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

-500

-400

-300

-200

-100

0

100

Dec

-10

Mar

-11

Jun-

11

Sep-

11

Dec

-11

Mar

-12

Jun-

12

Sep-

12

Dec

-12

Mar

-13

Jun-

13

Sep-

13

Dec

-13

Mar

-14

Jun-

14

Sep-

14

Dec

-14

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.”

-

2

4

6

8

10

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)

-

1

2

3

4

5

6

7

8

No Yes

Do you capture data on all interactions with a customer (phone calls, etc)?

-

1

2

3

4

5

6

7

8

No Yes

Are you linking data across systems?

Current Landscape – Level of analysis (ctd)

-

1

2

3

4

5

6

7

8

Mainly actuaries Specialistmaths/statistics

Other data/ITspecialists

What capability / professionals have been involved? Data scientists? Statisticians?

-

1

2

3

4

5

6

7

8

No Yes

Do you plan further development? Does it include specific system/data collection

enhancements?

Current Landscape – Level of analysis (ctd)

-

1

2

3

4

5

6

7

8

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