300 Years of Data Analytics in Life Insurance

Matt Ralph and Avanti Patki © Matt Ralph & Avanti Patki

This presentation has been prepared for the 2016 Financial Services Forum.

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.

Data analytics in life insurance is old news…

…300 years old!

When did the Life Insurance industry stop being the juggernaut of data analytics?

BC 2000 1700 1800 1600 1900

Worker’s comp innovation, Telematics,

Internet of Things for P&C

Ancient Greece: Benevolent societies.

Pensions (likely little use of

formal analysis)

England: Friendly Societies

established (likely little use of formal analysis)

John Graunt’s 1662 work on

mortality tables, the birth of actuarial analytics

DeMoivre discovered more innovative

analytics techniques

Bill of Mortality kept weekly in London and

large cities on the Continent of Europe to warn the upper classes when the incidence of plague was starting to increase so they could leave the city for the

supposed safety of the countryside

First life assurance company based on

actuarial science founded (the

Equitable)

Athenian, Babylonian and Chinese traders

bought/sold maritime

insurance (likely little use of

formal analysis)

First P&C insurer, and business

insurer established

(likely little use of formal analysis)

First offering of Accident

Insurance based on basic analytics

GLMs and Empirical Bayes (a.k.a. credibility) techniques start being used to price general insurance contracts

Life

General

Sophistication of data analytics

What does data analytics look like in Life Insurance today?

350 years ago

What does data analytics look like in General Insurance today?

• Modelling need became the mother of invention (e.g. auto, flood insurance)

• Alternatives data sources (IoT e.g. telematics in auto insurance)

• Workers Comp (analysis vests) • P&C Insurance (risk analysing drones)

What does data analytics look like in other industries today?

• McDonalds: automated drive thru menus

• Tel Aviv Highway 1: higher tolls at busier times

• LA crime watch: algorithm to predict where crimes are likely to occur

• Southwest Airlines: speech analytics used on customer calls

• Morton’s steakhouse: real time response to (joke) customer requests

How do these industries compare in their customer appeal?

LIFE INSURANCE

GENERAL INSURANCE

OTHER INDUSTRIES

We all know there’s value for Life Insurers in the data analytics space…

Product Management

Sales & Distribution

New business underwriting

Claims Management

Customer Interactions

• Predict what customers want before they ask for it

• Continuous real-time underwriting

• Leverage human digital records to tailor services

• Targeted marketing

• Robo-advice

• Agent recruitment and monitoring

• Leverage social media for customer knowledge and direct distribution (WeChat in China)

• Customers now understand their own risk much better (wearables, apps e.g. MyFitnessPal, Pinterest)

• Shorter, less intrusive underwriting

• New predictors of health, using

data other than population studies (23&Me)

• Fraud detection using predictive analysis

• Shorten claims cycle times: o higher customer satisfaction o optimised instant payout limits o reduced labour costs o faster turnaround times for customers

• Easier classification of claims complexity

• Reduce and management litigation costs

• Unstructured voice data analysis and learning

• 360 degree view of customer behaviour on social media

• Retention focus on high value customers, employees, brokers

• Customer wellness and care management

…but life insurers still haven’t taken the leap!

What is stopping them?

DATA AND INFRASTRUCTURE • Quality • Usability • Quantity • Non traditional data • Modelled variable • Legacy systems • Customer delivery

REGULATION AND UNDERINVESTMENT

• Compliance • Insights ‘legal’? • New infrastructure • The right talent • Customer delivery • Longer payback period • Tangible bottomline impact

INSULAR AND COMPLEX INDUSTRY

• Why fix what ain’t broke • No challengers (till now!) • Fintechs: disruptors, not

opportunities

ETHICS AND PRIVACY

• Different “purpose”

compared to GI • Stealth pricing • Convenient vs creepy

CUSTOMER COLLABORATION

• Customers uncomfortable

providing data • LI not perceived as

innovative • Customer trust

INTERNAL CULTURE

• Box ticker BAU? • Set up for real-time insights

…a plethora of issues – the funnel of doom

Internal Culture

Customer Collaboration

`

Data & Infrastructure

Investment and Resources

Environment

Ethics

$#!@

Despite this, analytics-focused innovation is creeping into the life insurance industry

Bought by Many An example of how niche markets are being tapped into using data analytics, addressing underinsurance in the Australian life insurance market

LIME: Life Insurance Made Easy An example of how ‘data-driven’ doesn’t just mean adding more systems…it’s a complete facelift for the entire value chain

Beagle Street An example of how the customer view of life insurance is being totally transformed

Build our own

fandoms

Start saying yes!

Join the fintechs, take the

investment leap

Embrace our legacy,

become the analytics

gurus once again

‘If I had asked people what they wanted, they would have said

faster horses’ - Let’s stop

building faster horses…