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Disability Income Data What do we need to capture and analyse now?
Michael Dermody & Chris Scheuber
© 2015 KPMG Actuarial
This presentation has been prepared for the Actuaries Institute 2015 Actuaries Summit.
The Institute Council wishes it to be understood that opinions put forward herein are not necessarily those of the Institute and the Council is not
responsible for those opinions.
Agenda
1. Current landscape 2. Insights from other regions & industries 3. Actions 4. Discussion
Industry Profitability The Retail Disability Income loss (-$394M or 43% of gross revenue) related to both Ordinary and Superannuation business. The large reduction in 10 year bond rate in the December quarter would partly, but not fully, explain these losses. The remainder would likely be due to further reserve strengthening.
Extract from KPMG Life Risk Profitability Update, December 2014 based on APRA Quarterly Life Insurance Performance Statistics
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Australian Industry Retail DI Experience
– Quarter to 31 December 2014
Super - Individual Disability Income Insurance Ordinary - Individual Disability Income Insurance
Prof
it/(L
oss)
afte
r ta
x $m
Industry’s View on Maturity of Current State & Value from Development
“Research, can’t believe more insight wouldn’t come out of that.” “Systems development about improving understanding – getting holistic view” “We started using external data sources – we got value out of it and are going to continue with it.”
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No Yes
Do you think reserving and rating factors will be different in future from what they are now?
Direct Life Offices Reinsurers
DI Sources of Data
Underwriting
Advice
Administration Claims Admin
Internal to group • Banking • General insurance
Claim Mgt Aids
Commission / Sales & Agent Management
External to group - Popln Segment
Internal to Life company External to Life Co
Point of Sale Policy Maintenance Claim Processing Agent Management
Phone calls
Paper Correspondence
Medical Reports
Rehab/ Investigator Reports Phone Calls
Medical Reports
Current Landscape – Existing Analysis Everyone
Annual experience investigations: - One way analysis - Some two way analysis
Some
Annual experience investigations with GLM
Some
Early warning signals/lead indicators - deep dive when results poor Targeted actuarial analysis Discussion with frontline staff including UW, claims. Individual claims file analysis
Embryonic
Group data analytics team Unstructured and structured
Starting to consider
Database with linked data across systems
Current Landscape – Level of analysis (ctd)
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No Yes
Do you capture data on all interactions with a customer (phone calls, etc)?
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No Yes
Are you linking data across systems?
Current Landscape – Level of analysis (ctd)
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Mainly actuaries Specialistmaths/statistics
Other data/ITspecialists
What capability / professionals have been involved? Data scientists? Statisticians?
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No Yes
Do you plan further development? Does it include specific system/data collection
enhancements?
Current Landscape – Level of analysis (ctd)
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Yes - dataelsewhere in
group
Yes - sourceddata external
to group
No - but clearplans/begun
exploring
No - butsomething like
to explore
No - and at thisstage nointerest
Does your analysis leverage external data sources:- Data from other parts of the group outside life co; and / or - Data external to group?
DI vs Other Insurances/Banking
Permanent Differences Volume Data Depth Free text, but… Ethical/legal/cultural
considerations
Temporary Differences Data quality Monitoring impacts of
change Data structure
DI – Discrimination Commonwealth Legislation* Insurance/Super exemptions Age Discrimination Act 2004 Age/Health Status and Sex: Able to charge
differential premiums on provided it is based on actuarial/statistical data and it is reasonable for the entity discriminating to rely on that data. Age/Health Status if no actuarial or statistical data is available/cannot reasonably be obtained then can charge different premiums where it is reasonable having regard to any other relevant factors. Racial discrimination not permitted.
Disability Discrimination Act 1992
Racial Discrimination Act 1975
Sex Discrimination Act 1984
*There are also anti-discrimination laws in each state which prohibit discrimination on a number of other grounds eg sexual orientation.
Insights from Other Regions Current Analysis Application to DI in Australia Consider external data (structured first then unstructured) – credit scores, medical / prescription information, demographics, etc
?
Unstructured analysis (no hypothesis – leave analysis to find relationships)
?
Text mining of claims notes, prescription data and treatment assists with understanding severity of claim
Likely relevant for DI in Australia – investment required regarding text mining or capturing information on severity in codified field.
Analysis by advisor helps to understand reasons for success in terms of volumes and profitability of business written
Relevant for DI in Australia – additional insight beyond analysis by channel and provides helpful feedback to the business
What do we do now? Leverage more existing internal data to do deeper analysis
Build data advances into the system
architecture
Overlay external data to analysis and decision making
Start collecting new data fields
Use insights from analysis for decision
making
Leverage more existing internal data to do deeper analysis
How to do it DI examples Connect data from separate unconnected systems / sources
• Claims to underwriting to admin • Linkage to internal banking data
Leverage existing unstructured data using text mining techniques
• Claims manager’s and underwriter’s notes • Claim causes
Correct existing data errors • Occupation class • Claim cause
Utilise more advanced techniques than one way factor analysis
• Generalised linear models • CART models
Leverage more existing internal data to do deeper analysis
How to do it DI examples Connect data from separate unconnected systems / sources
• Claims to underwriting to admin • Linkage to internal banking data
Leverage existing unstructured data using text mining techniques
• Claims manager’s and underwriter’s notes • Claim causes
Correct existing data errors • Occupation class • Claim cause
Utilise more advanced techniques than one way factor analysis
• Generalised linear models • CART models
Build data advances into the system architecture
How to do it DI examples Automate links between systems • A policy and/or customer ID can be created which is
looked up or pre-populated when entering data across all systems
Transform text fields into helpful drop-downs
• Transforming claim cause to a categorisation (ICD-10 and/or customised to include severity)
Build business rules to limit future data errors
• Not allowing important fields to be left blank • Ordering of dates for claims
DI Sources of Data
Underwriting
Advice
Administration Claims Admin
Internal to group • Banking • General insurance
Claim Mgt Aids
Commission / Sales & Agent Management
Database / Master File
External to group - Popln Segment
Internal to life Co
External to life Co
Point of Sale Policy Maintenance Claim Processing Agent Management
Phone calls
Paper Correspondence
Medical Reports
Rehab/ Investigator Reports Phone Calls
Start collecting new data fields How to do it DI examples When applying for a policy • BMI
• Credit information
Policy renewal and queries • Validate existing fields provided at underwriting
Claim inception and management • Current salary (even for agreed value) • Number of dependants • Offsets and different benefit types paid
Use insights from analysis for decision making
How to do it DI examples Underwriting • Use additional factors to differentiate pricing, bearing
in mind legislative requirements
Claims • Triaging of claims to focus effort better • Providing expected durations for claims team – provides understanding of fields not captured correctly
Retention • Identifies more accurately lost value from lapses of individual policies • Identifies policies more likely to lapse & more successful actions to retain them
Strategy • Identifies advisers who write most profitable business
Overlay external data to analysis and decision making
How to do it DI examples Claims • Social media to assist with investigations
Strategy • Credit scores by region • Mosaic or similar which categorises individuals by address & contains population level information
Discussion
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