Data Analysis for Credit Card Fraud Detection

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Data Analysis for Credit Card Fraud Detection

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Description

Data Analysis for Credit Card Fraud Detection Alejandro Correa Bahnsen Luxembourg University Introduction Introduction Simplify transaction flow Fraud?? Network Introduction Database Evaluation of algorithms Logistic Regression Financial

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Data Analysis for Credit Card Fraud DetectionAlejandro Correa BahnsenLuxembourg University

Introduction

Simplify transaction flow Fraud?? Network

Introduction
Database
Evaluation of algorithms
Logistic Regression
Financial measure
Cost Sensitive Logistic Regression Agenda

Database Larger European card processing company

2012 card present transactions

750,000 Transactions
3500 Frauds
0.467% Fraud rate

148,562 EUR lost due to fraud on test dataset Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan Test Train

Raw attributes

Other attributes:
Age, country of residence, postal code, type of card Database 7

Derived attributes

Combination of
following criteria: Database 8

Evaluation Confusion matrix

Introduction
Database
Evaluation of algorithms
Logistic Regression
Financial measure
Cost Sensitive Logistic Regression Agenda

Model

Cost Function

Cost Matrix Logistic Regression

1% 5% 10% 20% 50% Logistic Regression Under sampling procedure 0.467% Select all the frauds and a random sample of the legitimate transactions.

Logistic Regression Results

Motivation

False positives carry a different cost than false negatives

Frauds range from few to thousands of euros (dollars, pounds, etc) Financial evaluation There is a need for a real comparison measure

Cost matrix

where: Financial evaluation Ca Administrative costs
Amt Amount of transaction i Evaluation measure

Logistic Regression Results Selecting the algorithm by F1-Score Selecting the algorithm by Cost

Logistic Regression Best model selected using traditional F1-Score does not give the best results in terms of cost

Model selected by cost, is trained using less than 1% of the database, meaning there is a lot of information excluded

The algorithm is trained to minimize the miss-classification (approx.) but then is evaluated based on cost

Why not train the algorithm to minimize the cost instead?

Cost Matrix Cost Sensitive Logistic Regression Cost Function

Cost sensitive Logistic Regression Results

Conclusion Selecting models based on traditional statistics does not give the best results in terms of cost

Models should be evaluated taking into account real financial costs of the application

Algorithms should be developed to incorporate those financial costs

Thank you!

Contact information Alejandro Correa Bahnsen
University of Luxembourg
Luxembourg

al.bahnsen@gmail.com

http://www.linkedin.com/in/albahnsen

http://www.slideshare.net/albahnsen

Data Analysis for Credit Card Fraud Detection

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