The Institute of Risk Management

Flexibility and alternative uses of the IM

Raphael Borrel

Tamsin Abbey

Date: October 2014

Agenda

• Workgroup Objectives

• Key Model Uses

• Model capabilities for uses of the model

• Model limitations

Workgroup objectives

• The “flexibility and alternative uses of Internal Models” workgroup

purpose will be to identify:

• key uses for internal models

• flexibility and capabilities necessary to enable these uses

• identification and management of model limitations for these uses

• governance, modelling and other implementation challenges associated

with these uses

0%

25%

50%

75%

100%

Not used

Considered but not a major influence

One of the elements considered

Major decision factor

Model Uses Internal models are used as a major decision factor in the traditional areas of Capital, risk appetite and

reinsurance purchase decisions. They are increasingly considered in other strategic and business

decisions.

Chart 1: rating of the importance of uses of the model in decision making (from IMIF survey October 2014)

Users of the internal model The internal model is significantly used by risk and actuarial to support their own processes and to

produce management information. Key decision makers take into account outputs of the model into

business decisions. Profit centre managers are the ones who least use the internal model.

Understanding of the model is proportional to the amount of use stakeholders make of it.

Chart 2 Ranking of the level to which stakeholders

use the results of the internal model (from IMIF

survey October 2014)

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Not at all

Low

Medium

High

Chart 3 Ranking of the level to which stakeholders

understand the results of the internal model (from

IMIF survey October 2014)

Model Capabilities

“The idea of 10 dimensions might sound exciting, but they would cause

real problems if you forget where you parked your car.”

Stephen Hawking, The Grand Design

Uses requirements • The level of reliance a firm will place on its internal model is largely dependent on how mature that

model is. The capabilities of the model required at each level of maturity should be understood.

• The table below provides high level examples of capabilities expected for different levels of maturity

of uses of the model.

7

• Assess capital needs according to

various definitions: economic,

regulatory.

• Deliver a holistic view on tail statistical

losses

• Compare with Available eligible capital

(Solvency ratio)

• Understand dependencies between

risks

• Monitor, sssess drivers of risk at

different levels of probability and

granularity

• Accounting (P&L and B/S), economic

and regulatory view of capital.

• Allow for one year and forward-looking

risk analysis.

• Deliver risk adjusted return measures

on capital and economic Value

creation for insurance products and

investment strategy.

• Understand the risk adjusted trade off

between different opportunities over

different horizons (one year, planning

horizon, product life)

• Allow for diversification or portfolio

management at different levels of the

organization

Manage Capital Adequacy Support the management of risks Support performance management

Examples of typical capabilities required

Case Study: Risk management process

Formulate Risk Strategy

Risk Identification

Risk and Control Management

Scenario Analysis

Risk Appetite Monitoring

ORSA Process and Reporting

Regular monitoring of

current exposures

against risk appetite.

Review of risk limits

upon amber or red

triggers (in addition to

action taken).

Identify potential

scenarios.

Quantify the

scenarios.

Contingency plan

against the scenarios.

Assess controls

efficiency and

residual risks.

Selection and

implementation of

mitigation measures

Identify existing risks

against business

objectives.

Use of emerging risks

analysis to scan

horizon for long-term

risks and capital

issues.

Set risk strategy and

risk appetites.

Cascade into risk

tolerances and limits.

Regular reporting on

risk and solvency

position of the

company.

Case Study: Risk management process

Formulate risk

strategy

Risk

identification

Risk and control

management

Scenario

analysis

Risk appetite

monitoring

ORSA process

and reporting

• Assessment of risk strategy implications on

economic and regulatory capital.

• Use current view of risk profile to inform risk

appetite setting, formulate via iterative process.

• ORSA to support longer-term risk strategy

discussion.

Pro

ce

ss

Inte

rna

l m

od

el u

se

s

• Assessment of drivers of risk at relevant probability levels.

• Assessment and monitoring by risk type, i.e. Insurance,

Market, Credit and Operational.

• P&L attribution to identify unexpected historic risks and

deviations against plan.

Formulate Risk Strategy Risk Identification

• Regulatory and economic capital assessment.

• Multi-point risk distribution (risk appetite).

• One year and multi-year view of risk and capital, on

economic and GAAP (and SII) accounting bases, as aligned

with risk appetite.

Mo

de

ling

Cap

ab

ilitie

s

• P&L and balance sheet view of risk and capital.

• P&L attributions (including loss per economic and

accounting definition).

• Multi-point risk distribution.

Case Study: Risk management process

Formulate risk

strategy

Risk

identification

Risk and

control

management

Scenario

analysis

Risk appetite

monitoring

ORSA process

and reporting

• Assessment of drivers of risk at short return periods.

• Refinement of models post mitigation.

• Link back into internal model, e.g. through setting

operational risk capital model.

• Evaluation and optimisation of reinsurance and hedging

possibilities.

Pro

ce

ss

Inte

rna

l m

od

el u

se

s

• Identification of scenarios through understanding drivers of

balance sheet shocks.

• Stress testing and reverse stress testing.

• Assessment of some scenarios, e.g. net loss arising from

extreme cat losses.

• Link back into internal model,

• Understanding of capital ‘re-set’ in new environments

Risk and Control Management Scenario Analysis

• Model granularity by risk type and business line.

• P&L view of risk and capital.

• Performance metrics set on a risk adjusted basis.

Mo

de

ling

Cap

ab

ilitie

s

• Model granularity by risk type and business line.

• Balance sheet view of risk and capital, at granular level.

• Understanding of dependencies between risks.

• Models can be re-parameterised and run within manageable

timescales to produce reliable outputs, in a resource efficient

manner.

Case Study: Risk management process

Formulate risk

strategy

Risk

identification

Risk and control

management

Scenario

analysis

Risk appetite

monitoring

ORSA process

and reporting

• Confirmation that the reforecast business plan, based on the

balance sheet and actuals to date, is:

• Within risk appetites

• Delivering the required return on capital and value

creation.

• Evaluating effectiveness of risk mitigation measures

• Informing management action

Pro

ce

ss

Inte

rna

l m

od

el u

se

s

• Current view of risk profile.

• Expected future view of risk profile.

• Assessment of economic and regulatory capital.

Risk Appetite Monitoring ORSA Process and Reporting

• Regular model runs (potentially using simplified model).

• Multi-point risk distribution.

Mo

de

ling

Cap

ab

ilitie

s

• Quarterly model runs.

• Regulatory and economic capital assessment.

• One year and multi-year view of risk and capital.

“Remember that all models are wrong; the practical question is how

wrong do they have to be to not be useful”*.

Model limitations

*George E.P. Box and Norman R. Draper, Empirical Model-Building and Response Surfaces (1987), 74.

Model limitations Whilst the top 3 limitations of internal models are related to the performance of the model and the

availability of data and timeliness of data/outputs, it appears that additional flexibility is missing in

internal models to enable them to be more fit for purpose for the various uses intended.

Chart 4: Perceived impact of limitations on the effective use of the internal model (IMIF survey October 2014)

0%

25%

50%

75%

100%

Negligible impact

Minor impact

Major impact

Case Study 1: Dependence approach • The selection of an approach (correlations, copulas) to model the relationship between the various

risks has major implications on the quality of the outputs of the internal model and its fitness for

purpose for various uses.

• For example, certain copulas will assume higher correlations in the tail to reflect a stronger link

between risks in extreme conditions and less correlation under normal conditions. Other copulas

will assume very little correlation in the tail.

• Some uses such as calculation of the SCR require accuracy at the 99.5%. Some other uses such

as measuring the volatility of earnings will require more accuracy at lower return periods (e.g 1 in 10

year event). The choice of a specific approach may lead the model to have more limitations for

specific uses that require outputs that it is not so good at modelling.

0.1 1.2 2.3 3.4 4.5 5.6 6.7 7.8 8.9 10

0.1

1.2

2.3

3.4

4.5

5.6

6.7

7.8

8.9

10Normal Joint Unit Lognormal Density Tau = .35

0.153-0.17

0.136-0.153

0.119-0.136

0.102-0.119

0.085-0.102

0.068-0.085

0.051-0.068

0.034-0.051

0.017-0.034

0-0.017

Figure 2

The normal and Frank copulas graphed in Figures 1 and 2 do not produce a strong rela-

tionship between large losses, although the normal shows a slightly stronger relationship,

whereas the Frank is stronger around the mode.

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0.1 1.2 2.3 3.4 4.5 5.6 6.7 7.8 8.9 10

0.1

1.2

2.3

3.4

4.5

5.6

6.7

7.8

8.9

10

Gumbel Joint Unit Lognormal Density Tau = .35

0.187-0.204

0.17-0.187

0.153-0.17

0.136-0.153

0.119-0.136

0.102-0.119

0.085-0.102

0.068-0.085

0.051-0.068

0.034-0.051

0.017-0.034

0-0.017

Figure 3

In contrast, the Gumbel copula keeps a strong relationship even for the large losses, as

seen in the higher values of the density function in the upper right of Figure 3.

10

The heavy right tail (HRT) copula is even stronger in right tail correlation than is the

Gumbel. While difficult to see in Figure 4, it is also weaker in the left tail. This will be

more clear with the tail concentration functions discussed below.

0.1 1.2 2.3 3.4 4.5 5.6 6.7 7.8 8.9 10

0.1

1.2

2.3

3.4

4.5

5.6

6.7

7.8

8.9

10

HRT Joint Unit Lognormal Density Tau = .35

0.187-0.204

0.17-0.187

0.153-0.17

0.136-0.153

0.119-0.136

0.102-0.119

0.085-0.102

0.068-0.085

0.051-0.068

0.034-0.051

0.017-0.034

0-0.017

Figure 4

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Lower correlation in more extreme situations

Higher correlations in more extreme situations

Case study 2: Combined LOB analysis

• A company parameterised large losses severity and frequency across

territories at business line level due to the relatively small datasets at

portfolio level. Frequency and severity were then scaled at territory

level.

• This limitation was not known to the management who were using the

model for strategic planning purposes at territory level.

• Limitations of the model directly impacted this specific use because

the various territories were assumed to have the same underlying risk

profile, hence country and portfolio particulars were ignored.

Draft: Current working draft

Thanks to workgroup participants for their contributions

• Jonathan Bilbul, AIG

• Alastair Clarkson, Standard Life

• Dan Apollos, LV=

• Visesh Gosrani, Cardif Pinnacle

• Justin Le Blanc, Deloitte

• Julien Masselot, Markel

• Parth Patel, Ascot

• Sebastian Rath, Nationale Nederlande

• Justin Skinner, QBE

Questions?