PRESENTED BY: JERRY CHETTY

INVESTIGATIONS IN DATA HEAVY INDUSTRIES

What is data heavy industries?

2/13/2018

Fraud by numbers

• Health care R22 billion

• Card fraud R600 million

• Cybercrime R35 billion

• Short-Insurance R2-3 billion

• Inflated costs R233 billion

• Long-term insurance R870 million

2/13/2018

Some facts about fraudsters

2/13/2018

Fraudsters gather intelligence

2/13/2018

An investigator’s ME time

2/13/2018

What are the latest fraud trends?

What are the red flags?

What did we learn from the last fraud?

How difficult can we make it for

fraudster?

What intel do we have on suspects?

What is data heavy industries?

2/13/2018

Imagine conducting an investigation here!

2/13/2018

PHYSICAL EVIDENCE VS.

EVIDENCE IN DATA

Sherlock Holmes vs Data mentalist

2/13/2018

2/13/2018

Life is all about how

we see things

&

Sometimes we can

only see one thing

at a time

2/13/2018

Who would you

employ?

2/13/2018

Suggested approach

2/13/2018

2/13/2018

Insurance and it’s complex model

4. CLAIMS

PROCESS

SOS

INTER-

MEDIARY

INFLATED

FALSE CLAIMS

BACKDATING OF

COVER

CHANGING

CIRCUMSTANCES

OF CLAIMS

SUBMIT FALSE

CLAIMS ON

CLIENTS POLICY

2. ISSUE

POLICY

3. PAY

PREMIUM

DEBIT

ORDER

(D/O)

INTER-

MEDIARY

THEFT OF

PREMIUMS

CONTRAVENTION

OF S45 of STI

1. SELL

COVER

CONTACT

CENTRE

SANTAM

MANDA-

TED

INTER-

MEDIARY

PREVIOUS

CLAIMS

SECURITY

REQUIREMENTS

UNDERWRITING

FRAUD

FRAUD

MATERIAL

AFFECTS FACTS

PREMIUM

UNDERWRITING

FRAUD

5. ASSESOR

INTERNAL EXTERNAL

6. SERVICE

PROVIDER

CORRUPTION

FALSE INVOICES

INFLATED

REPAIR COSTS

(FALSE CLAIMS)

INFERIOR

REPAIRS

SUPPLIER

FRONTING

Methods of committing fraud in the insurance

industry?

1 Opportunistic crime

One person

Random act High incident

Low rand value

Low incident

High rand

value

2

Organised crime

One or more people

Elaborate

scheme with

an element of

collusion

Our case study – Investigating in big data

1 incident = R21 000

Evidence to prove

Great case solved

or is it?

Examine all claims:

records

Traditional tests =

zero results

Profile fraudster

What’s in a surname?

R1.8m

Our case study – Investigating in numbers

FRAUDULENT THIRD PARTY CLAIMS

• R22 000 only one incident

• Sufficient evidence to prove this

• Great case solved!

• Or is it?

• Extract claims data processed by alleged fraudster = lines (Overwhelming)

• Traditional analysis = Investigation = zero results (Frustration)

• Profile existing fraudulent transaction and alleged fraudster

• Refine analysis = investigation = What’s in a surname? = R1.4m

DOES LIGHTNING STRIKE IN THE SAME PLACE TWICE?

• Extract claims data for entire department = lines

• Analysis = Investigation = R1.2m fraud

Learning's • Huge opportunity for using data in investigations

• Uncover more fraud

• Provides most accurate financial impact of fraud incident

• Develop detective and proactive anti fraud strategies

• Results from any form of data analysis does not prove FRAUD

• Still need hard-core investigations

• Need the skill of both an investigator and the data mentalist – collaborative effort

• Using one person to execute both functions is NOT a collaborative effort

• Data heavy is the way of the future – need to embrace its usefulness

FRAUDSTERS DO!

Hiding within

those mounds of

data is knowledge

that could change

the life of a

patient, or change

the world.”

Atul Butte, Stanford School

of Medicine

“Without big data,

you are blind and

deaf in the middle

of a freeway”

Geoffrey Moore,

management consultant

and theorist

“Information is

the oil of the 21st

century, and

analytics is the

combustion

engine.”

Peter Sondergaard,

Gartner Research

QUESTIONS