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Journal ArticleDOI

Fraud Detection in Telecommunications: History and Lessons Learned.

Richard A. Becker, +2 more
- 01 Feb 2010 - 
- Vol. 52, Iss: 1, pp 20-33
TLDR
The history of fraud detection at AT&T is reviewed, one of the first companies to address fraud in a systematic way to protect its revenue stream and the use of simple, understandable models, heavy use of visualization, and a flexible environment are advocated.
Abstract
Fraud detection is an increasingly important and difficult task in today’s technological environment. As consumers are putting more of their personal information online and transacting much more business over computers, the potential for losses from fraud is in the billions of dollars, not to mention the damage done by identity theft. This paper reviews the history of fraud detection at AT&T, one of the first companies to address fraud in a systematic way to protect its revenue stream. We discuss some of the major fraud schemes and the techniques used to address them, leading to generic conclusions about fraud detection. Specifically, we advocate the use of simple, understandable models, heavy use of visualization, and a flexible environment and emphasize the importance of data management and the need to keep humans in the loop.

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Citations
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Book ChapterDOI

An Overview of Concept Drift Applications

TL;DR: This chapter provides an application oriented view towards concept drift research, with a focus on supervised learning tasks, and constructs a reference framework for positioning application tasks within a spectrum of problems related to concept drift.
Journal ArticleDOI

A principle component analysis-based random forest with the potential nearest neighbor method for automobile insurance fraud identification

TL;DR: In this paper, individual classifiers are appropriately combined and a multiple classifier system with an increase in classification accuracy is presented and a new voting mechanism based on Potential Nearest Neighbor is presented to replace the traditional majority vote.
Book ChapterDOI

Inferring Strange Behavior from Connectivity Pattern in Social Networks

TL;DR: A complete graph from a large who-follows-whom network is studied and it is discovered that the lockstep behavior on the graph shapes dense “block” in its adjacency matrix and creates “ray" in spectral subspaces.
Journal ArticleDOI

Social engineering in cybersecurity: The evolution of a concept

TL;DR: It is argued that while the term began its life in the study of politics, and only later gained usage within the domain of cybersecurity, these are applications of the same fundamental ideas: epistemic asymmetry, technocratic dominance, and teleological replacement.
Proceedings ArticleDOI

SoK: Fraud in Telephony Networks

TL;DR: The taxonomy differentiates the root causes, the vulnerabilities, the exploitation techniques, the fraud types and the way fraud benefits fraudsters and uses CAller NAMe (CNAM) revenue share fraud as an example to illustrate how the taxonomy helps in better understanding fraud and to mitigate it.
References
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Journal ArticleDOI

Forecasting Sales by Exponentially Weighted Moving Averages

TL;DR: The paper presents a method of forecasting sales which has these desirable characteristics, and which in terms of ability to forecast compares favorably with other, more traditional methods.
Journal ArticleDOI

Statistical Fraud Detection: A Review

TL;DR: This work describes the tools available for statistical fraud detection and the areas in which fraud detection technologies are most used, and statistics and machine learning provide effective technologies for fraud detection.
Journal ArticleDOI

Adaptive Fraud Detection

TL;DR: This paper uses a rule-learning program to uncover indicators of fraudulent behavior from a large database of customer transactions, which are used to create a set of monitors, which profile legitimate customer behavior and indicate anomalies.
Journal ArticleDOI

Minimum Hellinger distance estimates for parametric models

Rudolf Beran
- 01 May 1977 - 
TL;DR: In this paper, the authors define and study a parametric estimation procedure which is asymptotically efficient under a specified regular parametric family of densities and is minimax robust in a small Hellinger metric neighborhood of the given family.
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