Advanced Data Analytics in Fraud Identification February 23, 2015.

Advanced Data Analytics in Fraud Identification

February 23, 2015

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► Current challenges

► Data analytics defined

► What are clients saying?

► Technology

► Case Examples

► Questions

Agenda

33

Current challenges err…opportunities

GeopoliticalGovernment contracts

Conflict minerals Transparency

Reputation

Cyber Sanctions

Litigation

Labor relationsCustomer complaints

Investigations

Tax evasionOff shore

HSEFines

Self certification

Corruption

Suppliers

BribesPolitically Exposed Persons

4

United States casesUS Pending investigations

► As of July 2014, 106 publicly disclosed investigations pendingABM Industries Incorporated Delphi Automotive PLC Key Energy Services Inc.. Qualcomm Incorporated

Accenture PLC Deutsche Bank AG Kimco Realty Corporation Quanta Services Inc..Agilent Technologies Inc.. Deutsche Post AG (DHL) KKR & Company LP Rolls Royce PLCAirbus Group DreamWorks Animation SKG Inc.. Las Vegas Sands Corp Sanofi SAAlstom SA Dun & Bradstreet Corporation Layne Christensen Company SBM Offshore NV

Analogic Corporation Eli Lilly and Company Mead Johnson Nutrition CompanySciclone Pharmaceuticals Inc..

Anheuser-Busch InBev SA/NV Embraer SA Merck & Co Inc..Sensata Technologies Holding NV

AstraZeneca PLC Ericsson AB Microsoft Corporation Siemens AG

Avon Products Inc. Expro International Group PLCMondelēz International Inc. (formerly Kraft Foods Inc.) SL Industries Inc.

Barclays PLC FedEx Corporation Morgan StanleySmith & Wesson Holding Corporation

Beam Inc.Freeport-McMoRan Copper & Gold Inc. Motorola Solutions Inc. Société Générale SA

BHP Billiton LtdFresenius Medical Care AG & Co KGaA MTS Systems Corporation Sony Corporation

Bio-Rad Laboratories Inc. General Cable Corporation National Geographic STR Holdings Inc.

Blackstone Group LP GlaxoSmithKline PLC NCR CorporationTata Communications Limited

Bristol-Myers Squibb Company Gold Fields Limited Net 1 UEPS Technologies Inc. Tesco CorporationBrookfield Asset Management Inc. Goldman Sachs Group Inc. News Corporation TeliaSonera AB

Bruker Corporation Goodyear Tire and Rubber Company Nordion Inc.Teva Pharmaceutical Industries Limited

BSG Resources LtdGrifols SA (Talecris Biotherapeutics Holdings Corp) Novartis AG UBS AG

Central European Distribution Corporation Halliburton Company

Och-Ziff Capital Management Group LLC

Universal Entertainment Corp

Chestnut Consulting Inc. Harris Corporation Olympus CorpUniversal Music Group (Vivendi)

Cisco Systems Inc. Hyperdynamics Corporation Oracle CorporationViacom (Paramount Pictures)

Citigroup Inc. Image Sensing Systems Inc. Orthofix International NV VimpelCom LtdCredit Suisse Group AG Ingersoll-Rand PLC Owens-Illinois Group Inc. Wal-mart Stores Inc.

Cobalt International Energy Inc.International Business Machines Corporation Panasonic Corporation Walt Disney Company

Comcast (NBCUniversal Inc.) Johnson Controls Inc. PTC Inc. WS Atkins PLC (PBSJ Corp)Cubist Pharmaceuticals Inc. (Optimer Pharmaceuticals Inc.) JPMorgan Chase & Co Park-Ohio Industries Inc.Dialogic Inc. Juniper Networks Protective Products of America Inc.

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Forensic Data Analytics Defined

66

Forensic Data AnalyticsAnalytics Defined

Forensic Data Analytics (“FDA”): Refers to the multi-disciplinary service for the efficient and cost effective identification of relevant information in large-scale client datasets for a wide range of activity including the analysis of data to obtain meaningful insights for investigative, legal, regulatory, anti-fraud or risk mitigation matters.

FDA combines the extensive use of data, statistical and quantitative analysis, and explanatory and predictive models to guide and identify issues and areas warranting further review.

Outputs from FDA can allow companies to generate legally defensible fact-based evidence in order to drive decisions and focus investigative efforts where they matter, with the aim of achieving favorable outcomes.

7

FDA supports the Corporate Integrity & Compliance Framework

Forensic Data Analytics

Corporate Integrity & Compliance Framework

ProtectDetect Respond

Reactive

Risks

Proactive

88

How is fraud detected?

Source: ACFE 2010 Report to the Nations On Occupational Fraud

48.5% by tipor accident

99

Forensic Data Analytics Maturity

Focus Capabilities (in order of maturity)

Forensic single version of the truth

“Gather the hay into a haystack”

1. Forensic data discovery & extraction

2. Data joining and filtering

Errant behaviour detection

“Find the needle”

3. Application of rules4. Matching to external data (e.g.

sanctions lists)5. Unstructured text mining (e.g. key

word search)6. Entity analytics7. Statistical analysis & anomaly

detection (structured & unstructured data)

Ongoing monitoring & protection

“Prevent the needle being lost in the first place”

7. Visualisation and drill-down8. Case management and feed-back

look

Higher Detection

Rate

Lower False

Positive Rate

10

Understanding the FDA approach

TRANSACTIONAL DATA

MASTER & REFERENCE DATA

BUSINESSINTELLIGENCEDATA

SOCIAL MEDIADATA

structuredunstructured

Database Management Tools

http://www.google.com/url?sa=i&rct=j&q=&esrc=s&source=images&cd=&cad=rja&uact=8&ved=0CAcQjRw&url=http://galleryhip.com/social-media-icons-facebook.html&ei=DqCBVJe3A4G5PcbygfAJ&psig=AFQjCNG1TVKR8eX89B4_ZkVCrOVrQad7og&ust=1417867620524537


11


TRANSACTIONAL DATA



SOCIAL MEDIADATA

Rules-based tests

Text mining & search

Big data and/or SQL server data processing platform


DetectionTools

VISUALIZATION & RISK RANKING




12


TRANSACTIONAL DATA



SOCIAL MEDIADATA

Rules-based tests




Statistical anomalies& Predictive

Pattern Matching

Case Manager, Task Delegation and Data Refresh / ScriptingAutomation

InvestigationTools


DetectionTools




13


TRANSACTIONAL DATA



SOCIAL MEDIADATA

Rules-based tests


CaseReview



Statistical & Predictive

Pattern Matching


InvestigationTools


DetectionTools




14


TRANSACTIONAL DATA



SOCIAL MEDIADATA

Rules-based tests


CaseReview

Management

Review



Findings &Recommendations


Pattern Matching


InvestigationTools


DetectionTools




15


TRANSACTIONAL DATA



SOCIAL MEDIADATA

Rules-based tests


CaseReview

Management

Review



Findings &Recommendations


Pattern Matching

Transaction review, Risk ranking, Case Manager, Data Refresh / ScriptingAutomation

InvestigationTools

Repeat the process: Continuous Monitoring

(On-Site, centralized, outsourced)


DetectionTools




1616

What Are Clients Saying?

1717

What Are Clients Saying?FDA Technology

1818

What Are Clients Saying?FDA Challenges

Uncertainty about the relevance of FDA in the Company

FDA producing positive results to indicate and prove any fraud or bribery that is occurring

FDA is not prevalent to the culture

Huge volume of data to analyze

To identify fraudulent information across large data sets

Lack of human resources or manpower to operate FDA

Spreading the FDA culture across different Business Units

Difficulty in adapting FDA to comply with different regulations in various markets

Poor quality or lack of accuracy in the data

To prevent fraud rather than discover fraud

FDA is too expensive

Convincing senior management or the company about the benefits of FDA

Improving the quality of the analysis process

Challenges with combining data across various IT systems

Getting the right tools or expertise for FDA

0% 5% 10% 15% 20% 25% 30%

2%

3%

3%

4%

5%

5%

6%

6%

8%

9%

10%

10%

15%

15%

26%

With respect to forensic data analytics, what would you say is your single biggest challenge or requirement in your organization?

1919

Technology

2020

► Keyword library

► Concept analysis

► Entity extraction

► Text is everywhere

TechnologyText Analytics

2121

Document/data review analytics

► Concept induction and linguistic analysis► Keywords and Ontologies

► Emotive tone and ethical issue detection

► Topic modeling, concept mining, entity extraction

► Social network and actor analysis► Centrality and proximity

► External domains, family and friends

► Ontologies

2222

Ontologies

► Keywords alone are not as effective to reliably identify key concepts

► Ontologies allow us to capture concepts appearing in unstructured textual data

► Can be developed in automated or manual ways, and are reusable across engagements

2323

Stock ontologies

►Non-Responsive (1,832 classifiers; 19,188 terms)►NR Business

►Resumes, “doughnuts in the kitchen,” newsletters, itineraries

►NR Junk►Spam, fantasy football, baby pictures

►Emotive Tone (66 classifiers; 4,101 terms)►Angry, Confused, Secretive, Surprised

►Ethical Issues (419 classifiers; 5,042 terms)►Discrimination, workplace safety, price fixing,

personal problems

2424

Technology Risk Scoring

Filter by selected analytics

Review breaches on targeted analytics

2525

TechnologyGeocoding and Heat Maps

Hotspots of activity are

easily identified

Identify global epicenters of activity, as well as anomalies

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WHO`

WHAT

WHEN

WHY

• People-to-people analysis

• Entity-to-entity analysis

• Map communication lines to organization chart

• Top words mentioned

• Key concepts / topics

• Top or unusual dollar amounts

• Sensitive words / phrases

• When communications occur

• Communication spikes around key business events

• Positive vs. Negative Sentiment

• Top 10 negative journal entries

• Top 10 angry emails

• Top 10 most concerned emails

• Customer survey analysis

• Employee survey analysis

Who is talking to whom?

Social Networking

Concept Clustering

Communication Over Time

Sentiment Analysis

about what?

over which time period?

how do they feel?

TechnologyCommunication Analytics

2727

TechnologyEmail Analytics

► Identify the strength of known relationships

► Identify unknown relationships

►Can be performed on email logs, don’t need actual emails

2828

Entity analytics

Unusual connection between entities

2929

Technology Social Media Analysis

3030

Email Analytics

Dec 1 04 Mar 1 05 Jun 1 05 Sep 1 05 Dec 1 05 Mar 1 06 Jun 1 06 Sep 1 06 Dec 1 06 Mar 1 07 Jun 1 07

Month/Year

0.0

0.2

0.4

Sum of Incentive Pressure Pct

0.0

0.2

0.4

Sum of Opportunity Pct

0.0

0.5

Sum of Rationalization Pct

Incentive/ pressure terms

Keyword hits as a percentage of total emails

Opportunity terms

Rationalization terms

Investigation timeframe, September to March

► Research example: FCPA bribery case

3131

Convergent analytics model

Anti-fraud library of

journal entry and cash

disbursementtests

Vendorbackgroundchecks andpoliticallyexposedpersons

EY/ACFE keywordlibrary of misappropriation &

bribery/corruption terms(multi-language)

Accelerated decision

support and dynamic reporting

Suspicioustransactionprofiling and

predictivemodeling

Text analytics:“who,” “what,”

“when,” “where”

Vendor masterEmployee master

Travel & Entertainment

Accounts PayableGeneral Ledger

3232

Emotive tone -- Secretive

From: Donna

Sent: Wednesday, November 9, 2011 10:45 AM

To: Nikki

Subject: RE: Shhhhh

Absolutely.

-----Original Message-----

From: Nikki

Sent: Wednesday, November 09, 2011 10:45 AM

To: Donna

Subject: Shhhhh

Please don't mention our convo to anyone! I am high risk so I want to be sure that everything is safe and where it should be.

3333

Scott ClaryErnst & Young LLPPrincipalFraud Investigation & Dispute

ServicesHouston, TX(713) [email protected]

Thank You

Advanced Data Analytics in Fraud Identification February 23, 2015.

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Transcript of Advanced Data Analytics in Fraud Identification February 23, 2015.