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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.

“In [Enter your God Here] We Trust, all others must bring DATA” W.E. Deming

all rights reserved.

EY 2015

What exactly is Big Data? (and why is this relevant)

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?


EY 2015

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


EY 2015

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.


EY 2015

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.


EY 2015

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.


EY 2015

Times are changing

[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


EY 2015

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


EY 2015

Predictive Fraud Analytics

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.


EY 2015

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. !

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

Missed Opportunities and biggest Challenges

Technology Matters

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%


EY 2015

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


EY 2015

Secure the buy-in, execute the build

Improved results and recoveries,

compared to other not using FDA

Earlier Detection of misconduct,


More Cost-effective results,


Higher visibility to the board,


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


EY 2015

Key Takeaways

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Limited, a UK company limited by guarantee, does not provide

services to clients. For more information about our

organization, please visit ey.com.

© 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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big risks require big data thinking: how new technologies ... · top fraud risk concerns fraud...

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