Cost-Sensitive Thresholding Over a Two-Dimensional Decision Region for Fraud Detection

Abstract

Fraud in credits is a difficult to detect growing menace, whose results depend on the amount, so a cost-sensitive perspective must be taken. Classical approaches reduce to a fraud probability estimation and a decision threshold selection without considering the amount or considering it but neither explicitly nor examining the aggregated losses on the sample, leading to sub-optimal strategies. A new thresholding approach is proposed to solve these drawbacks and minimize aggregated losses, based on the construction of a two-dimensional decision space using any estimated fraud probability and the credit amount. This expansion allows more freedom for the optimal decision making rule search, which is performed with a new algorithm. The proposed method generalizes previous approaches, so an improvement is consistently achieved. This is shown in a study of two real data sets, comparing the results obtained by a wide range of classifiers.

Keywords: Cost-sensitive classification, Instance-dependent classification, Thresholding, Fraud detection, Risk analysis, Decision region