Upload
others
View
1
Download
0
Embed Size (px)
Citation preview
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…