GovLoop training analytics

Analytics to Outcomes:
Leveraging Predictive and Prescriptive
Analytics

September 8, 2011

Brought to you by:

Today’s Speakers
Steve Ressler
President and Founder
GovLoop

Christer Johnson
IBM Global Business Services, Partner
Advanced Analytics Services Leader, N. America

Shaun Barry
IBM Global Business Services, Associate Partner
Global Leader for Fraud Management Solutions

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IBM Advanced Analytics Software

Analytics to Outcomes:
Leveraging Predictive and Prescriptive Analytics

September 8, 2011

Christer Johnson
IBM Global Business Services, Partner
Advanced Analytics Services Leader, N. America

Shaun Barry
IBM Global Business Services, Associate Partner
Global Leader for Fraud Management Solutions

4

IBM Advanced Analytics & Optimization

What types of questions do we try to solve with Advanced Analytics?
Analytics Sophistication

What
happened? What could
happen if?
Simulation
Captured How can we
How many,
achieve the best
how often,
outcome?
Detected where?
What Optimization
trends will
Inferred What continue?
exactly is Forecasting
the
problem?
Use Structured Data & Simplified to be How can we
Unstructured Data consumable and What will
achieve the best
happen
• Numeric accessible to everyone, What
next if?
outcome and
optimized for their actions are address variability?
• Text Predictive
needed? Stochastic
specific purpose, at the Modelling
Optimization
• Image
point of impact, to
• Audio deliver better decisions Descriptive Predictive Prescriptive
• Video and actions through: Analytics Analytics Analytics

5


Three Areas of Benefit for Analytics Solutions

Data Infrastructure
Productivity Analytics Simplification /
Take-out cost and BAO Foundation
improve efficiency

Finance / Risk / Fraud
Government Analytics
Efficiency
Improve control, bottom Supply Chain /
line and stop losses Operational Analytics

Citizen Service
Responsiveness
Better service Human Capital
citizens needs Analytics

6



Data Infrastructure
improve efficiency

Efficiency

Citizen Service
Responsiveness

7


Veterans Administration (VA)
Agent Orange Fast Track Claims Processing System
Background Challenges
 Second largest federal agency  VA has a large disability benefits paper claims
supporting our nation’s veterans backlog that continues to grow, even with 15,000
people manually processing.
 500,000 staff, and 15,000 paper
claims processors  Needed a way to quickly process Agent Orange
related claims
 58 Regional Office processing
centers  “…we have to get beyond just sort of the brute-force
approach to this and … get better business
processes, automation in place.” -General Eric
Shinseki, VA Secretary 5/14/10

Solution Benefits and Results
 Fast Track is comprised of:  System up and running in 120 days
–An external website which both Veterans and  34,000 claims (150,000 documents)
Medical Providers access to support information processed through the Fast Track system
or evidence to a claim.
 Reduced the VA end-to-end claim processing
–An internal VA FileNet system where users can
process these disability claims. time without decreasing the quality and
consistency of the program via Fast Track
–An IBM scanning facility in Rocket Center, WV
where hardcopies can be digitized and included
electronic claims
with the Veteran‟s claim and supporting  Veterans have 24x7 online access to the
evidence. status of their claims and now have a much
less complex application process

8

A Large US Bank

Current State: “I have an offer – let me find a customer to sell to”

Offer Offer Direct Mail Relevance?
DEPOSITS Offer Offer Awareness?
Value?
Customer Needs & Segment Strategies

Offer Offer
Mass Market | Mass Affluent | Small Business

Understanding? Mass Market
BC Clarity?
 “You do not know me & ask
me multiple times about the
Offer Offer same thing.”
CARD Offer Offer
Agent, IVR Mass Affluent
Offer Offer
 They don’t really know me -
Customers are offered products
that may feel irrelevant and
Offer Offer Online, Email
disconnected vs. solutions
MORTGAGE Offer Offer
Small Business
Offer Offer
 “I use Large US Bank for
ATM
convenience but primarily use
another bank. “
Offer Offer  “The Bank doesn’t understand
INVESTMEN Mobile, SMS me, my industry or my business. “
Offer Offer
TS  The Bank’s associates can’t
Offer Offer address all of my business needs
Chat

9
9

A Large US Bank

Target Vision: “I have a customer – what do they need most?”

“The bank knows
CIM me and values
Direct Mail
DEPOSITS
Governance, my relationship“
Prioritization
Customer Needs & Segment Strategies
Mass Market | Mass Affluent | Small Business

& BC
“They seem to
Optimization Brilliant! know what I
CARD need and when I
Agent, IVR need it.”

“The bank isn’t
MORTGAGE Online, Email always selling
something.”

“They always get
ATM
INVESTMENTS me to the right
place and never
Customer Analytics fail to follow up.”
Customer Mobile, SMS
Experience &
Treatment “There is real
Strategies Integrated Data value to me in
Chat getting all my
needs met by
one bank.”

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10



Data Infrastructure
improve efficiency

Efficiency

Citizen Service
Responsiveness

11


Real-Time Pattern Recognition of Streaming Data in a Neo-Natal Unit
(Toronto Hospital)


Transforming Education – Gwinnett County Public Schools
Client Gwinnett County Public Schools (GCPS)

Industry Education

Challenge Education delivery has traditionally been „brick and mortar‟ classrooms and
instruction by textbook. Typically student challenges are identified after they
occur. GCPS‟ vision is to identify student challenges and opportunities for
improvement before they occur – applying the appropriate interventions or
enrichment to enhance learning for each student.
Solution For a pilot student population, IBM developed a model to identify factors that
potentially predict a student‟s success in Algebra. These predictors were used to
determine „at-risk‟ student groups, and thus assist educators in determining the
remediation and/or instructional guidance for different student populations.
Benefits The results of the analytical model convinced GCPS to expand the scope to
ensure integration of predictive analytics into the future teaching and learning
model for the school system. In addition, with the integration of appropriate
instructional content, GCPS has embarked on a path toward differentiated and
focused student instruction based on student needs – potentially before the
needs are identified. The goal is to enhance student learning and ultimately
improve student achievement.

13


North Carolina Medicaid – Fraud and Overpayment Detection
Client State of North Carolina

Industry Government

Challenge The current business process and technology used to fight fraud,
waste, and abuse in Medicaid is ineffective – producing only around
$25 million annually in recoupment letters. This leakage, combined
with a significant state budget deficit, motivated the state to
aggressively pursue cost takeout projects.

Solution The state implemented a comprehensive fraud analytics solution,
based on IBM technologies. The IBM solution examines claims for
suspicious patterns of behavior, and it identifies organized criminal
rings and collusive behaviors.
Benefits $75 million in recoupment letters issued in first 12 months

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An example of how this analysis works
 SCHEME – Services Not Rendered

15


 SCHEME – Pop-up Provider/Storefront Scheme

16


 SCHEME – Doctor Shopping

17


State of New York – Income Tax Refund Fraud
Challenge New York wanted to enhance current audit case selection
methods for detection of audit issues at the time a return is
processed. Specific audit programs include Earned Income
Credit, Dependent Child Care Credit, Itemized Deductions,
Wage/Withholding, and Identity Theft.

Solution IBM‟s fraud analytics solutions. Our solution applies
business rules and predictive models to categorize and score
returns nightly and identifies the „next best case‟ for audit
selection. In addition, a separate web based portal provides
screening and resolution of cases.

Benefits  $1.6 billion increase in refund denials
 Increased screener and auditor productivity
 “Honest” taxpayers have refunds quickly processed with less
hassle

Lessons  Real-time analytics are complex but provide great benefit
Learned  Benefits can be gained without substantial increase in staff
 Don‟t believe the “we‟re already doing that” argument
 Having an aggressive Business Champion is essential
18


Aetna – Medical Cost Trend Analysis
Client Aetna

Industry Healthcare

Challenge With the goal to better manage medical cost, Aetna wanted to enhance
its capability to identify and diagnose changes or patterns in cost
trends in a more automated and timely basis.

Solution For a pilot set of data, IBM built a multiplicative regression model that
can simultaneously evaluate the impact that all possible factors have
on changes to overall medical costs.
Used optimization to determine the best fit regression model.

Benefits While only in pilot, the results of the model were enough to convince
Aetna to expand the scope of the model and pursue a path towards
implementing the model as part of their ongoing cost trending analysis
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Let‟s say that:

2008 Cost = $100 and 2009 Cost = $180

$ Cost Impact $ Cost
of Factor 1 = Impact of
$60 Factor 2 =
$30

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Let‟s say that: 2008 Cost = $100 and 2009 Cost = $180

Total Cost Impact of Factor 1 & Factor 2

Factor 1 Factor 2
Individual Cost Individual Cost
Impact = $60 Impact = $30
Factor 1 Factor 2
remaining impact, Combined remaining
with Factor 2 Cost Impact impact, with
removed Factor 1
$50 $10 removed
$20

21


Network Modelling and Optimization at USPS

 Developed optimization and simulation models
(NIA) used to design future network structure
 Developing a transportation optimization and (NIA)
planning system (TOPS) to improve utilization Strategic
and reduce costs of the USPS‟s transportation • Node Optimization
network Tactical (TOPS)
• Route Plans & Schedules
 Benefits to the Client
• Routing Decision Rules
– Estimated operational savings of 10- 20 percent
– Better understanding of excess capacity Operational
• Dispatch & Routing Execution (SAMS)
• Track & Trace Visibility (SASS)

Operations
Statistics Economics
Research

Data Mining / Data Cost / Benefit Analysis
Optimization
Analysis Econometrics
Simulation
Estimating actual mail Micro Economics
Discrete-Event flows

22


USPS Highway Corridor Analytical Program

Client U.S. Postal Service (USPS)

Challenge Needed to identify quick-hit savings for the plant to plant transportation
for a region of the country by increasing utilization on each truck.

Solution Built an optimization model using ILOG CPLEX to evaluate
transportation between approximately 20 sorting centers. HCAP was
designed as a transportation optimizer to identify opportunities to
consolidate USPS highway transportation in order to save costs.
Complex data mining and predictive analytics were required to
estimate mail volume flows.

Benefits Within the first year of implementation, USPS realized transportation
cost savings that resulted in a 400% return on investment.

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Data Infrastructure
improve efficiency

Efficiency

Citizen Serivce
Responsiveness

24


Background – U.S. Social Security Administration (SSA)

Number of Recipients: 12.4 Million

Total benefits: $105 Billion

Administrative costs: $5 Billion

25


Historically - Lengthy Disability Approval Process
97 days to 17 months

738,000
cases in
backlog

Initial Federal
Application Reconsideration Hearing Appeals District Court

Level 1 Level 2 Level 3 Level 4 Level 5

Up to 5 years

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Quick Disability Determination (QDD) – an new process

Clearly
Disabled Decision and
QDD Unit benefits in 11
days
Scoring
Model:
All
Is Applicant
Applications
Clearly Normal
Disabled? Decision: 97
Adjudication
days on
and
average, up
Not Clearly application
to 5 years
Disabled levels
• SSA conceived of a new
• Request additional
process medical records
• Create a centralized • Request medical
examination
team for expedited
review of cases
• Automatically send
workload to this group
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The Specific Algorithms Used for QDD
Two were needed:

Robust Risk Minimizer Misspelled Vocabulary
(RRM) Correction (MVC)

• Analysis tool that “reads” the • Essentially a spell checker
text from the allegations field
and numeric data found on • Ensures the RRM does not
the application use the two or more different
spellings of the same
• Computes the probability of impairment as if they were
being a QUICK DECISION totally different impairments

• Example of a Probabilistic
Classifier

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Social Security Administration – Disability Benefits

Has reduced the
cycle time to process
10% of applications
for disability from:
97 days to 20 days

Has used predictive
modeling to save
$2 billion in disability
benefit renewal costs
since 2000.

29


Federal Housing Administration – Insurance Pricing
Client FHA

Industry Government Agency: Mortgage Finance

Challenge To accommodate the growing baby boomer population and changes in
the housing market, Federal Housing Administration (FHA) re-analyzed
the reverse mortgage (HECM) design and pricing structure to better
align insurance prices to the current conditions.

Solution IBM developed a simulation model for mortgage loan performance of
various premium structures. Then, IBM integrated the simulation with
an optimization algorithm to determine the optimal pricing structure.

IBM also leveraged grid-computing technology to boost computation
power and complete the re-pricing effort in a timely manner

Benefits FHA used the model to determine an array of insurance pricing and
risk management options to lower front-end insurance cost for the
borrowers, increasing the program‟s attractiveness to new enrollees. It
also strengthened the financial soundness of the program, allowing
HECM to maintain as a risk-neutral program in the volatile market
environment. 30


Best Buy Case Study: Segmentation Approach
• Start with 30-40 modeled variables – “Feature Vectors” – The customers response
to the firm‟s value proposition
• Each feature vector is like a gene strand, which describes a facet, or set of customer
behavior traits

Most segmentation
approaches only
Time until focus here
Repurchase Econometric:
Annual Age +
Annual
in Key Real-estate &
Unemployment Spend Transactions Income +
Preferred Categories Level Geography
Product
Preferred Categories
Length of Channel
Time as
Customer

Participation in
Use of In- Loyalty
Return / Use of Service
House Credit Program
Breadth of Exchange Programs
Behavior Card
Categories
Response to Shopped
Recency +
Media
Frequency +
Value

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A Major Health Insurer: Customer Analytics
Industry Healthcare

Challenge With the changing in healthcare environment, the Payer is transforming
its traditional group-based engagement approach to a more consumer-
centric engagement approach.

Solution Leveraging predictive and customer analytics, IBM developed several
analytical models to extract key consumer insights for every customer,
including health insurance status, channel preferences, education needs.

Benefits Data-driven approach played a critical role in new customer engagement
strategy and enabled a more personalized and relevant consumer
engagement experience. Specifically:

– Insights into prospects‟ health insurance status enable a more targeted and
effective acquisition approach
– Insights into consumer‟s channel preference enable Payer to engage prospect
and members in the most effective channel for different messaging context
– Insights into members‟ education needs enable Payer to help member to
understand plan value and promote wellness, building trust and loyalty.

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How to get started

Pick your spot
Biggest and highest value opportunity

Prove the value
Start with questions Embed insights

Roll it out over time
Add capabilities Information agenda

Source: Analytics: The New Path to Value, a joint MIT Sloan Management Review and IBM Institute of Business Value study. Copyright ©
Massachusetts Institute of Technology 2010. 33


Next Steps

Join the IBM GovLoop User Group Today:
Analytics to Outcomes

For those in the Washington D.C. area on
September 15th join us for a complementary
analytics event:

Time: 7:30 a.m.-9:30 a.m.
Venue: Ronald Reagan Building ~ The Rotunda, 8th Floor
(North Tower)
1300 Pennsylvania Avenue, NW, Washington, DC
To Register:
"Tough Choices, Hard Numbers: How Does Your
Agency Cut Costs Without Losing Effectiveness?"
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