Capturing the Black Swan – Managing Risks with ReinsuranceClaims uncertainty is the biggest risk for insurers, and reinsurance is a crucial risk management tool. It is vital for all insurance directors and senior managers to understand the gross exposures, as well as how and why reinsurance arrangements are structured.

We expect that after the Reserve Bank of New Zealand wraps

up insurer licensing, it will turn its attention to risk management

and how insurers are responding in the post-Canterbury

earthquake environment. The recent Cook Straight earthquakes

are another reminder of the significant catastrophe claims risk

which New Zealand insurers face.

In this edition:>> The challenges

of determining an

insurer’s PML

>> The use of catastrophe

models, and some of

their limitations

>> Roles and responsibilities

for determining the PML

>> Potential reinsurance

program pitfalls.

d’finitiveReinsurance as a component of Risk Management. SEPTEMBER 2013

[ New Zealand ]

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Sydney +61 2 8252 3300 Auckland +64 9 363 2894 Melbourne +61 3 8080 0900 Wellington +64 4 460 5213

It’s vital that NZ insurance boards know their responsibilities and have a clear approach to managing their Probable Maximum Loss (PML) estimation process. In this d’finitive we outline the key steps to be considered.

2 d’finitive SEPTEMBER 2013

Estimating the Probable Maximum Loss (PML)The Canterbury earthquakes highlighted the importance of understanding extreme event exposure in New Zealand. There is now greater awareness that:

>> Catastrophe models are not always reliable and can provide a false sense of security

>> Directors need to understand exposures and the reinsurance program

>> Boards need to consider the potential implications of events larger than the adopted PML

>> The logistics of responding to a major catastrophe are enormously complex.

The bottom line is that an insurer’s catastrophe exposure, and the PML, can never be ‘known’ – it can only be estimated.

Using Catastrophe ModelsWhere do catastrophe models fit?

Catastrophe models are a core part of the exposure and capital management process, and represent the key input into estimation of the PML. They are not usually enough on their own, however, as:

>> The models do not address all hazards, or all claim costs (we discuss how to adjust for this below)

>> Other information about past and potential catastrophes is an important input, particularly non-modelled events

>> Insurer risk appetite will determine the return period that is chosen for the PML estimate.

The diagram below illustrates the interactions between the different components of the exposure and capital management process.

CATASTROPHE MODELS

PML

REINSURANCE

OTHER CATASTROPHE DATA

RISK APPETITE

CAPITAL MANAGEMENT

Catastrophe models must be considered in the broader financial management context

Uncertainty in Catastrophe ModelsCatastrophe models are only as good as the assumptions which are fed into them, and represent ‘best endeavour’ estimates at a point in time. It is useful to understand the level of uncertainty involved in the modelling.

The graph below illustrates the extent of model uncertainty in a typical catastrophe model. The 95% confidence interval is very large, and widens as the return period increases.

SEPTEMBER 2013 d’finitive 3

Incu

rre

d C

ost

($

m)

500

450

400

350

300

250

200

150

100

50

-

1,000800600400200-

Return Period (yrs)

The RBNZ requires attestation by directors of the 1 in 1,000 year Catastrophe Risk Capital Charge component – that is, the 1 in 1,000 year PML; the Appointed Actuary is required to comment on the basis for its determination. The uncertainty in the PML estimate at this level is extreme.

The 1 in 1,000 year requirement takes a very conservative view compared to other regulators (e.g. Australia requires 1 in 200 year return period). An insurer needs to be ‘comfortable’ with its adopted PML, and with the risk of exceeding it. The adopted PML should be defensible, with sufficient analysis and documentation.

PML

95% Confidence Interval

The first step is to adjust the catastrophe model results for known omissions. This will mean allowing for:

>> Non-modelled perils

>> Secondary hazards which aren’t modelled – liquefaction following earthquakes has in the past been excluded

>> Higher loss adjustment expenses following an event

>> Post-event demand surge inflation.

Once the modelling work is complete, it is important to compare the estimated PML with Realistic Disaster Scenarios (RDS) to check its reasonableness.

The second step is to deal with model uncertainty. This can be done in a number of ways, which are set out below.

4 d’finitive SEPTEMBER 2013

DEALING WITH UNCERTAINTY

MAKE NO ADJUSTMENT

BLENDING RESULTS OF

VARIOUS CATASTROPHE

MODELS

ADOPT A HIGHER RETURN

PERIOD

ADD A JUDGEMENTAL

MARGIN

ADD EXPLICIT MARGIN TIED TO ASSESSMENT OF

UNCERTAINTY

Estimating the PML – a Framework

The diagram below shows the main steps in estimating the PML. At each step of the process the ‘strength’ of the PML estimate improves.

CATASTROPHE MODELLING

RESULTS

ADJUSTMENT FOR KNOWN OMISSIONS

DEALING WITH UNCERTAINTY

Set risk appetite

Agree framework

Oversight

Approve PML and RI

Agree framework

Implement oversight process

Recommend PML and RI

Develop decision making

framework

Consistency with targets

and risk appetite

Consideration of key risks and

uncertainty

Determine Realistic Disaster

Scenarios

Design of reinsurance

arrangements

Improving data quality

Dialogue with modellers

Assess gaps in modelling

Running models

Assessment of models

Test Sensitivities

Model Realistic Disaster

Scenarios

CEO / CFO ACTUARY / CROREINSURANCE

MANAGERCATASTROPHE

MODELLERS

PML Assessment – Roles and ResponsibilitiesThe roles played in determining and approving an insurer’s PML should be clear, and should relate to the skills that can be brought to the review. The following diagram sets out possible roles and responsibilities for reviewing and approving a company’s PML and how these fit in to the broader reinsurance management context. Central in the process is the Appointed Actuary and/or CRO who is generally tasked with developing the decision making framework and guiding the review to completion.

Keeping control of the PML Process

To maximise control of the PML estimation process, and understand the uncertainty involved, insurers should:

>> Clarify who will take responsibility for assessing the PML

>> Implement a clear framework, and clear process for oversight

>> Communicate with cat modellers – particularly around model uncertainty

>> Document the process and assumptions clearly

>> Educate Board members so that they are in a position to confidently sign off the PML and the reinsurance arrangements.

SEPTEMBER 2013 d’finitive 5

BOARD

Defining roles and responsibilities will improve risk management

6 d’finitive SEPTEMBER 2013

What should Boards be checking?

Here are some questions for directors to ask when they consider the PML estimation process.

Reinsurance Program Design – Potential Pitfalls

Designing a reinsurance program can be a complex task, and executives and directors will gain comfort by having a detailed understanding of the underlying process. Importantly, asking the ‘right’ questions can bring clarity.

The table below sets out some of the potential pitfalls of program design.

Linked to oversight>> How did we assess how

good our data is?

>> How did we assess different models?

>> How did we assess what perils are not modelled?

>> How does the level of protection compare to our competitors?

>> What analysis have we done to understand the sensitivity of the results to key assumptions?

As evidence of process>> What model are we

using and why?

>> What allowance is made for growth?

>> What costs are not allowed for by the models?

>> What did we learn from the RDS?

Area Issue Consequence

Risk appetite Not set using a robust approach Risk appetite not consistent with the vision and strategy, resulting in inconsistent business decisions.

Roles and responsibilities Lack of clarity Potential gaps in risk mitigation.

Poor execution and oversight.

Exposure data Not testing quality of data used in modelling catastrophe outcomes

Reinsurance arrangements materially under (or over) protecting the portfolio.

Use of models Trusting experts and the ‘black box’ to provide the answer, without questioning.

Not understanding model limitations, or sensitivity of results to key assumptions

Reinsurance program design that over or under responds to material loss events.

Adopted PML No explicit buffers, or reasons for applying buffers not clear

Insufficient protection at top end of program.

Jeremy Weight Tel + 64 9 363 2898jeremy.weight@finityconsulting.co.nz Auckland Office

Steve Curley Tel + 61 2 8252 3326 steve.curley@finity.com.au Sydney Office

Contact the Authors

Finity ConsultingFinity is one of Australia and New Zealand’s leading actuarial and management consulting firms. Specialising in general and health insurance, Finity works closely with large and niche insurers as well as government agencies to deliver world-class actuarial, pricing and strategic advice.

Finity’s Management Consulting team is armed with a wealth of general insurance knowledge. We provide tailored solutions to assist you in establishing, strengthening and growing your business – from strategy to process. We work together with you to give practical advice that allows you to make the best business decisions.

Contacts

John Smeed john.smeed@finityconsulting.co.nz + 64 9 363 2894

Graeme Adams graeme.adams@finity.com.au + 61 2 8252 3314

Jacob Mamutil jacob.mamutil@finity.com.au + 61 2 8252 3318

This article does not constitute either actuarial or investment advice. While Finity has taken reasonable care in compiling the information presented, Finity does not warrant that the information is correct.

Further clarification can be sought from our consultants.

Copyright © 2013 Finity Consulting Pty Limited

Finity is proud to have recently been named a finalist in the 2013 New Zealand Insurance Industry Awards for Service Provider of the Year.

Look out for news about our New Zealand

Directors Forum coming soon!

d’finitive[ New Zealand ]

www.finity.com.au

Finity Consulting Pty Limited ABN 89 111 470 270

Australia & New Zealand Insurance Industry Award ‘Service Provider of the Year’ 2006, 2007, 2008, 2009 and 2011.Australian Insurance Industry Awards - Inaugural Inductee into the Hall of Fame 2012.

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