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Income Protection – managing the
cycles
Ashutosh Bhalerao, Luv Bhatnagar,
Phin Wern Ting
© < ClearView>
This presentation has been prepared for the Actuaries Institute 2018 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.
Agenda
1. Current state of play
2. Are economic factors an ‘underestimated’ driver of IP claims
cost?
3. How can we better manage the cycle?
Setting the scene
0
500
1,000
1,500
2,000
2,500
3,000
30-Sep-07 30-Sep-12 30-Sep-16 30-Sep-17
Retail Income Protection - Inforce Premium ($'m) **
CAGR ~8.5%
** Strategic Insight 10 Year Review, 2007-2017
Is the Retail IP
segment growing?
Setting the scene
➢ Retail IP profit margins (pre tax) / Net premium rolling
-40.0%
-30.0%
-20.0%
-10.0%
0.0%
10.0%
20.0%
30.0%
Jun 2009 Dec 2009 Jun 2010 Dec 2010 Jun 2011 Dec 2011 Jun 2012 Dec 2012 Jun 2013 Dec 2013 Jun 2014 Dec 2014 Jun 2015 Dec 2015 Jun 2016 Dec 2016 Jun 2017 Dec 2017
Profit margins (pre-tax) / Net Premium Rolling 12 months
What’s happening
with profitability?
** APRA quarterly statistics
1. Volatility
2. Systemically
under-priced
3. Potential long
term trend?
Only 2 periods
with profit
margins > 10% !
Setting the scene
➢ Industry has attributed this to a ‘sustainability issue’
➢ Benefit design issues (e.g. one-duty, 10 hour rule, able to work in WP, generous built in terms which could be optional etc.), no doubt have a claims cost effect. But, why are they causing a deterioration in the last 10 years?
➢ Growing number of mental illness claims.
➢ In our view the adverse experience is more driven by economic conditions (e.g. Unemployment, under-employment, slow wage growth) and the extent to which these have had an impact on Income Replacement Ratios.
A model of drivers of IP experience
IPClaims
Cost
Un
em
plo
y-m
en
t
Un
de
rem
-p
loym
en
tSociety
Evolution(C21)
Nature of work(Gigs,
knowledge, etc)
MentalStress/
strength
TrendCyclical
Unemployment rate
decreasing but
underemployment
trending upwards
Wage growth
across all
industries tracking
CPI (in some
periods below,
including recent
periods)
What’s the current economic landscape?
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
10.0%
Mar 2009 Aug 2010 Dec 2011 May 2013 Sep 2014 Jan 2016 Jun 2017
Economic factors
Unemployment rate Underemployment rate Wage Growth CPI
What does this mean for the IP product?
➢ Does IP claim cost have an economic linkage?
➢ Pro-cyclical vs anti-cyclical?
➢ Performance will vary by sector – can be a 2 speed economy!
➢ Is there an occupation linkage? What about Self-employed individuals?
➢ Benefit indexation has over the recent periods outpaced wage growth.
➢ What do these factors do to Income Replacement Ratios at policy inception vs point of claim?
But first, what can we do statistically?
70%
80%
90%
100%
110%
120%
130%
140%
88 89 90 91 92 93 94 95 96 97 98 99 00 01 02 03 04 05 06 07 08 09 10 11 12 13
Claims cost % FSC-KPMG ADI 2007-2011
ADI 2007-2011
Source: 2002 Report of the Disability Committee, FSC-KPMG Disability Income Experience Investigation 2009-2013
IAD 89-93
(re-based to ADI)
No industry data
Multivariate regression
Regression equation:
Claims costs AvE = - 0.05 + 22.8 x Underemployment – 1.4 x Unemployment – 8.6 x % ∆ AWE
Adjusted R-squared = 68%Significance of model F-statistic = 0.03 (< 0.05)
Correlation with Claims cost AvE
Underemployment rate 79%
Unemployment rate 37%
% change in AWE -10%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
0.0%
1.0%
2.0%
3.0%
4.0%
5.0%
6.0%
7.0%
8.0%
9.0%
10.0%
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
Wa
ge
Gro
wth
Un
em
plo
ym
en
t a
nd
un
de
rem
plo
ym
en
t
Economic indicators
Underemployment rate Unemployment rate % change in AWE
Source: ABS
Slow wage growth
Rising underemployment
80%
90%
100%
110%
120%
130%
140%
150%
160%
170%
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
Actual vs Predicted claims cost AvE
Predicted Actual
Observations
➢ What does this mean for claims cost?
➢ Declining NPAT, rising loss ratios from APRA statistics
➢ The statistics suggest an anti-cyclical trend?
➢ The exposure to sector and occupation will vary by insurer
➢ Lag or lead?
Incidence Termination
Employed Lag? Lag?
Self-employed Lead? Lag?
Further considerations
➢ Model limitations
➢ Lags in economic indicators
➢ Incidence versus termination experience
➢ Other studies
– Australia & South Africa
• Increase in claims incidence when unemployment rises
How have income replacement ratios moved over time?
➢ Lack of data to get this information directly.
➢ To answer this, we built a model with the following simplifying assumptions:
➢ Incomes for new policies have increased by AWE each year
➢ Anti-selective wage growth: -1% drift on inforce wage growth
➢ Initial replacement ratio is 75% for each year of sale
➢ Portfolio lapse rate of 12.5% p.a.
➢ Sum insured indexation take-up rate is 85%
➢ Average sum insured indexation rate is 4% p.a.
➢ Partial lapse rate (i.e. sum insured decrease) is 0.5% p.a.
How have income replacement ratios moved over time? Key takeaways
➢ IRRs have increased in recent times due to slow wage growth
➢ Claims will be linked to IRRs , which would explain some of recent deterioration
70.0%
71.0%
72.0%
73.0%
74.0%
75.0%
76.0%
77.0%
78.0%
79.0%
80.0%
Inco
me
Rep
lace
men
t R
atio
Year
Income Replacement Ratio - for portfolio
Portfolio average
How do we better manage the cycles?
Key aspects are…..
➢ Data
➢ Reinsurance Strategy
➢ Reserving
Data
➢ Ideally would track income replacement ratios (IRRs) including:
➢ IRRs at policy commencement versus claim time
➢ Estimated IRR for portfolio currently (broken by sector) and how this may change in
future
➢ However, currently difficult to get this as:
➢ Policy commencement - Financial evidence only always requested for guaranteed
agreed value (above limits for agreed value and indemnity)
➢ Renewal – no data collected
➢ Claim Time – income information only collected for indemnity (highest average in
last 3 years) and agreed value (highest average from one year pre policy
inception).
Would a bank not know the LVR on its loan portfolio?
Reinsurance
Economy improves, profits
made, insurer and reinsurer
prices drop
Traditional Pricing
and Reinsurance
makes the system
more volatile!
Economy worsens, losses made,
insurer and reinsurer prices
increase
Alternative Reinsurance Strategy
➢ Build automatic repricing in the treaty:
➢ Profit in the current year results in reinsurance rate dropping (like an automatic
credibility adjustment)
➢ Loss in the current year results in reinsurance rate increasing
➢ For example:
➢ Gross Premium p.a. = $200
➢ Reinsurance premium = $100 p.a.
➢ In Year 1, say actual reinsured claims are $60, compared to $85 expected (i.e. $25
profit for the reinsurer)
➢ Average duration is 10 years
Impact of $25 Profit on Reinsurance Premiums – Year 1
New Reinsurance Premium (Year 3) = $100 -$2.5= $97.5
Change in Reinsurance Premium = -$25/10=-$2.5
Original Reinsurance
Premium (Year 1) = $100
Years
$25
Profit
Impact of $10 Loss on Reinsurance Premiums – Year 2
$10
Claims
Loss
Original Reinsurance Premium (Year 2) = $97.5
Change in Reinsurance Premium = $10/10=-$1
New Reinsurance Premium
(Year 2) = $97.5 +$1 = $98.5
Years
$25
Profit
• Direct insurer pricing based on long term expected claims and reinsurance premiums
• Reduced reinsurance premiums from good times help pay for adverse experience in economic downturn => helps reduce pricing volatility for direct insurers
Reserving and Pricing➢ Assumptions should be more dynamic
➢ Allow for mean reversion:
➢ Incidence and Termination
➢ Allow for expected changes in IRRs based on forecast wage growth, underemployment
etc.
➢ Industry has failed to do this in previous cycles
A model of drivers of IP experience
Cyclical
Assumptions
Mean
reversion
Reserving
Reinsurance
Game theory
IP
Claims
Cost
Un
em
plo
y
-me
nt
Un
de
rem
-
plo
ym
en
t
Long term
Trends
Product
positioning
Claims mngt
RTW/Health
Early
intervention
Price changes
(up and down)
inevitable
Product
management
over time
So what??➢ IP experience impacted by:
➢ Cyclical factors– economic drivers such as wage growth, underemployment =>
resulting impact on IRRs
➢ Long term trends – mental illness, nature of work
➢ Benefit definitions have not changed, yet experience has deteriorated
➢ There are opportunities for better cycle management
➢ Allowing for mean reversion in pricing and reserving
➢ Reinsurance structuring
➢ We need to collect better data as an industry!!
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