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Information Security and the Law Seminar
Big Risks Require Big Data Thinking: How new technologies can detect and prevent fraud incidents.
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“In [Enter your God Here] We Trust, all others must bring DATA” W.E. Deming
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EY 2015
What exactly is Big Data? (and why is this relevant)
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Big Data, the weird and the wonderful
NBA teams are using it for improving
athlete performance.
Large scale retailers for predicting
buying habits
F1 teams for optimizing pit stops and
evaluating car performance in real
time.
Supply Chain and Logistics for
improving warehouse to shelf JIT
delivery
We will be using it for Forensic
Analysis and Corruption Risk
mitigation. BUT HOW?
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EY 2015
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The Three V’s of Big Data
Velo
city
Near Real Time
Real Time
Batch
Periodic
Frequent / Automated “Big data is high-volume, high-velocity and high-
variety information assets that demand cost-
effective, innovative forms of information processing
for enhanced insight and decision making”, Gartner
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EY 2015
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Forensic Data Analysis End to End
Understand and Collect
Structured and Unstructured data.
Data about Data is pivotal for using Big data.
Apply Data Analytics on assumed business understanding and with the right technology
Interpretation of trend analysis and exception reporting based on harnessed data.
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EY 2015
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Forensic Data Analysis Using big data
Useful insight
1
How to separate the noise from all that data? What do you need?
Identify relevant sources of data.
Databases, devices, social media etc
2 Extract information using the assistance
of Domain experts and data scientists.
3 Involve domain experts for the industry
in question. Define Key Fraud
Indicators and Metrics
4 Deploy the right technology to harness
the data using Key Fraud indicators.
5 Present or Visualize the information in
front of you.
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EY 2015
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Forensic Data Analysis Using technology to draw correlations
Correlate a document to an email to data gathered from an End User Device between them.
Draw relationships from unstructured data to transactional data entries in typical applications or systems. Process can start from an entry back to other sources.
In investigations – the primary purpose would be to draw a timeline of relevant events to the case.
In Proactive Forensic analysis – Use of Fraud indicators or metrics to proactively detect fraud.
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EY 2015
Times are changing
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[FACT] Regulatory scrutiny increases. UK (Bribery Act) and US (Foreign
Corrupt Practices Act FCPA) provide guidance with respect to an
effective compliance program. Adopting FDA procedures into the
monitoring and testing compliance can create a cycle of improved
adherence to company policy and improved fraud prevention and
detection.
Shifting Tectonic Plates of Regulation
466 Executives Interviewed as part of this survey
11 Markets From 9 different industries
Nov 2013 to
Jan 2014
Current regulatory landscape creates further impetus for new approaches
in Forensic Data Analysis.
Governments around the globe are strengthening anti-corruption and
enforcement continuous to grow. Some 40 member countries of the
Organization for Economic Co-Operation and Development (OECD),
including US, Russia, Brazil, Germany, France and the UK have adopted
the OECD Anti-Bribery Convention.
Forensic Data Analysis
Global Survey
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EY 2015
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Top Fraud Risk Concerns
Fraud incidents reported according to the Association
of Certified Fraud Examiners (ACFE) on their latest
report for 2013.
1300
Months is the median duration of a fraud before
detected
18
Of the fraud incidents committed resulted in over 1
million USD in losses
>20%
FDA Aligned well with perceived risk areas
74% Of the cases, FDA is used for detecting Bribery and corruption risk
62% Of the cases, FDA is used for detecting financial statement fraud.
Top risks
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EY 2015
Predictive Fraud Analytics
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Big Data has Big Potential
72 %
7 %
2 %
Believe big data can play a key role in fraud prevention and detection
Are actually using them
Are aware of any specific big data technologies
Increasing Data Volumes. IBM suggests that 90% of the worlds data has been generated over the past two years.
Convergence of unstructured data from social media, email, free-text.
Merge of unstructured data into traditional accounting analytics which historically rely on numerical information.
Challenge creates Opportunity
Model-based Mining and visual analytic tools
Sophisticated BAU prevention controls based on trend anomalies for stopping improper payments or business transactions
Integration of statistical analysis and text mining
Implementation of continuous monitoring detection / prevention framework
Faster event correlations and investigation of relevant facts.
Faster resolution of the truth using hard core evidence.
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EY 2015
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Why use Forensic Data Analysis: key benefits and adoption
FDA enhances the risk assessment process and improves
fraud detection
The top benefits reporting form the use of FDA are the ability
to “enhance our risk assessment process” followed by the
ability to detect potential misconduct that we couldn’t detect
before”. This sentiment is reflected in both C-suite and other
executives. !
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Forensic Data Analysis rollout within the organization
Who can Benefit the most
Common FDA Testing Area
Payment Stream, accounts
payable analysis Altered invoices, inflated process, suspicious payment
descriptions, requestor/approver conflicts
Common Fraud
Vendor master / employee
analysis and comparisons Fictitious vendors, vendor risk ranking, background
due diligence
Bribery & Corruption Bid Rigging, conflicts of interest, contract compliance,
kickbacks, illegal gratuities
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Missed Opportunities and biggest Challenges
Technology Matters
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Forensic Data Analysis Technology Maturity Model
of respondents use MS Excel as
the main FDA tool
Traditional focus on the upper left
quadrant handling structured rule-based
analytics
Use database tools such as MS
SQL Server
65%
43%
Ma
jori
ty
So
ph
isticate
d
Sophisticated Tools like
Hadoop
SAS, SPSS and
Strata
2%
11%
Forensic /Audit
Analytics like ACL
and IDEA
26%
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EY 2015
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Technology: the right tools for the right job
Use of Statistical and data-mining techniques for
predictive modelling, clustering and anomaly
detection.
Enable Continuous Monitoring and Real-time
detection
Highest Adopters
Power and Utilities Industry stands out as the highest adoption rate of FDA tools used for continuous monitoring and predictive analytics
41% Used for Continuous Auditing
34% Text Analytics
20% Statistical data Mining
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EY 2015
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Secure the buy-in, execute the build
Improved results and recoveries,
compared to other not using FDA
Earlier Detection of misconduct,
compared to other not using FDA
More Cost-effective results,
compared to other not using FDA
Higher visibility to the board,
compared to other not using FDA
Technology counts
11%
15%
14%
12%
!
1
5 Success Factors of FDA Integration
Focus on the low-hanging fruit: the
priority of the initial project matters
2 Integrate more advances FDA
technologies
3 Communicate early and broadly
4 Sustained FDA success relies on the
use of experienced, knowledgeable end-
users interpreting the results
5 BE PATIENT. Enterprise-wide
deployment takes time; does not
happen over night
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EY 2015
Key Takeaways
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Thank You all rights reserved.
EY 2015
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© 2015 Ernst & Young Cyprus Ltd. All rights reserved.
This material has been prepared for general informational
purposes only and is not intended to be relied upon as
accounting, tax, or other professional advice. Please refer to
your advisors for specific advice.
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