The document discusses fraud detection strategies, highlighting the various sources of fraud detection such as internal audits and suspicions. It emphasizes the importance of data analysis, including the use of analytics, data cleansing, and various statistical techniques to identify and predict fraudulent activities. The content also covers best practices for managing e-business transactions in order to minimize fraud risk.

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