Forensic Data Analytics
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EY India's forensic data analytic models are developed to identify variances in data sets, which may impact an organization’s profit and loss statement. Check out the evolution of forensic data analytics.

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Evolution of Forensic Data Analytics - EY India

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Evolution of forensic data analytics ey india 7441375

Forensic Data Analytics


Evolution of forensic data analytics ey india 7441375

Key to unlocking invisible information using forensic

“lookback”

Forensic Data Analytics as a topic and its adoption within the industry had long been overdue.

With the advent of technology and increasing incidents of fraud, there has been a significant rise

in adoption of Forensic Data Analytics. Due to this, company appointed auditors and independent

directors are now seeking to implement proactive fraud-prevention solutions and are avoiding post-

incident remediation processes.

Forensic Data Analytics is a science used to proactively seek opportunities to prevent and detect

fraud, waste and abuse by leveraging information in corporate data assets. It enables identification

of meaningful patterns and correlations in existing historic data to predict future events and assess

the reasons for various fraudulent activities. Such insightful predictive information is generally

“invisible,” but provides a platform on which organizations can take business decisions related to

fraud, disputes and misconduct.

The greatest value of forensic analytics is when it forces us

to notice what we did not expect to see.


Evolution of forensic data analytics ey india 7441375

Evolution of forensic

data analytics

Without big data analytics,

companies are blind and deaf,

meandering aimlessly like a

deer on freeway

day, and that number is doubling every

40 months

Big data is a reality:

The volume, variety and velocity of

data coming into the organization have

reached unprecedented levels. About

2.5 exabytes of data are created each

1. Big data

Torture the data, and it will

confess to anything

data warehousing techniques may

not be able to identify anomalies in

the existing data set thus preventing

proactive fraud management

Issues in managing big data:

Big data requires high performance

analytics to process billions of rows

of data with hundreds of millions of

data combinations. The traditional

2. Manage

data

Absence of forensic evidence

is not evidence of forensic

absence

Recent scams in the limelight:

In the recent times, India has been hit

with multi billion value scams associated

with the following:

Bribery and Corruption

3. Key Risk

Events

Procurement fraud and collusion in

bidding process

Accounting misstatement

Anti Money Laundering

Forensic analytics is the oil of

the 21st century which protects

organizations combustion

engine from going bust

4.

possible if existing data assets were

analyzed from forensic perspective to

avoid wrongful or criminal deception

intended to result in financial or

personal gain.

Adoption of forensic data analytics:

The associated risks could have been

mitigated if key stakeholders would

have paid attention to anomalies at

an earlier stage. This could have been

Forensic

Data

Analytics

How does forensic data analytics help organizations?

Proactive fraud prevention management

Controlling the magnitude of fraud in a reactive

set up

Effective and focused internal controls

Improving regulatory and compliance environment

3

Forensic Data Analytics


Evolution of forensic data analytics ey india 7441375

Capability landscape

Forensic Data Analytics can be used as a standalone

service or in conjunction with existing practices

such as investigations, audits, process review and

due diligence. In the current context, data exists

in structured (multiple form of databases) and

unstructured forms (emails, office documents,

presentations, Excel sheets, PDF files, archive files,

text and image files) in organizations. Using EY’s

proprietary tools, raw data can be transformed

into formats that can be analyzed, and with the

help of advanced analytical capabilities, anomalies

can be identified that may indicate potential fraud.

Some of our key offerings include, but not limited

to, identification of fraud in vendor, customer and

employee registration, procurement to pay, order to

cash, sales and distribution, travel and entertainment,

payroll disbursment.

Our forensic analytic models are developed to

identify variances in data sets, which may impact

an organization’s profit and loss statement. This

model also touches on various aspects, from simple

narration captured in a transaction to complicated

sentiments and tone analysis. It also includes data

within applications and data recorded on social and

professional networks for further analysis. This analysis

helps a company to move beyond identification of low

value pilferage to implementing controls on existing

and potential weak areas. Any dataset in historic, near

real time and real time form can be assimilated through

big data solutions to help a company improve its

bottom line by checking fraudulent activities

Unstructured data

Forensic data analytics

Sample Dashboard

Debit/Credit

Credit

Journal Entries and Amount Per User

Amount per Account Name

User_ID

EY_Account_Name

NL trading account NX

Debit

60,565,488

79,779

200M

EY_Class

Assets

Cost of Revenue

Expense

Expenses

Functional Transfer Ac..

Liabilities & Stockholde..

Local Legal Accounts (..

Other income and ded..

EY_Account_Name

Product/Program Rela..

Purchases not capitali..

(G)/L on Sales of Equip..

13th Month Salaries #1

A&P - Customer Events

A&P - Trade shows

A&P Collaterals - Prod..

A/P - Credit out of Debi..

EY_TIME_TAG

After office hours entries

Within Office hours ent..

Trade Rec'bles - Receivables

58,874,500

Agency Billing Settlement Ac..

Distinct count of Journal_ID

FSMA Revenue - Other Disc..

50K

50,276

Rental Rev. - Short Term Re..

100M

Amount

-60M

-40M

-20M

0M

20M

40M

60M

Amount

9,131

Amount per Sub Category

Structured output

2,541

1,864

716

333

291

4,869

0M

0K

2,3933,705

91

34

EY_Sub_Category

4

2

Accounts Receivable: Trade

58,874,500

Other Current Assets: Miscel..

Due to (from) Trade and Oth..

-67,944,770

67,944,770

RVBEUSEKOM

CLABRAVEGA

SKAYA

BJANKI

CKLEIN

GGOOSEN

ANVSCHAIK

AKLERK

PWENNEKES

TKOPPENS

TSMITS

BATCHUSER

NWINTER

JHAMAKER

SAMEIER

Rental Revenue: Rental Agr..

Deferred Rental Revenue

Deferred Revenue Managed..

-60M

-40M

-20M

0M

20M

40M

60M

Amount

Account name per month (Calculated Based on Document Number)

Effective_Date

2011

2012

EY_Entry_week_day

Sunday

Monday

Tuesday

Wednesday

Thursday

Friday

Saturday

Account Name

July

August

September

October

November

December

January

February

Agency Billing Settlement Ac..

-393,707

-192,406

-254,811

Agency Billing Settlement Ac..

Billing Settlement A (165799..

-943,525

-615,525

-274,752

-83,159

Deferred Rev. - FM (213009..

-620,377

-454,933

Deferred Rev. - FSMA (2130..

Deferred Revenue - Rental (..

FM Rev. - Additional Sales (..

-801,576

EY_Entry_month_end

No

Yes

FM Rev. - FSMA Rev. Varia..

-10M

0M

10M

-10M

0M

10M

-10M

0M

10M

-10M

0M

10M

-10M

0M

10M

-10M

0M

10M

-10M

0M

10M

-10M

0M

10M

Amount

Amount

Amount

Amount

Amount

Amount

Amount

Amount

Figure 1: Structured output from unstructured data

4

Forensic Data Analytics


Evolution of forensic data analytics ey india 7441375

Our key differentiator in forensic

data analytics

At EY, data analytic techniques applied to internal or

external fraud follows a four pillar approach — WHO-

WHAT-WHEN- WHY. This approach looks at any

situation from all possible angles and highlights key

issues. This does not only help in managing risks, but

also in identification of potential growth areas.

The key to identify fraud lies in the ability to

comprehend what lies beneath.

Increasing concerns about fraud and vulnerability can

be alleviated by a range of forensic techniques, some

of which are presented below.

Link Analysis

Employee group

Link Analysis is a data-analysis technique used to

evaluate relationships (connections) between nodes,

including organizations, people and transactions. Key

applications of this technique include analysis of EPBX

data, mobile bills and user logical access records that

help a company map its user footprint.

Employee-

vendor nexus

In a recent incident in a manufacturing company, its

phone records were analyzed across different zones

to determine the nexus between its employees and

selected vendors on procurement and disposal of

scrap. Using Link Analysis, we were able to establish

“hidden” relationships and information leakage from

suspected employees to identified vendors for possible

“kickbacks.”

The size and width of

connectors indicate

frequency of the calls

Third party

Vendor group

Figure 2: Link Analysis

5

Forensic Data Analytics


Evolution of forensic data analytics ey india 7441375

Social Network Analysis

Social Network Analysis views relationships in terms

of network theory, which consists of nodes and ties.

Nodes represent individual “actors” within the network

and Ties represent relationships between individuals,

e.g., friendships, kinship, organizational position, etc.

Social Network Analysis, along with Link Analysis,

helps to identify related parties, conflict of interest, bid

rigging, among other fraud.

India lead managing business throughout

India through relatives as key distributors

In a large consumer products company, the India lead

had appointed his relatives as distributors, and through

known vendors, managed distribution of products in

key states. Social Network Analysis, followed with a

background check, helped to reveal the nexus. This

led to a full-blown investigation and the company now

undergoes vendor due diligence before it carries out

any business.

India sales head

Vendor network in east

Vendor network in west

Relatives as key distributors

Vendor network in south

Vendor network in north

Figure 3: Social Network Analysis

Concept Clustering

Concept Clustering involves grouping similar entities

or behavior into tight semantic clusters for the purpose

of identifying anomalies or red flag. It is used actively,

along with an electronic data review. In this example,

Concept Clustering was executed on more than a

million documents to identify all the information with

terms such as “gifts,” “incentive” and “facilitation.”

We were able to bring these down to a sizable volume

with the required criteria that was analyzed in a time-

bound manner. Concept Clustering can be effectively

used on structured and unstructured data.

Miscellaneous

Fraud

Cash

Gift

Figure 4: Concept Clustering

Sentiment Analysis

Known as behavioral analysis, this refers to the

application of text analytics to identify and extract

subjective information including the attitudes of

writers, their affective state and the intended

emotional quotient. It determines whether expressed

opinions in a document are positive, negative or

neutral. The “fraud triangle” can be applied to

categorize events into rationalization, opportunity and

pressure to identify sentiments. Organizations use this

data to conduct behavioral training, stem attrition,

and identify disgruntled employees and potential fraud

conversation.

Angry

Surprised

Confused

Cursing

Derogatory

Figure 5: Sentiment Analysis

6

Forensic Data Analytics


Evolution of forensic data analytics ey india 7441375

Data Visualization —

identifying the “hidden” from “not so apparent”

Data Visualization techniques have proved to be effective, since humans can better absorb large pieces of

information in a visual format than that displayed in numbers or text. When the result of a fraud identification query

is combined with Data Visualization, e.g., an account payable or journal entry data, a significant amount of useful

and previously invisible information can be reviewed at one go.

Tag Cloud

One of the most widely used visual techniques is a Tag

Cloud. This is a good example of expressing complex

data that can be understood intuitively. A Tag Cloud

is the visual representation of communication relating

to transactional data entries. It is represented by a

combination of words in varied fonts, sizes or colors.

This format is useful for quickly determining the

important terms to identify key fraud issues

Figure 6: Tag Cloud

Interactive CXO dashboards

A useful feature of analytics is that an entire data set

can be converted to a meaningful dashboard for a CXO

analysis.

Such dashboards help in understanding databases and

spreadsheets of any size with their easy drag and drop

interface. They not only display information visually

in seconds, but also create interactive maps with the

click of a mouse. They can effectively analyze time

series from years to months to the actual time in a day.

Their most helpful feature is their capability to combine

different databases to a single view and publish

interactive dashboards on the Web.

Figure 7: Interactive CXO Dashboard

Here, we have sliced the entire expense dump of an

organization from four key lenses including WHERE

(geography), WHAT (type of expense), HOW (expense

description) and WHO (the employee who incurred the

expense). Having multi-dimensional data on a common

platform helps a company perform an insightful analysis

to determine the tests that need to be performed on

expense data.

7

Forensic Data Analytics


Evolution of forensic data analytics ey india 7441375

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