taming 'big data' to improve enforcement, compliance and … · 2020-02-01 · review of...
TRANSCRIPT
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
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Do You Know Me?
Thief
Convict
Fugitive
Mayor
Businessman
Benefactor
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Do You Know?
21
And Do You Know…
Who They Know?
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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:
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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.
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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: [email protected] 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