taming 'big data' to improve enforcement, compliance and … · 2020-02-01 · review of...

Saturday, May 18, 2013 | 10:45 - 12:00 PM

Speakers:

Brian Golden, JR Helmig, Jack Nadeau and David Stewart

How to Harness Data Analytics in Financial Crime Control

Taming 'Big Data' to Improve Enforcement, Compliance and

Regulation

JR Helmig

Founder

Leveraged Outcomes

Fairfax, VA

Process Matters

• Tasking – 5 Ws and 2 Hs

• Collection – proper data hunting

• Processing – make the data better

• Analyze – what type should be used?

• Dissemination – when? To whom?

• Feedback – how to improve

How to Solve Key Questions?

Must create analytical roadmap

• How to measure success?

• What is legal? What is not? Brand risk?

• Privacy, authorities, and governance

• Data fusion and

transfer rules

Consistent Applications & Methods

• Know default configurations and assumptions

• Vet hypotheses and problem statements

• Update prior studies to reflect new capabilities

• Consistency across time and people

Average $ Value?

Record $ Value

1 10,000

2 10,000

3

4 AAA

5 null

Blended Methods

Rule Based

Pattern Recognition

Social network, link, nodal analysis

Anomaly Detection

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Data +

Methods +

Tech +

People =

Strategic

Effect

• How good are your current methods? Measured how?

• How are you moving from

A to B?

David Stewart Business Director, Fraud and Financial Crimes Practice

SAS Institute, Inc.

Cary, NC

Enterprise Approach- Analytics

“…a fragmented approach is consistently leaving organizations at greater risks of attack. A more

holistic approach to security ensures all layers of protection function together.” Avivah Litan, VP Gartner Group

Industry Trends

• Traditional silos persist in fraud, cyber security, and AML compliance

• Reactive approach to analyze risks after the fact

• Fail to connect the dots

• Converged FC Analytic units (FCIUs)

• Role of FC Business Analyst to proactively search for hidden risks

• Use of network analytics to identify hidden relationships

Techniques/Skills Required

Copyr i g ht © 2012 , SAS Ins t i tu t e Inc . A l l r ights reser ve d .

Case study AML ALERT TRIAGE– TIER II U.S. BANK

– Reduced cash structuring alerts by 40%

– Auto-disposed 20% of cash structuring alerts to SAR QA

– Saved 7 FTE within FIU

BUSINESS ISSUE

– Too many false positives

– Not enough staffing to handle volume

– Need to automate some human processes

ANALYTIC DIFFERENTIATORS

– Advanced profile signatures

– Predictive models to score all alerts

– Alert “pre-processing” automation to auto-decision

Copyr i g ht © 2012 , SAS Ins t i tu t e Inc . A l l r ights reser ve d .

Case study CREDIT CARD FRAUD- TIER 1 GLOBAL BANK

– $60M/year

increase in bottom

line from increased

revenue

– $40M/year bottom

line saving from

Fraud loss

prevention

– Reduction from

3,000 rules to 1

model + 75 rules

BUSINESS ISSUE

– Steady increase in Customer Dissatisfaction from Credit Card Usage

being declined inappropriately

– Increased fraud losses

– Increasingly difficult to maintain the prior fraud platform with 3,000+

rules

ANALYTIC DIFFERENTIATORS

– Holistic customer signature

– SONNA real-time decisioning

Copyr i g ht © 2012 , SAS Ins t i tu t e Inc . A l l r ights reser ve d .

Case study ENTITY LINK ANALYSIS – LARGE N. A. BANK

– $1,996 > average savings realized through use of network analytics

– False positive rate of 4:1 for high risk fraud and credit accounts

– Alerted accounts on average 2-3 weeks earlier than existing processes

– Automated network creation eliminated hours of manual investigation

BUSINESS ISSUE

– Reduce losses related to credit card bust-out

– Improve efficiency of investigation and case operations

– Identify organized fraud rings and react accordingly

– Identify related accounts with increased risk

ANALYTIC DIFFERENTIATORS

– Hybrid application of rules, models, anomaly detection, & models

– Sourced application, payment, and negative news data

Recommendations

• Integrate data across fraud, AML, security disciplines

• Harvest unstructured data from external sources

• Recruit analytic talent into LOB’s

• Have a “big intelligence” strategy for future deployments

Jack Nadeau Senior Director, Product Management

LexisNexis

Dayton, OH

17

Victor Hugo’s Les Miserables

18

Do You Know Me?

Jean Valjean

19

Do You Know Me?

Thief

Convict

Fugitive

Mayor

Businessman

Benefactor

20

Do You Know?

21

And Do You Know…

Who They Know?

22

How Does This Work In The Real World?

Preventing Real Estate Collusion

Just as the financial world failed to realize the impact of Fraud for Profit until significant damage was done, the mortgage industry is now waking up to an increase in instances of collusion, the sophistication of these schemes, and the larger resultant losses.

The Future of Customer Intelligence with Big Data 23

How Does This Work In The Real World?

= Property with a suspicious transaction

= People associated with the transaction

Parties that share a common associate Groups of individuals that are potentially colluding and are engaged in directed suspicious activities form clusters

In Network Cluster In Network Transaction

The Future of Customer Intelligence with Big Data

How Does This Work In The Real World?

• Entity resolution and linking

• Data analytics

• Computing horsepower

You can unlock the potential of your data through:

25

Unlock Your Data

Verify Identities • Confirm identity via accurate customer data

applied to each individual customer.

Define Risks • Capture individual due diligence profiles. • Interrogate entity-linking profiles on the

customers’ network.

Enhanced Due Diligence

• Deep dive information for high-risk entities. • Assess both initial and refreshed customer data

for current risk profiles.

26

What Can This Mean To You?

Summary

LexisNexis and the Knowledge Burst logo are registered trademarks of Reed Elsevier Properties Inc., used under license. Copyright © 2013 LexisNexis. All rights reserved.

• Don’t think that you know your customer—they might be an actor.

• You can unlock the potential of your customer files through data analytics, entity linking and computing horsepower.

• Enhance your financial crimes investigations by verifying identities, understanding networks and identifying suspicious patterns.

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For more information contact Jack Nadeau: Office: 937.865.7570 Email: jack.nadeau@lexisnexis.com www.lexisnexis.com/risk

Brian Golden Manager, Assurance Services, Fraud Investigation & Dispute

Ernst & Young

New York, NY

Topics • Using analytics to address regulatory actions, enhanced regulatory

scrutiny

• Utilizing data analytics in internal investigations

• How data analytics support and intersect with e-discovery in financial crime litigation

• Integrating data analytics into existing compliance processes and procedures

Enhanced regulatory scrutiny North America Dodd-Frank Act, Basel III and FATCA Convergence are likely to dominate the regulatory landscape in this region

Latin America Improved regulatory regimes with the intention to come into line with the G20 recommendations. For example, a higher percentage of adherence to Basel core principles than the developing markets of Asia

Asia Pacific More global regulations, such as Dodd Frank, FATCA and Basel III, must contend with national laws, approaches to regulation, making the region an area of high regulatory variance

Rest of the world Many of the emerging markets such as Africa and the Middle East currently under extreme civil unrest are making the application of national and regional regulation extremely challenging

Europe (and Eastern

Europe emerging markets) EMIR, Dodd Frank, FATCA, Basel III, AIFMD and UCITS all contribute to a complex and concentrated period of regulatory reform in the region over the next 3-5 years and beyond

Data analytics & internal investigations

Visualizing data analytics

Vi ew suppor t i ng

document s as

dynami c obj ect s

Gr aphi cal r epr esent at i on of r el at i onshi ps bet ween

seemi ngl y di scr et e ent i t i es Epi cent er s of act i vi t y

become i mmedi at el y

di scer nabl e

Data analytics & e-discovery & financial crime litigation

Integrating data analytics into existing compliance processes and procedures

►Review of fraud policies and controls

►Industry benchmark of anti-fraud programs

►Gap analysis ►Future state design session

►Who owns fraud? ►Assess roles and

responsibilities ►Fraud and risk committee

formulation ►Customized training ►Corporate governance ►Corporate anti-fraud

road map ►FCPA / anti-bribery

assessments

►Fraud risk assessment ►Targeted anti-fraud analytics ►Anti-bribery and corruption analytics ►M&A Due Diligence ►3rd Party Due Diligence ►3rd Party Risk profiling ►Conduct background checks

►Investigations ►Fraud response

planning ►Forensic data

analytics ►Discovery and

document review

Code of Ethics

Fraud and Corruption Prevention

Policies

Communication and Training

Risk Assessment

Controls Monitoring

and Analytics

Incident Response

Plan

Reactive

Proactive

Setting the Proper Tone

Management Ownership and Involvement

Your Questions

Thank You for Attending the 2013 International

Financial Crime Conference & Exhibition