Example-Dependent Cost-Sensitive Credit Card Fraud Detection

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Information about Example-Dependent Cost-Sensitive Credit Card Fraud Detection
Technology

Published on March 23, 2014

Author: albahnsen

Source: slideshare.net

Description

Credit card fraud is a growing problem that affects card holders around the world. Fraud detection has been an interesting topic in machine learning. Nevertheless, current state of the art credit card fraud detection algorithms miss to include the real costs of credit card fraud as a measure to evaluate algorithms. In this paper a new comparison measure that realistically represents the monetary gains and losses due to fraud detection is proposed. Moreover, using the proposed cost measure a cost sensitive method based on Bayes minimum risk is presented. This method is compared with state of the art algorithms and shows improvements up to 23% measured by cost. The results of this paper are based on real life transactional data provided by a large European card processing company.

Example-Dependent Cost-Sensitive Credit Card Fraud Detection March 21st, 2014 Alejandro Correa Bahnsen with Djamila Aouada, SnT Björn Ottersten, SnT

Introduction € 500 € 600 € 700 € 800 2007 2008 2009 2010 2011E 2012E Europe fraud evolution Internettransactions(millions of euros) 2

Introduction $- $1.0 $2.0 $3.0 $4.0 $5.0 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 US fraud evolution Online revenue lost due to fraud (Billions of dollars) 3

• Increasing fraud levels around the world • Different technologies and legal requirements makes it harder to control • Lack of collaboration between academia and practitioners, leading to solutions that fail to incorporate practical issues of credit card fraud detection: • Financial comparison measures • Huge class imbalance • Response time measure in milliseconds Introduction 4

• Introduction • Database • Evaluation • Bayes Minimum Risk • Experiments • Probability Calibration • Other applications • Conclusions & Future Work Agenda 5

Simplify transaction flow Fraud?? Network 6

Data • Larger European card processing company • Jan2012 – Jun2013 card present transactions • 1,638,772 Transactions • 3,444 Frauds • 0.21%Fraud rate • 205,542 EUR lost due to fraud on test dataset Jun13 May13 Apr13 Mar13 Feb13 Jan13 … … … Mar12 Feb12 Jan12 Test Train 7

• Raw attributes • Other attributes: Age, country of residence, postal code, type of card Data TRXID Client ID Date Amount Location Type Merchant Group Fraud 1 1 2/1/12 6:00 580 Ger Internet Airlines No 2 1 2/1/12 6:15 120 Eng Present Car Rent No 3 2 2/1/12 8:20 12 Bel Present Hotel Yes 4 1 3/1/12 4:15 60 Esp ATM ATM No 5 2 3/1/12 9:18 8 Fra Present Retail No 6 1 3/1/12 9:55 1210 Ita Internet Airlines Yes 8

• Derived attributes Data Trx ID Client ID Date Amount Location Type Merchant Group Fraud No. of Trx – same client– last 6 hour Sum – same client– last 7 days 1 1 2/1/12 6:00 580 Ger Internet Airlines No 0 0 2 1 2/1/12 6:15 120 Eng Present Car Renting No 1 580 3 2 2/1/12 8:20 12 Bel Present Hotel Yes 0 0 4 1 3/1/12 4:15 60 Esp ATM ATM No 0 700 5 2 3/1/12 9:18 8 Fra Present Retail No 0 12 6 1 3/1/12 9:55 1210 Ita Internet Airlines Yes 1 760 By Group Last Function Client None hour Count Credit Card Transaction Type day Sum(Amount) Merchant week Avg(Amount) Merchant Category month Merchant Country 3 months – Combination of following criteria: 9

Date of transaction 04/03/2012 - 03:14 07/03/2012 - 00:47 07/03/2012 - 02:57 08/03/2012 - 02:08 14/03/2012 - 22:15 25/03/2012 - 05:03 26/03/2012 - 21:51 28/03/2012 - 03:41

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