201206 iasa session 408 - applied analytics
DESCRIPTION
Presentation given at the 2012 IASA Annual Conference on the use of analytics in the insurance industry. Case study examples provided with extrract of recent survey.TRANSCRIPT
June 2012 1
Applied Analytics: Converting Mountains of Data into Nuggets of GoldSession 408 Tuesday June 5, 2012 11 AM
Session Objectives
Learn how some insurers have found creative new ways to make use of analytics, with the goal of turning information into management action.
Hear one carrier’s analytics case study and learn how to use an analytics roadmap to effectively assess and capture the benefits of analytics company-wide.
After attending this session, audience members will be able to:§ Describe a roadmap for applying analytics
§ Evaluate the suitability of analytics for various functional areas
§ Describe how one carrier has used analytics and related technologies to improve business performance
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Top Line Benefits of Analytics
Business analytics enable organizations to be able to:§ Gain deeper, more relevant business insights to inform decisions§ Bring predictive analysis and regression modeling to entire organization§ Use analytics to identify and determine options for industry challenges§ Effectively and proactively manage risks§ Strengthen data governance at each level of the organization§ Reduce costs through more accurate, data-driven decision-making§ Use analytic capabilities and outcomes for change management efforts§ Create a culture that thrives on fact-based decisions versus “gut”
Impact on decision-making is one of the key reasons……
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Top Reasons for Adopting Analytics
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5%
2%
7%
2%
2%
31%
7%
34%
28%
25%
41%
38%
32%
39%
43%
36%
24%
25%
55%
24%
22%
36%
33%
5%
1%
0% 20% 40% 60% 80% 100%
Intuition
Experience
Group Dynamics
Collaborative Consensus
Historical Data
Future Projections (Predictive)
Not at all Some Typical/Common Almost Always Exclusively
Survey Says….AKA “gut feel”
June 2012 6Robert E Nolan Company Executive Survey, 2011
60%82%
Analytical Companies Perform Better
June 2012 7
A difference of 26% in year on year growth
27% year over year growth
1% year over year growth
Yet Companies Struggle to Implement
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Most frequent reasons companies struggle with analytic initiatives:• Too much management, not enough leadership• Limited support and buy-in at multiple levels within the organization• No guiding purpose or vision for people to rally around• Overemphasis on technology implementation/success criteria• Business benefits are too fuzzy to articulate and communicate clearly• No consistent communication or messaging to stakeholders• Poor identification of stakeholders and influencing factors• Compensation structures and incentives not aligned
Survey Comments on Barriers to Growth in Use of Analytics“Resistance comes from most experienced, those requiring 100% accuracy”“Access to critical data that is not captured in the system but is on paper”“Getting away from tribalism, managing by anecdote and subjective decisions”“Availability of resources and the money necessary to do it right”“Data is spread all over and difficult to integrate or consolidate”“Privacy will become a major issue as external data sources drive decisions”
Robert E Nolan Company Executive Survey, 2011
And Opinions Vary Greatly(2011 Nolan Analytics Survey Comments)
“The importance placed on analytics will grow, however there will be a disproportionate reliance placed on results, until management learns that garbage in/garbage out continues to cast its shadow.“
“It really doesn’t matter as most data currently produced comprises the basis for most uses necessary. Advanced techniques do not therefore produce ‘advanced’ data - the numbers are the numbers no matter how produced. Indeed, give me a room full of ladies in green eyeshades and Marchant calculators and maybe a punch card reader or two and I could be perfectly happy with managing the business, no matter how complex.“
“Those companies that do not embrace technology and analytics will be left behind in the dust of those companies that do. “
Retrospective versus Predictive
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Case Study A: Prospect Scoring
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PredictiveAnalysis
and Modeling
Low Medium HighPropensity to Convert
High value,Low
conversion, 2nd Priority
High value, Medium
conversion, Top Priority
High value, High
conversion, Top priority
Good value, Low
conversion, Low Priority
Good value, Medium
conversion, 2nd Priority
Good value, High
conversion, Top Priority
Low value, Low
conversion,Low Priority
Low value, Medium
conversion, Low Priority
Low value, High
conversion, 2nd Priority
Potential Value
Low
Med
ium
H
igh
Potential Future Value of Customer
Scoring of prospects based on conversion and value, marketing strategy developed to match
Survey Data
Web LogData
TextData
Purchased Data
Psycho-graphic Data
Case Study B: Agency Management
60% of customers would switch carriers if advised to do so by their agent. (Source: JD Power & Associates)
33%+ of agents are likely to change insurance carriers.(Source: National Underwriter and Deloitte)
Insurers that better manage their agents achieve competitive advantage.§ New customers have high acquisition expense, retaining one is more profitable.§ New agents have high acquisition expenses and pose a greater risk of inferior
retention rates, resulting in lower profits.§ Monitoring effectiveness of agents provide early warning that an agent may be
about to leave, triggering action and market differentiation.§ Predictive scorecards tie traditional features like traffic lights and speedometers to
powerful analytics. § Dashboard visuals provided at-a-glance access to the current status of new KPIs, with
automatic alerts for underperforming objectives and strategies.
Case Study B implemented an agency dashboard based on new KPI’s that were modeled with a predictive analytics tool.
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Case Study B: Agency Management
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Shift away from traditional sales performance metrics like Premium Revenue.New KPI: Agent Profitability net of cost to serve –calls, e-mails, supplies, etc.
Case Study C: Loss based Pricing
Result: More equitable and competitive risk adjusted pricing.
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$812.50
$1187.00
$438.00
Territory average loss ratios generate prices that are too high for some and too low for others.
Detailed risk analytics generate more accurate loss cost estimates by discrete segments of business.
ISO Price Analyzer Tool used for graphics
Case Study D: Retention StrategiesStep 1: Determine Life time Value
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Time of Purchase Demographics -Loses predictive value over time as relevance is superseded by inforce behaviors
Customer behavior shifts focus from current to future value
Predictive Analysis
Current Value
Future Value
Post Purchase Activity –Increases in predictive value over time as behavioral patterns develop
Case Study D: Retention Strategies Step 2: Predict Potential Lapse
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Predictive Analysis –
Model Channel and
Consumer Behaviors
Source of Business influences lapse tendencies based on channel behaviors
Transaction behavior influences lapse tendencies based on consumer behaviors
Case Study D: Retention StrategiesStep 3: Develop Strategy Matrix
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Match effort to risk and value –
• High value low risk gets medium effort, save money on retaining low risk customers
• Low value customers get low cost efforts across the board
• Targeted high efforts on high value / high risk
Case Study E: Claims Fraud
June 2012 18SAS Institute
About 10% of all insurance claims are fraudulent.Annual fraud losses for P&C industry total $30B in US alone.§ Need to detect unknown patterns of financial fraud.§ Keep track of new fraud schemes.§ Unsure exactly what to look for.
Rules: Captures fraud on known patterns previously usedEx: Two claims in different time zones within short window
Anomaly Detection: Detect unknown patterns (ind & aggr)Ex: Statistics (mean, std dev, uni/multivariates, regression)
Advanced Analytics: Detect complex patternsEx: Knowledge discovery, data mining, predictive assessment
Social Network Analytics: Determine associative linksEx: Knowledge discovery via associative link analysis (entity map)
Automated Fraud Detection Points
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FNOL EvaluateClaim
CloseClaim
Negotiate / Initiate ServicesPredict duration
Forecast loss reservesOptimize fast track claimsPrioritize resourcesFraudulent scoringLitigation propensity
Prioritized investigationFocus on organized fraudMinimize claim paddingReduce false positives
Identify salvage and subrogation opportunitiesIndicate deviations from similar claimsReports on claims overrides
InitiateSettlement
SIU
Update Claim
Fraud Referrals Fraud Referrals
Re-estimate durationReassess loss reservingPrioritize resourcesFraudulent rescoringReview litigation propensity
Cross-sell options for satisfied customerCustomer retention program
Assign Claim
Fast Track Claim
Other Brief Claims Examples
Optimized Claims Adjudication process.§ Using data mining to cluster and group claims by loss characteristics
(such as loss type, location and time of loss, etc.).§ Claims scored, prioritized and assigned per experience and loss type.§ Higher quality, more consistent, and faster claims handling.
Adjuster Effectiveness Measurement.§ Adjusters typically evaluated based on an open/closed claims ratio.§ Analytics create key performance indicator (KPI) reports based on
customer satisfaction, overridden settlements and other relevant metrics.
Claims involving attorneys often 2X settlement and expenses. § Analytics help determine which claims are likely to result in litigation.§ Assign to senior adjusters to settle sooner and for lower amounts.
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Analytics help shorten the claims cycle times. § Claims cost 40% more if FNOL is delayed by 4 days. § Auto accidents take average of 16.2 days to repair and return.§ Significant expense savings on rental cars, etc. for auto repairs.
Duration and Customer Satisfaction are directly correlated.
Analytics drive higher Customer Satisfaction and Lower Costs
One More Claims Example
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JD Power, 2007
27%36%
37%
772
854828
Over 2 wks8-14 days1 wk or less
% o
f Cla
iman
ts
Ove
rall
CSI
Inde
x
JD Power, 2007
Satisfaction going downCSI of 854 to 828 to 772
3 Guidelines to Implementing Analytics
All-executive panel agreed on three key guiding principles:1. Have an executive sponsored roadmap that clearly outlines.§ What resources will be needed for how long, § Where and when predictive analytics will be used, § Which tools will be used, and § How will success be measured.
2. Use data that is comprehensive, accurate, and current. § Not necessarily 100%, some have used only 70%. Must be representative.
3. Staff with talented and engaged people. § Completely understand business problem and are proficient with analytics. § Every person does not have to meet both qualifications; a team can be
used with some experts on the business and others experts on analytics.
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June 2012 23
When it comes to leveraging customer analytics, remember this well-known proverb:
The best time to plant a tree was 20 years ago. The second best time is today!
Closing Thoughts
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Are You Motivated?
Questions
June 2012 25
THANK YOU!!Robert E. Nolan CompanyManagement Consultants
www.renolan.com
Steven M. Callahan, CMC®
Practice Directorwww.linkedin.com/in/stevenmcallahan