Statistical data modeling Data preprocessing Matching algorithms Peer group outliers and covariance Time-series analysis

1. Fraud Detection
Hernan Huwyler
Madrid, Spain
Fraud Risk Forum
January 2013

2. Who detects fraud?
33%
18%
14% 13%
6%
• Fraud Risk
• Internal Audit Management
FRM • Suspicions
• Tipoff • By —chance” Transaction
Reporting
Controls
PwC's Global economic crime survey 2012

3. legitimate
record
legitimate
record
legitimate
record
fraudulent record
out of 9 system flags

4. Objective:
maximize correct predictions and maintain incorrect
predictions at an acceptable level

5. Data Analysis
Identify Analytics
Internal
Data
Apply Analytics to Data
Industry
Data
Leads Refine

6. Data Analysis 1
Branch A Branch C
Invigilation Branch B
HQ
Branch D
Business Branch A
BI
Branch C
Intiligence Branch B Branch D

7. Data Cleansing Algorithms
Poor initial data
conversion Factiva World Check
System
consolidations
World Compliance
Manual data entry
Interfaces and Customized data
baches cleansing rules

8. Demos
Vendors / Employees - Conflicts of interests
Vendor Activity – Sequentiality and fetching
Treasury – Unrecorded Payments
Treasury – Abnormalities and triangulations
Several sources of data
Combined attributes
Normalization

9. Some techniques
Statistical data modeling
Data preprocessing
Matching algorithms
Peer group outliers and covariance
Time-series analysis

10. Open discussions
Supervised, unsupervised & hybrid techniques
Quality data
Design, implementation and evaluation
Visualization tools
E-business transactions
Best practices