Trends in life insurance pricing and opportunities for analytical techniques. Paul Swinhoe, Ting Lim Deloitte Actuaries & Consultants Limited

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1 Trends in life insurance pricing and opportunities for analytical techniques Paul Swinhoe, Ting Lim Deloitte Actuaries & Consultants Limited

2 Presentation topics Current industry issues and observations from recent pricing survey Opportunities for data analytical techniques Conclusions

3 Current industry issues Worsening experience in disability income business Increase in lapses for individual risk business Profit margins expected to reduce across most risk business

4 Worsening experience in Disability The profit margin of the disability income business has deteriorated in the past 2 years. Income business Profit Margin (%) Profit Margins Split by product groups 40.00% 35.00% 30.00% 25.00% 20.00% 15.00% 10.00% 5.00% 0.00% Dec-09 Dec-10 Dec % % % Individual Lump Sum Individual Disability Income Group Lump Sum Group Disability income Source: APRA statistics, Deloitte analysis

5 Worsening lapse experience 16.0% Lapse rates - Individual Risk 15.0% 14.0% 13.0% 12.0% Individual Risk Lump Sum Individual Risk Income Protection 11.0% Source: Plan4Life data, Deloitte analysis 10.0% Sep-06 Sep-07 Sep-08 Sep-09 Sep-10 Sep-11

6 Profit margins expected to reduce across most risk business Competition Product design creep is the main concern for trauma business More entrants into direct market Insurers will have to look for ways to improve the profit margin

7 Current use of data analytical techniques Life insurers are behind in using data analytical techniques to improve their decision making, compared with other financial services providers (e.g. banks/general insurers) There is interest in predictive modelling Lack of capabilities/resourcing is seen as the main barrier

8 Potential Data Sources Current and past application form(s) Personal & family medical history Industry data (APRA, P4L, ABS, Census data) Consumer marketing data Loyalty programs, Affinity group memberships Credit card spend Bank transactions Social media (LinkedIn, Twitter, facebook)? Traditional internal data sources Application Data Customer History Medical Data Non-traditional internal data and external individual or household-level data sources Company data Financial Data Consumer Data Lifestyle Data Household Data Non-traditional data unlock new insights into customer and employee populations

9 Opportunities for data analytical techniques Underwriting Direct marketing In-force management

10 Approach: Analyse standard app combined with marketing data Triage: accept/traditional approach to u/w Benefits: Case study 1 Underwriting Improved efficiency, lower cost, greater consistency Less invasive sales process (eg. fewer medicals) Free up experienced underwriters to focus on more difficult applications

Underwriting with Predictive Apply an Algorithmic Solution using application data, internal and 3 rd party data, together with underwriting rules established by the

11 Underwriting with Predictive Apply an Algorithmic Solution using application data, internal and 3 rd party data, together with underwriting rules established by the insurer Provide results that are similar to fully underwritten decisions for a significant portion of the business predominantly the higher scoring segments. Models

12 Case study 2 Direct marketing Approach: Use marketing data to identify people who are more likely to take-up and qualify for insurance Benefits: Optimal use of marketing budget Minimise acquisition costs => cheaper products Increased cross-sell

13 Marketing with predictive models Direct marketing database Non-traditional data 45% 40% 35% 30% 25% 20% 15% 10% Percent of Decile Marketing Model Desirability Model Healthier population 5% 0% Low Score Decline Substandard High Score Model Score High Score Low Score Low Score Continue current marketing efforts Do not target those least likely to respond or qualify for insurance Model Score High Score Focus marketing resources on the most attractive potential customers Continue current marketing efforts Focus limited resources on those most likely to purchase a product, and most likely to be healthy

14 Case study 3 In-Force Management Segmentation analysis can be used to identify groups of members displaying non-standard experience Results can improve pricing points, reserving accuracy At signs of imminent lapse (i.e. late premium, repeated request for surrender value) should insurer react, or let policyholders leave? Claims management or fraud detection

Retention with predictive models 200% Inforce business Likelihood of surrender relative to average Policyholders most likely to surrender Pop. Ave.

15 Retention with predictive models 200% Inforce business Likelihood of surrender relative to average Policyholders most likely to surrender Pop. Ave. Retention Model 0% Low Score High Score Model Score Low Score High Score Non-traditional data appended Marketing Model Model Score High Score Low Score Focus retention rerouces on the healthy customers most likely to surrender Continue current retention processes Continue current retention processes Spend fewer retention resources where they will have the least effect Spend retention efforts on those more likely to leave, but whom you want to keep

16 Challenges and Opportunities in Challenges Availability of data Legal and ethical concerns Opportunities Australia Putting all your data to use Partnerships (loyalty/rewards programs, credit cards)

17 Conclusions There is appetite for using analytical techniques to improve business decisions There are opportunities to improve business performance

General information only This presentation contains general information only, and none of Deloitte Touche Tohmatsu Limited, its member firms, or their related entities (collectively the Deloitte

18 General information only This presentation contains general information only, and none of Deloitte Touche Tohmatsu Limited, its member firms, or their related entities (collectively the Deloitte Network ) is, by means of this presentation, rendering professional advice or services. Before making any decision or taking any action that may affect your finances or your business, you should consult a qualified professional adviser. No entity in the Deloitte Network shall be responsible for any loss whatsoever sustained by any person who relies on this presentation. About Deloitte Deloitte refers to one or more of Deloitte Touche Tohmatsu Limited, a UK private company limited by guarantee, and its network of member firms, each of which is a legally separate and independent entity. Please see for a detailed description of the legal structure of Deloitte Touche Tohmatsu Limited and its member firms. Deloitte provides audit, tax, consulting, and financial advisory services to public and private clients spanning multiple industries. With a globally connected network of member firms in more than 150 countries, Deloitte brings world-class capabilities and deep local expertise to help clients succeed wherever they operate. Deloitte's approximately 170,000 professionals are committed to becoming the standard of excellence. About Deloitte Australia In Australia, the member firm is the Australian partnership of Deloitte Touche Tohmatsu. As one of Australia s leading professional services firms. Deloitte Touche Tohmatsu and its affiliates provide audit, tax, consulting, and financial advisory services through approximately 5,400 people across the country. Focused on the creation of value and growth, and known as an employer of choice for innovative human resources programs, we are dedicated to helping our clients and our people excel. For more information, please visit our web site at Liability limited by a scheme approved under Professional Standards Legislation. Member of Deloitte Touche Tohmatsu Limited 2012 Deloitte Actuaries & Consultants Limited.

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