1. Leading compliance monitoring activities to assess fraud and
corruption risks
ACI China Anti-Corruption Summit
June 18, 2014

2. 11
1 Discussion: Top Compliance Issues
2 EY’s First Annual Global Forensic Data Analytics Survey
3 Leveraging Forensic Data Analytics (“FDA”) to Detect Fraud
4 Dashboarding & Visualization
5
Leveraging Statistical Analysis and Text Mining to Identify “Corrupt
Intent”
Agenda

3. 2
Discussion: Top Compliance Issues

4. 33
► Bribery and corruption remain top risks
► Regulatory pressure
► Third-party integrity
► M&A due diligence
► Risk areas include:
► Integrity of vendors, suppliers and distributors, government officials
► Improper payments in the forms of bribes or kickbacks
► Travel and entertainment abuse
► Conflicts of interests (e.g., employee and supplier matches)
Top issues— what we are seeing

5. 44
Start with the Fraud Tree
Fraud tree
Cash
larceny
Theft of
other assets
– inventory/
AR/
fixed assets
Revenue
recognition
Non
financial
Conflicts
of
interest
Bribery and
corruption/
FCPA
Illegal
gratuities
Bid-rigging/
procurement
Corruption Fraudulent statements
Asset misappropriation
Fake
vendor
Payroll
fraud
T&E
fraud
Theft of
data
GAAP Reserves
General focus of auditors
General focus of
internal auditors
General focus of attorneys
(opportunity for Internal Auditors and
Investigators)

6. 55
Frequent compliance examples
Social Media Monitoring Advanced Email Monitoring Mobil Devices
Meals & Entertainment Marketing & Events CRM and Sales Data
Information Security Employee Payroll Distributor & Margin Analysis
Capital Projects Education, Grants, Sponsorships
Emerging monitoring activities may include…
Vendor Payments / AP
Trading / AML
Vendor Due Diligence & Watchlist
Monitoring
Charity & Donations

7. 66
► Internal Audit
► Compliance & Legal
► Investigations
► Business / Operations
What we hear:
1. Make my program more effective and measurable
2. Make my program more efficient (reduced sample sizes, risk based, cost savings)
Now, more than ever, increased transparency is top-of-mind among our clients
in…

8. 77
How global companies are responding
► Compliance and legal are often teaming with internal audit to look beyond anti-corruption
policies and training and into tests of books and records
► Integrating new analytics specifically targeting corruption – these aren’t your typical rules-based,
process control SOX tests
► Integrating “Big Data” concepts including:
► Text mining (unstructured data)
► Statistical analyses and anomaly detection
► Visual analytics and interactive dashboards
► 100% data sampling, not just random sampling
► Analytics used to assess high fraud/corruption risk areas

9. 88
Compliance monitoring challenges
► The rapid pace of regulatory requirements requires a good compliance monitoring program to have the
flexibility to accommodate a continuously changing regulatory environment.
► ERP systems and enterprise data warehouses are often not integrated with other key systems related to
compliance (e.g., speaker programs, event management systems, sample management, promotion materials,
etc.).
► Many departments work in their own organizational silo which creates redundant efforts to meet
monitoring and reporting requirements.
► The volume of business activities that should be monitored can overwhelm the resources of most
organizations.
► Get the right FDA tools and the right people to operate FDA
► The data available for analysis are incomplete or inaccurate

10. 9
EY’s First Annual Global Forensic Data Analytics Survey

11. 1010
EY’s first annual global forensic data analytics survey
► This survey was conducted between October 2013 and December 2013 on behalf of EY’s Fraud
Investigation and Dispute Services practice (“FIDS”)
► Survey approach
► 446 companies surveyed, across 11 countries
► Respondents are executive and senior management responsible for anti-fraud and anti-corruption programs
► 45% of the companies generate $100 million to $1 billion in revenue, 55% - over $1 billion
► Over a dozen industries represented, with the largest shares held by financial services, Pharmaceutical, oil & gas,
utilities, and mining

12. 1111
► 75% of the companies surveyed use forensic data analytics (“FDA”)
► FDA includes a broad base of users, including corporate executive management (81%) and the board of
directors (65%)
► Triggers for using FDA are, as we would expect, businesses’ greatest concerns: bribery and corruption,
financial statement fraud and asset misappropriation
► FDA is seen as cost-effective and offering many benefits, primarily as a means of enhancing companies’
ability to detect fraud and misconduct
► FDA typically represents 2/5 of overall anti-fraud and anti-bribery program spend currently and this is
typically felt to be sufficient. However, over half predict an increase in spend on FDA in the next 3 years
Key findings

13. 1212
Key findings (cont.)
► 67% of respondents say their current anti-fraud and anti-bribery program is effective in preventing and
detecting fraud and corruption; however, 64% say they need to do more to improve their current
procedures, including the use of FDA
► 62% of respondents say they need to improve management’s awareness of the benefits of FDA and
proactive transaction monitoring
► Survey respondents reported the single largest challenge was getting the right FDA tools and a lack of
human resources or manpower to operate FDA
► Spreadsheets and database tools still dominate the technology landscape. There is a need to go beyond
traditional rules-based analytics by leveraging more sophisticated FDA technologies such as statistical
modeling, predictive analysis, visualization, and interactive dashboards

14. 13
4%
62%
63%
79%
82%
82%
82%
89%
90%
Other
Able to analyze non-structured data formats, alongside structured data formats to identify…
Cost effective
We can review a large amount of data in a shorter period of time
Earlier detection of misconduct
Assists in planning our audits or investigative field work
Offers better comparison of data for improved fraud risk decision-making
Able to detect potential misconduct that we couldn’t detect before
Enhances our risk assessment process
Total
4%
54%
57%
70%
80%
73%
79%
84%
86%
C-Suite
Main benefits of FDA

15. 14
61%
68%
70%
77%
81%
84%
Internal investigations or business integrity
Board of directors
Business unit managers
Legal/compliance
Corporate executive management
Internal audit
FDA benefits extend high into the organization

16. 15
Leveraging Forensic Data Analytics (“FDA”) to Detect Fraud

17. 1616
Source: ACFE 2010 Report to the Nations On Occupational Fraud
50% by tip or accident demonstrates the need
for improved analytics
2012 ACFE Report to the Nation on Occupational Fraud
How is fraud detected?

18. 1717
And it is not just a data warehouse.
Analytics are business driven and technology enabled.
Forensic Data Analytics is
The ability to collect and use electronically stored information, both structured
and unstructured data sources, to identify potentially improper payments,
patterns of behaviour and trends. Forensic data analytics encompasses
integrating continuous monitoring tools, analysing data in real time and allowing
for immediate action to prevent suspicious or fraudulent payments.
Forensic data analytics defined

19. 1818
Forensic data analytics maturity model
► EY developed an FDA maturity model that describes four key quadrants of FDA activity that span
both structured data sources, such as transactional data, and unstructured data sources, such as
free-text communications
► Upper-left quadrant: “traditional” rule-based queries
► Upper-right quadrant: statistical methods
► Bottom-left quadrant: simple keyword search
► Bottom-right quadrant: data visualization and text mining
A leading FDA practice incorporates elements of all four quadrants to ensure more effective
detection and fewer false positives.

20. 1919
False-positive rateHigh Low
Structured
data
Detection rateLow High
Unstructured
data
“Traditional” rule-based,
descriptive queries
and analytics
Matching, grouping,
ordering, joining, filtering
Statistical Analysis
Anomaly detection,
clustering, risk ranking,
predictive modeling
Traditional keyword searching
Keyword search
Data visualization
and text mining
Data visualization, drill down
into data, text mining
Forensic data analytics maturity model
Beyond traditional “rules-based queries” – consider all four quadrants

21. 2020
Gather Process Analysis Delivery/Follow up
ERP CRM
Contracts
Warehouse
manageme
nt
T&E
Other
• Obtain data from all central systems and
external sources.
• Load, validate and transform data into
define common model – independent of
ERP.
• Link sources to facilitate analysis.
• Provide global dashboards to facilitate
identification of risk issues.
• Deliver dashboards to be reviewed as part of
the testing process.
Below is an illustration of how a broad data collection exercise operates in practice. The objective is to gather data from a range of sources – and undertake initial
processing to provide a central team with the ability to identify the higher risk activities. Following that review, targeted analytics would be deployed to identify the
issues, transactions and relationships that need to be reviewed.
EY forensic data analytics workflow

22. 2121
Tailored design with data analytic risk indicators
High Risk Transactions
Duplicate Payments
Meal Splitting
Travel Agents
Overbilling
A%
B%
C%
D%
In-Scope Transactions
► Not every item bears the same risk level
► Define risk based on understanding of business process and potential control weaknesses
Risk indicator framework design

23. 2222
Why Continuous Monitoring?
► Executive visibility and transparency
► Drive process improvements
► More advanced anti fraud control
► Improved audit effectiveness
Enables Our Clients:
► Proactively identify and remediate transaction-related issues
and challenges
► Generate advanced analytics/insights
► Timely, accurate, complete reporting
EY’s approach to continuous transaction monitoring

24. 23
Dashboarding & Visualization

25. 2424
The dashboard tells you “who got paid what, where and what for”.
Data visualization: accounts payable monitoring

26. 2525
The 4W1H tell you “Who entertained who, where, what for, and
for how much?”
Data visualization: travel & entertainment monitoring

27. 2626
Filter by selected analyticsReview breaches on targeted analytics
Payment risk scoring
Key component to reducing false positives and focusing risk assessment

28. 2727
The dashboard tells you relationships identified through the analysis of structured and unstructured data sources.
Data visualization: social network analysis

29. 2828
Rather than simply comparing watch-list names to a vendor table in a spreadsheet, this example links accounts payable data to
third-party watch-list data to identify potentially improper payments to sanctioned or high-risk entities and displays the results
in an interactive dashboard.
Demonstrate management oversight & intent
Linking payment data to sanctions and watch list databases

30. 2929
Geocoding AP risk scores to identify hot regions.
Risk scoring and data visualization
Geocoded heat maps

31. 30
► Leveraging Statistical Analysis and Text Mining to Identify
“Corrupt Intent”

32. 3131
“<blank>”
Donation
Pay on behalf of
Special payment
Volume contract incentive
One time payment
Honorarium
Incentive payment
Friend fee
Nobody calls it “bribe expense”
Commission to the customer
Consulting fee
Government fee
Processing fee
Goodwill payment
Beyond just keyword searching, text mining within payment data plays a key role
in identifying potentially improper payments.
Focusing on payment text descriptions
What if you saw these terms used as justification for payments?

33. 3232
Text mining: vendor payment analysis
High risk terms linked to payment amounts

34. 3333
These three variables
were this highest drivers of
suspicious transactions
These variables were less important when
predicting suspicious transactions. Client should focus resources on
monitoring efforts for the three leading drivers, which accounts for 80%
of the predictive value.
Perform Variable
Analysis
Predictive modelling
Focus on the variables that matter most

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© 2013 Ernst & Young, China
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