Discovery of fraud rules for telecommunications—challenges and solutions

Abstract

Many fraud analysis systems have at their heart a rule-based engine for generating alerts about suspicious behaviors. The rules in the system are usually based on expert knowledge. Automatic rule discovery aims at using past examples of fraudulent and legitimate usage to find new patterns and rules to help distinguish between the two. Some aspects of the problem of finding rules suitable for fraud analysis make this problem unique. Among them are the following: the need to find rules combining both the properties of the customer (e.g., credit rating) and properties of the specific "behavior" which indicates fraud (e.g., number of international calls in one day); and the need for a new definition of accuracy: We need to find rules which do not necessarily classify correctly each individual "usage sample" as either fraudulent or not, but ensure the identification, with a minimum of wasted cost and effort, of most of the fraud "cases" (i.e., defrauded customers). These aspects require a special-purpose rule discovery system. We present as an example a two-stage system based on adaptation of the C4.5 rule generator, with an additional rule selection mechanism. Our experimental results indicate that this route is very promising.

A Comprehensive Survey of Fraud Detection Techniques

“…To better detect fraud two advanced data mining approaches are support vector machines and random forests, together with the well known logistic regression [20] [21]. Logistic regression (LR) is useful for situations in which it is wanted to be able to predict the presence or absence of a characteristic or outcome based on values of a set of predictor variables.…”

A Framework for Internal Fraud Risk Reduction at IT Integrating Business Processes: The IFR² Framework

“…Kirkos et al (2007), Fan (2004), Viaene et al (2002), Bonchi et al (1999), and Rosset et al (1999). Viaene et al (2002) actually apply different techniques in their work, from logistic regression, k-nearest neighbor, decision trees and Bayesian neural network to support vector machine, naive Bayes and tree-augmented naive Bayes.…”

Debt Detection in Social Security by Sequence Classification Using Both Positive and Negative Patterns

“…Rosset et al [18] studied the fraud detection in telecommunication and presented a two-stage system based on C4.5 to find fraud rules. They adapted the C4.5 algorithm for generating rules from bi-level data, i.e., customer data and behaviour-level data.…”