Business Intelligence Solutions for Business Intelligence Solutions for the Insurance Industrythe Insurance Industry
DAT – 13 Data WarehousingDAT – 13 Data WarehousingRasool AhmedRasool Ahmed
©2000 Thazar Solutions Corporation
Business IntelligenceBusiness Intelligence
Questions:
• BI - What is it?
What would I do with it?
Why do I need another system to do it?
• BI supplier selection evaluation criteria.
©2000 Thazar Solutions Corporation
Business IntelligenceBusiness Intelligence
•Executive Analysis•Query•Reporting•Data Mining
OperationalOperationalData SourcesData Sources
•Claim•Sales & Marketing•Financial•Underwriting•Third Party Data•Other Int. Systems
Access
LoadTransform
Extract
Data WarehouseData Warehouse•Multi-Company•Multi-Line•Multi-Source•Multi-Dept/Function•Transaction Level
PolicyJ BrownFemale
July 20, 1945Financial Consultant
Claim
Judy BrownOne Claim Filed
CWP
MVR
Brown, Judy AnnTwo Tickets 1999
One DUI
External
Judy JacksonGood Credit HistoryIncome > 100,000
Judy Ann BrownFemale
July 20, 1945Financial ConsultantGood Credit History
Income > 100,000One Claim Filed
Closed Without PaymentTwo Tickets 1999
One DUI
©2000 Thazar Solutions Corporation
BI - What would I do with it?BI - What would I do with it?
•Interactive•Intuitive•Mgt Review
Exec / Mgt ProfilingExec / Mgt Profiling
Detailed AnalysesDetailed Analyses
•Loss Triangles•Risk Assessment•New Business•Exposure Evaluation
Data ModelingData Modeling
•Pattern Recognition•Predictive Modeling•Risk Scoring“Fraud with 85% accuracy”
“…predict with 82% accuracy those customers thatWill cancel their policies.” “… The special Investigation Unit can now prioritizeand catch 66% more fraudulent claims per referral.”
©2000 Thazar Solutions Corporation
Why do I need another system to do it?Why do I need another system to do it?
• Data organized for OLTP, not analysis
• Inability to slice and dice – geared for management reports
• Unintelligible coding structures; no meta data
• Not a complete picture (multiple systems); can’t merge
• Inability to augment data
• 87% of all insurance master files are non-relational
• Inability to profile trends
©2000 Thazar Solutions Corporation
Business IntelligenceBusiness Intelligence
•Executive Analysis•Query•Reporting•Data Mining
Insure MartsInsure Marts™™•Corporate Detail•Corporate Summary•Line of Business
Insurance WarehouseInsurance Warehouse™™•Policy•Claim•Rating
Data SourcesData Sources•Claim•Sales & Marketing•Financial•Underwriting•Third Party Data•Other Int. Systems
•Reinsurance•And more
Access
LoadTransform
Extract
Aggregate
InformationInformationsource datasource data
provided byprovided byinsurerinsurer
InsightInsightknowledge knowledge
& insight & insight gathered by gathered by
insurerinsurer
solu
tions
pro
vide
d by
Tha
zar
©2000 Thazar Solutions Corporation
Business IntelligenceBusiness Intelligence
How Much?
199X 2000
$$ $3+ M 1/4% NWP
Time > 3yrs 3 – 5 months
Function Reports Profiling/Predictions
©2000 Thazar Solutions Corporation
Business IntelligenceBusiness Intelligence
For Example…….:
Fraud Detection Losses are 70% of NWP; 10-20% of Losses are Bad FaithIdentifying <4% of Bad Faith claims pays back cost of DWH
RetentionPoor Average Good 25% 35% 45% (after 4yrs)
|----------------------------------- (2%) Loss Ratio |----------------- (1 ½%) Loss Ratio
Increasing Retention by 1 ½% pays back cost of DWH (reduced losses only)
New BusinessCosts of Sales can vary from 5% to 20% of NWPMoving 5% of business to channel that is 5% more efficient pays back cost
©2000 Thazar Solutions Corporation
Better Qualityof Data
35%Better Understanding
of the Business
20%
More TimelyDecisions
30%Exploit New Market
Opportunities
15%
Meta Group Survey of 300 Companies Implementing Warehouses
Over 400% ROI in less than 3 years!
Why Companies are DoingWhy Companies are DoingBusiness IntelligenceBusiness Intelligence
©2000 Thazar Solutions Corporation
Revenue Growth / Expense Control
• Questions You Need Answered Now
• Questions You Have Not Thought About
• Acquisitions
Why Companies are DoingWhy Companies are DoingBusiness IntelligenceBusiness Intelligence
©2000 Thazar Solutions Corporation
Personal Auto – Retention AnalysisPersonal Auto – Retention Analysis
• Policy Holder Characteristics• Loss Attributes• Policy Attributes• Distribution Analysis• Dimensions
©2000 Thazar Solutions Corporation
Homeowners – New Business AnalysisHomeowners – New Business Analysis
• Policy Level Analysis• Risk Characteristics Analysis• Home Feature Analysis• Time Views
©2000 Thazar Solutions Corporation
Engagement OverviewEngagement Overview
• wk 1-2 Plan / Organize• wk 3-4 Data Analysis Workshop• wk 5-7 ETL Development• wk 8-9 Test & Balance• wk 10-11 Load Warehouse & Marts• wk 12 Go Live
©2000 Thazar Solutions Corporation
Implementation Roles / Implementation Roles / ResponsibilitiesResponsibilities
BI Solutions Provider
• Project Manager
• Business Analyst*
• Data Analyst*
• Technical Analyst
* Insurance knowledge & experience is critical
Client
• Project Manager
• Business Analyst
• Data Analyst
©2000 Thazar Solutions Corporation
Supplier Both You
Executive Sponsorship & Vision
Functional Executive commitment
Information Systems Team involvement
BI Insurance Experience & Methodology
Pre-defined Models, Templates, Marts
& Executive / End User Browser
Critical Success Factors...Critical Success Factors... Critical Success Factors...Critical Success Factors...
©2000 Thazar Solutions Corporation
Critical Success Factors...Critical Success Factors... Critical Success Factors...Critical Success Factors...
Supplier Supplier BothBoth
YouYou
Scope & definition study - phased Implementation Scope & definition study - phased Implementation
Expectations & Results understoodExpectations & Results understood
Business & I/S experts to implement Business & I/S experts to implement
Training and skills transfer Training and skills transfer
Easily Supported & MaintainedEasily Supported & Maintained
Add additional departments, enhancementsAdd additional departments, enhancements
& applications& applications
©2000 Thazar Solutions Corporation
Lessons LearnedLessons LearnedLessons LearnedLessons Learned
Don’t:• build “boil the whole ocean”
• oversell to end-users
• build something that can’t be maintained and extended
Do:• start small “phased” - prioritization by LOB
• ensure data has integrity and is balanced
• define measurable objectives Keep At It !!!• deliver “baseline” results early & continue to build
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