Abstract The Data Mining techniques for the detection of Financial Fraud The Data Mining techniques in the area of Fraud Detection was examined and categorized into four categories of financial fraud securities and commodity fraud insurance fraud bank fraud and other financial fraud and six classes of data mining techniques in the field of financial fraud regression classification clustering outlier detection prediction and visualization The Data Mining approach have been applied wide ranging to the detection of credit card fraud although insurance fraud and corporate fraud have also attracted a skillful trade of attention in recent years In difference we find a distinct lack of research on money laundering securities and commodities fraud and mortgage fraud The major data mining techniques used for FFD are neural networks logistic models decision trees and the Bayesian belief network all of which them provide key solutions to the problems inherited in the detection and in the classification of fraudulent data Introduction The Association of Certified Fraud Examiners ACFE defined fraud as The use of one's profession for personal enrichment through the deliberate misuse or application of the employing organization s resources or assets In the technological systems fraudulent activities have occurred in many areas of daily life such as mobile communications telecommunication networks
E commerce and online banking Fraud detection includes identification of fraud as fast as possible once it has been accomplished Fraud detection methods are continuously designed to defend criminals in reinventing to their strategies The development of new fraud detection methods is made more difficult due to the severe limitation of the exchange of ideas in fraud detection At current fraud detection has been applied by a number of methodology such as artificial intelligence statistics and data mining Data Mining Techniques In Fraud Detection 1 Credit Card Fraud Detection The Credit Card Fraud Detection Challenge involves replication of past credit card transactions with the expertise knowledge of the ones that turned out to be fraud This replica is then used to distinguish whether a new transaction is fraudulent or not Our goal here is to determine 100 of the fraudulent transactions while minimizing the inaccurate fraud classifications Credit card fraud detection is fully confidential and is not much reveal in open Large span data mining techniques can enhance the state of the art in commercial exercise Scalable techniques to examine heavy amounts of transaction data that effectively calculate fraud detectors in a timely manner is a major problem specifically for e commerce Besides efficiency and scalability the fraud detection task presents technical problems that include skewed division of training data and non uniform cost per error both of which have not been widely studied in the knowledge discovery and data mining network Following are the techniques that detect credit card fraud detection
1 1 Outlier Detection 1 2 Neural Networks 2 Computer Intrusion Detection An intrusion detection system is required to perform and automate system observing by keeping aggregate survey trail statistics Intrusion detection techniques can be widely categorized into two categories depends on model of intrusions misuse and anomaly detection Misuse Detection Misuse detection trials to recognize the attacks of earlier observed intrusions in the form of a signature or a pattern for example recurring changes of directory or attempts to read a password file and forthrightly monitor for the occurrence of these patterns Misuse approaches include model based reasoning keystroke dynamics monitoring expert systems and state transition analysis Misuse detection is simple than Anomaly detection Anomaly detection Anomaly detection approaches include predictive pattern generation neural networks and statistical approaches The advantage of anomaly detection is that it is possible to find out novel attacks against systems Following are the techniques that detects computer intrusion detection 2 1 Expert Systems 2 2 Neural Networks 2 3 Model Based Reasoning 3 Telecommunication Fraud Detection Large amounts of data are being collected as a result of the large usage of mobile telecommunications Over a couple of time an individual person phone generates a largish pattern or signature of use While call data are measured for individual subscribers about the data indicative of fraudulent call signatures or patterns
Furthermore examining is thus needed to be able to isolate fraudulent use An unsupervised learning algorithm can examine and cluster call signatures or patterns for an individual subscriber in order to simplify the fraud detection process This research investigates the unsupervised learning potentials of two neural networks for the profiling of calls made by users over a couple of time in a mobile telecommunication network Our study provides a comparative examination and application of Long Short Term Memory LSTM and Self Organizing Maps SOM recurrent neural networks algorithms to user call data records in order to obtain a descriptive data mining on users call patterns Following are the techniques that detects telecommunication fraud detection 3 1 Rule Based Approach 3 2 Neural Network 3 3 Visualization Methods
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