Download - Demystifying business intelligence
CEO and Co-Founder, Insight Decision Solutions Specializes in BI for insurance and has overseen many BI projects Used BI for RPEC mortality study Former Chair of the Technology Section Speaks without an accent
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BI system installed in-house Background Case studies BI as a business project
Actuarial role and benefits from BI
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Typical reasons given for BI: Consolidate Data Make information accessible Enhance with analytics Integration Enterprise view of business
Concerns: Security Breadth of users Project risk and cost
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Benefits often intangible
Against: Reporting available from other
systems Local data extracts available Would create additional version
of data Users comfortable with existing
tools
For: Integration of data Operational data not structured
for analysis Maximize potential from
existing systems
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By definition the Insurance Industry is one of the largest users of data
Early failures Notable successes BI in other industries
1997 – IBM, Oracle, Microsoft launch BI products
1990 – Cognos launch first desktop BI tool
Niche vendors, fragmented tool sets
Emergence of mainstream, web based products
Data Warehouse / OLAP Server
Presentation Server
UsersMetadata
Data Store
Integrated Systemse.g. valuation system
ETL
ETL
Source systems
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What triggers projects? Challenges and lessons learnt
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Senior management want better information Actuaries needed improved analytics Improved data management
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CEO / Board requests Format suitable for (non-technical) audience Verifiable and integrated – need to get everyone on the same page Need to be able to answers questions not yet anticipated Budget sometime easier to obtain
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Improved Financial Reporting• GL drill-down by policy and product dimensions
• Integrated financial planning
• Source of Earnings
Experience studies
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Valuation extract management• Valuation data transformations – consistent
• Reproducibility of extracts
Shared data source
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Health Insurer writes $1bn premium annually Rate increases subject to regulator approval may be 6% p.a. Value of accelerating approval by one month Additional Revenue = $1bn x 0.06 x 1/12 = $5m p.a.
(modifications: not all business receives increase, may not be 6%, one time catch up, etc)
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Opportunity spotted by COO• Loss ratio by agent
• Not credible, but effective
Handling success – report explosion, staff role change Concern of data availability to all areas
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Personal Information, access to sensitive financial information
• Modify query based on login credentials
• Design in structure of DBs to restrict access Laptops / transportable data
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Many non-tangible benefits discovered during the project Would have done differently:
• Definitions / less pre-planning
• Accessibility
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Business functions cannot be an after thought Success does not come from a data mapping exercise Business leadership is critical for success Application model needs to be designed upfront
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Unique features of insurance need to be recognized in the design• Sale is the start of a relationship
• Data is complex Life Health – temporal, P&C large number of attributes
Goal of moving logic upstream• Reduces work
• Avoid inconsistencies
• Proves system
Data Warehouse / OLAP Server
Presentation Server
UsersMetadata
Data Store
Integrated Systemse.g. valuation system
ETL
ETL
Source systems
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Goal Move application logic into the system managed by metadata
Policy Fact Table
Extract Date (FK)Product Code (FK)Jurisdiction (FK)Policy IdDate of BirthIssue AgeIssue Date (FK)Sum AssuredAnnual PremiumReserve
Time Dimension (1)
Extract Date (PK)YearQuarterMonth
Product Code Dimension
Product Code (PK)Product NameProduct DescriptionProduct TypeProduct FundProduct Group
Jurisdiction Dimension
Jurisdiction (PK)State or provinceSales AreaCountry
Time Dimension (2)
Issue Date (PK)Issue Year BandYearQuarterMonth
Policy Fact Table
Extract Date (FK)Product Code (FK)Jurisdiction (FK)Policy IdDate of BirthIssue AgeIssue Date (FK)Sum AssuredAnnual PremiumReserve
Time Dimension (1)
Extract Date (PK)YearQuarterMonth
Product Code Dimension
Product Code (PK)Product NameProduct DescriptionProduct Type (FK)
Jurisdiction Dimension
Jurisdiction (PK)State or provinceSales AreaCountry
Time Dimension (2)
Issue Date (PK)Issue Year bandYearQuarterMonth
Product Type Dimension
Product Type (PK)Product FundProduct Group
Need to embed calculated functions Need to accommodate unique insurance features
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Don’t be intimidated by jargon• OLAP (online Analytical Processing), ROLAP, HOLAP, MOLAP,
DOLAP, etc
• Star schema, normalization,
• Data warehouse, data marts, decision support systems
Insurance DW is complex not large
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BI has been successful in many companies, but despite this business case can be hard to justify
Build logic into the data structure not reports Insurance is complex and requires business expertise
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