life2016: better mortality modelling for better pricing

Better Mortality Modelling

for Better Pricing

Matthew Edwards and

Sacha Dhamani,

Head of Longevity, Prudential

Closing remarks

Session 4Chaired by Marcus Bowser

Life2016

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PulseModel

Improvements

Anti-selection

Underwriting and longevity risk

Current usage

Better Mortality Modelling for Better Pricing

- Agenda

PulseModel – Improved Pricing and Risk Management


Platform

The model is built in Igloo

Excel interface for full

parameter visibility (and

change parameters) so no

Igloo skill is required.

Stochastic / Monte Carlo Medical input

Primary care dataset CPRD

used for base

parameterisation

Risk factors include BMI,

HbA1c, smoker, postcode

group, duration since

diagnosis

Future trends from panel of

medical experts

(parameters & rationale)

Key Features

Multi-state model which

tracks disease status from

healthy (or with disease)

through to death

Parameters and risk factors

from UK medical dataset

Medically-informed views of

improvements

Business

Applications

Improved Segmentation

Medical underwriting

Improvement assumptions

Mortality of healthy lives

Mortality at high ages

Risk & regulatory challenge

Model Structure


DeathHealthy

Heart

Diabetes

Neuro

Stroke

Digestive

Cancer

Respiratory

Transitions Over Time



PulseModel

Improvements

Anti-selection


Current usage


- Agenda

Input from Medical Experts – Stroke Trends

© 2016 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.

Key factors in last 20 years’ stroke

mortality

Stroke units established

Stroke service centralised

Small gains from:

Thrombolysis

Thrombectomies

Surgical procedures

Looking forward …

Stroke units – no more to do

Thrombolysis effect small

Thrombectomies – small number of cases,

few experts

Surgical procedures – only for small % of

ischaemic strokes, not really for haemorrhagic

7

Input from Medical Experts – Diabetes Trends


What’s happened …

Approximately 700 new cases / day

Main risk factor is excess adiposity

Type 2 diabetes increases mortality by 30%

(macrovascular disease)

Some treatments available (eg GLP-1 based therapies)

but cost 2-6 x current insulin approach

Looking forward …

There are 4.75 million UK adults with ‘pre-diabetes’ (+

11.5 m higher risk)

‘An immense pool of potential new cases of diabetes’

In theory lifestyle modification can help but in practice in

the UK this does not happen

Treatment costs likely to fall in next 10 years as existing

drugs come off license

Input from Medical Experts – Eggs and Bacon



PulseModel

Improvements

Anti-selection


Current usage


- Agenda

Healthy and Unhealthy Mortality


Mortality of healthy v average

v enhanced at retirement

Also as a lower bound we

have a

‘permanently healthy’ life

Healthy Versus Normal Mortality


PulseModel can be used to compare

the mortality of males of identical

age, one of whom was known to be

healthy at age 60 (lower

line), the other was known to be

healthy at age 50.

The second graph represents the

same numbers in relative terms.

The length of the select period is

surprising to actuaries used to the 2

or 5 yr select periods of CMI tables.

0.00

0.02

0.04

0.06

0.08

0.10

0.12

60 65 70 75 80 85 90

Absolute mortality

0

0.2

0.4

0.6

0.8

1

1.2

60 65 70 75 80 85 90

Relative 'select effect'


PulseModel

Improvements

Anti-selection


Current usage


- Agenda

What Factors are Important?

14

Finding profitable niches requires precise

segmentation of mortality risk

Base case: Paul is a 40-year old ex-smoker with ‘podgy’ BMI, average

socio-economic status, and average blood sugar levels.

Adverse change in characteristics How many years older does

this equate to?

Obese (BMI > 30) + 3.3 years

High blood sugar + 1.8 years

Blue collar (lowest postcode quintile) + 3.6 years

Smoking + 6.6 years

Diabetes + 12.3 years

Neurological condition + 18.5 years

Positive change in

characteristics

How many sex changes does

this equate to?

Name becomes Sarah 1 (by definition!)

Loses weight 0.2

Forgets she ever smoke 0.6

White lace (highest quintile) 1.1

Rejuvenation therapy (1 year) 0.5

What are the effects on term assurance rates

of changes to the main rating factors?

We can compare with market term rates. The smoker/non-smoker effect from our model is 8.3 years, compared with a median of 8.2 years from mystery shopping – but with a wide range (7 to 9 yrs).

© 2016 Willis Towers Watson. All rights reserved. Proprietary and Confidential. For Willis Towers Watson and Willis Towers Watson client use only.

What is Longevity Risk?


Longevity risk can be

decomposed into two risks:

Morbidity: the risk of

people staying healthy

longer than expected

Mortality: the risk of ill

people living longer than

expected

We can use the model to isolate

the two effects

Group Life expectancy (rounded)

Enhanced 17.5 years

Population 20.0 years

Healthy at start 21.5 years

Healthy at start and diabetes-proof 23.0 years

Always healthy (disease proof!) 26.5 years

We saw before

the curves for

different groups

Longevity Risk – Improvements


Improvements derive from changes in disease incidence (eg fewer people having strokes) and

changes in disease mortality (eg fewer cancer patients die). How do these balance?

Longevity Risk – Stresses


Healthy Average (mixed health) Impaired

PulseModel also allows us to compare stresses applicable to impaired

portfolios compared with stresses on normal (or healthy) portfolios

Stress to the impaired portfolio is materially higher than stress to a

normal portfolio


PulseModel

Improvements

Anti-selection


Current usage


- Agenda

Current Areas Where PulseModel is Being Used


Applications

Segmentation

Medical underwriting

Improvements

Mortality of healthy lives

Mortality at high ages

Risk & regulatory

challenge

Project with a European

firm to improve term

business segmentation

Pension scheme

longevity risk project –

using the model to

quantify plausible trend

variations

Automated medical

underwriting for term /

dread disease (Europe)

Licensing the

improvements module

to two firms to improve

the robustness of their

trend assumption

Flexible and modular approach

https://www.towerswatson.com/en/Services/Tools/PulseModel

QuestionsLife2016


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