainly to afford a enhanced, incorporated, and precise result over the cyber crime prediction in the banking sectors. Our law enforcement organizations require to be adequately outfitted to defeat and prevent the cyber crime.