The document details a credit card payment fraud detection project from USC's Marshall School of Business, focusing on building supervised models to predict potential fraud transactions from a dataset of 95,007 records. It explores data patterns, feature selection, and various models, including logistic regression, XGBoost, and random forest, with performance metrics indicating high effectiveness in capturing fraud. The final analysis emphasizes the maximum return on investment achieved through effective fraud detection strategies.

1. Peipei Han
Marshall School of Business, USC
Card Payment Fraud Detection

2. Agenda
• Objective
• Data Exploratory
• Experts Variables
• Feature Selection
• Supervised Models
• Fraud Predictive

3. Predict potential fraud transactions
Build supervised model to generate fraud scores
Objective

4. Original Dataset
95,007 records of credit card payment transaction in 2010
10 variables including recordnum, cardnum, date, merchnum, merch
description, merch state,merch zip, transtype, amount and fraud
298 fraud labels
3 records from the original dataset:
Data Exploration

5. Data Exploration- Fraud Spatial Distribution
Fraud transactions Palo Alto, Seattle LA, Chicago, Savage, State College

6. Data Exploration – Amount & Seasonality
Different patterns
between fraud and normal transactions
Seasonality
Avoid when build variables

7. Totally 61 new numeric variables
Experts Variables

8. Experts Variables Cont.
Two dimension combination variables

9. Experts Variables Cont.
Last 7/3 days amount & count variables

10. HiveQL window function
The computation takes no more than 1 minute
Experts Variables Cont.

11. Lasso variable selection plot
Feature Selection
log(Best Lambda) = -7

12. Logistic Regression
Supervised Models
3% population capture 50% frauds

13. XGBoost
Supervised Models Cont.
3% population capture 91.94% frauds
Depth 30 Round 16

14. Random Forest
Supervised Models Cont.
3% population capture 94.63% frauds

15. Model Comparison
Supervised Models Cont.
Models FDR* at 3% FDR at 5% FDR at 50% FDR at 90%
Logistic Regression 50 52.68 86.91 98.99
XGBoost 91.95 93.29 96.98 100%
Random Forest 94.63 96.31 98.99 99.66
* FDR Fraud Detection Rate

16. Fraud Predictive
**False Positive Rate = # goods caught / # examined*False Positive Ratio = # goods caught / # bads caught

17. Fraud Predictive Cont.
Maximum ROI $146,440
($1,000,000)
($800,000)
($600,000)
($400,000)
($200,000)
$0
$200,000
$400,000
$600,000
$800,000
$1,000,000
$1,200,000
0 0.2 0.4 0.6 0.8 1 1.2
Fraud Savings Lost Sales ROI
Loss a
Fraud
Loss a
Sale
Cost per
Score
$600 $10 $0.05