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CAS Exam 7 > Section C: ERM > Flashcards

Section C: ERM Flashcards

1
Q

Brehm Chapter 1:

Enterprise Risk Management

A

ERM is the process of systematically and comprehensively identifying critical risks, quantifying their impacts and implementing integrated strategies to maximize enterprise value.

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2
Q

Brehm Chapter 1: Key Aspects of ERM

A

Key Aspects of ERM

  • An effective ERM program should be a regular process not a one time event
  • Risks should be considered on an enterprise-wide basis
    • should consider risks other than insurance risks
  • ERM focuses on risks that have a significant impact on the value of the firm (material impact)
  • Risk can be positive or negative; its the fact that the actual outcomes stray from expected
  • Risks must be quantified as best as possible including correlations among risks
    • should be on overall portfolio basis
  • Strategies must be implemented to avoid, mitigate or exploit risk factors
  • To maximize firm value, should examine the tradeoff between risk and return
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3
Q

Brehm Chapter 1: Types of Insurance Company Risk Factors

A

Insurance Hazard Risk - risk assumed by the insurer for a premium

  • Underwriting - risk due to non-cat losses from current exposures
  • Accumulation/Cat-risk - risk due to cat losses from current exposures
  • Reserve Deterioration - risk due to losses from past exposures

Financial Risk - risk in the insurer’s asset portfolio due to volatility in interest rates, foreign exchange rates, equity prices, credit quality, and liquidity

Operational Risk - risk associated with the execution of the company’s business

  • actions taken by the company
  • e.g. execution of IT systems, policy service systems

Strategic Risks - the risks of strategic choices made by the company

  • the risk of choosing the wrong plan
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4
Q

Brehm Chapter 1:

Enterprise Risk Management Process

A

The ERM process can be described as a sequence of steps:

Diagnose - firm conducts a risk assessment to determine material risks that exceed a company-defined threshold

  • General Environment - political uncertainties, government policies, macroeconomic changes, catastrophes, etc.
  • Industry - supply (input market) and demand (product market) changes, competitive uncertainties
  • Firm Specific - labour changes, liability (product, pollution, employment), R&D

Analyze - risks that exceed a company threshold are modelled as best as possible:

  • risks are quantified with probability distributions of potential outcomes
  • recognize correlation among risk factors

Implement - implement various activities to manage the risks

  • risk avoidance
  • reduce risk occurrence
  • risk mitigation
  • risk transfer
  • retain the risk

Monitor - monitor the actual outcomes vs. expected and update plans

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5
Q

Brehm Chapter 1: Goal Enterprise Risk Modeling

A

Goal is to understand and quantify the relationships among risks from assets, liabilities and underwriting.

Enterprise Risk Models combine several risk sub-models to produce an overall risk profile of the business.

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6
Q

Brehm Chapter 1: Enterprise Risk Modeling

These models help with the insurer with important management functions and strategic decisions such as:

A
  • Determining capital needed to support risk, maintain ratings, etc.
  • Identifying significant sources of risk and cost of capital to support those risks
  • Setting reinsurance strategies
  • Planning growth
  • Managing asset mix
  • Valuing companies for M&A
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7
Q

Brehm Chapter 1:

Elements Needed for Effective ERM Model

A

A good enterprise risk model has the following characteristics:

  • Model reflects relative importance of various risks to business decisions
  • Modelers have deep knowledge of the fundamentals of those risks
  • Model includes mathematical techniques to reflect the relationships among risks (dependencies/correlations)
  • Modelers have a trusted relationship with senior management
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8
Q

Brehm Chapter 1:

What are the essential elements of the enterprise risk model?

A
  • Underwriting Risk
  • Reserve Risk
  • Asset Risk
  • Dependencies/Correlations
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9
Q

Brehm Chapter 1: Essential Elements of Enterprise Risk Model

Underwriting Risk

A

1. Loss Frequency and Severity Distributions

  • Used to quantify loss potential

2. Pricing Risk

  • Risk of reserve deficiency due to underpricing which may go unnoticed for some time (until losses accumulate)
  • Underwriting Cycle

3. Parameter Risk

  • Risk from mist-estimated parameters, imperfect model form, unmodeled risks

4. Catastrophe Modeling Uncertainty

  • Uncertainty in 3rd party CAT models (e.g. probability of event/loss)
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10
Q

Brehm Chapter 1: Essential Elements of Enterprise Risk Model

Underwriting Risk - Parameter Risk

A

Estimation Risk - misestimation of model parameters due to imperfect data

  • the risk that the form and parameters of the frequency/severity distributions don’t reflect the true form and parameters

Projection Risk - changes over time and the uncertainty in the projections of these changes. (e.g driving increases since fuel is cheaper, criminals attack security vehicles because banks are more secure)

  • examples of projections include trending frequency and severity to future periods AND loss development

Event Risk - events outside the company’s control that impact frequency/severity trends (e.g. class action, asbestos, new cause of loss, etc.)

Systematic Risk - risk that cannot be diversified away and affects many policies (such as inflation)

  • do not improve when volume is added
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11
Q

Brehm Chapter 1: Essential Elements of Enterprise Risk Model

Reserving Risk

A

Reserving Risk

  • Risk that reserve develop differently than expected
  • Reserve uncertainty impacts the amount of capital required and the time the capital must be held
  • A model can understate both a reserve estimate and reserve uncertainty
    • need a model of reserve uncertainty in an enterprise risk model
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12
Q

Brehm Chapter 1: Essential Elements of Enterprise Risk Model

Asset Risk

A

Asset Risk

  • Model asset risk by generating probabilistic scenarios based on historical patterns and testing the insurer’s strategy against the scenarios, taking into account the scenario likelihood
  • Model should account for:
    • bonds
    • equities
    • foreign exchange/interest rates

Note: key aspect is modeling scenarios consistent with historical patterns so its realistic

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13
Q

Brehm Chapter 1:

Sources of Dependency

A

Sources of Dependency

  • Inflation rates, interest rates, equity values, etc. are correlated and should be modeled as such in a macroeconomic model
  • UW cycles, insurance loss trends and reserve development are correlated across lines of business and with each other
  • CATs and other event risk are often correlated across lines of business
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14
Q

Brehm Chapter 1:

Modeling Dependency

A
  • Modeling tail dependency in extreme events is crucial when developing an enterprise risk model
  • Use copulas to incorporate dependency if there’s higher correlation in the tail
  • Correlation through a multivariate normal distribution has a low tail dependency
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15
Q

Brehm Chapter 1:

Why default avoidance isn’t the most important reference point to set capital

A
  • Default avoidance is about protecting current policyholders
  • To protect shareholders, the company should avoid significant partial losses of capital that could damage franchise value
    • could impact customer base, agency relationships, reputation
  • Loss that would cause a significant rating downgrade is more meaningful reference point than total default
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16
Q

Brehm Chapter 1:

Meaningful reference points for setting capital besides default

A
  • Maintaining enough capital to avoid rating downgrade below certain level
  • Maintaining enough capital to service renewal business
    • if writing renewal business requires 80% capital than an appropriate reference point is a 20% loss of capital
  • Maintaining enough capital so insurer thrives after a catastrophe (not just survives)
  • Holding enough capital to maximize insurers franchise value
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17
Q

Brehm Chapter 1:

Challenge when using extreme reference points to set capital

A
  • Model is least reliable in the extreme tail (e.g. exhausting all captial)
    • Little data far in the tail
    • Results are senstive to assumptions about the distribution
    • Far tail is poorly understood
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18
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

What is the three-step evolutionary process?

A
  1. Deterministic Project Analysis
  2. Risk Analysis
  3. Certainty Equivalent

*Trying to determine cashflows to use in IRR or NPV calculation to see how risks impact the value of the firm. Based on this info, company can make decisions.

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19
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

Deterministic Project Analysis

A

Deterministic Project Analysis

  • Uses a single deterministic forecast to estimate present value or IRR
  • Uncertainty is handled judgementally by decision makers
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20
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

Risk Analysis

A

Risk Analysis

  • Use forecasted distributions of the critical variables in a Monte Carlo simulation to calculate a distribution of present value of cashflows
    • simulating cashflows on a discounted basis based on risks deemed important or influential
    • helps to see the risk’s impact on company’s cashflows
  • The risk judgment is intuitive applied by decision makers
  • a.k.a. Dynamic Financial Analysis (DFA)
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21
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

Certainty Equivalent

A

Certainty Equivalent

  • Similar to risk analysis but quantifies the risk judgement with a corporate preference or utility function for consistency
    • so judgement can be consistently applied
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22
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

For a public firm, why might a certainty equivalent approach with a corporate risk preference be undesirable?

A
  • Diversified investors only care about risk that cannot be diversified away in their portfolios (systematic risk) so they won’t care about firm-specific risk as this can be diversified away
  • If management’s goal is to maximize shareholder value, they should also ignore firm-specific risk
    • Issues with this - difficult to determine which risks are systematic and which ones are firm-specific.
    • Also, market-based signals such as the risk adjusted discount rate, lack the refinement and discriminatory power that managers need to make cost-benefit and tradeoff decisions for mitigation or hedging
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23
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

There are 5 major elements in internal risk modeling. Brehm focuses on the 5th one.

What are the sub-components of the 5th component?

A
  1. Corporate Risk Tolerance
  2. Cost of Capital Allocated
  3. Cost Benefit Analysis on Mitigation and Hedging
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24
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

Decision making with IRM: We desire a mechanism with the following steps:

A

Step 1: Determine an aggregate loss distribution with many sources of risk (e.g. lines of business)

Step 2: Quantify or assess the impact of possible aggregate loss outcomes on the company

Step 3: Assign a cost to each amount of impact

  • how much is the risk costing the company

Step 4: Attribute the cost back to the risk sources

  • allocated the cost back to the line of business or other source (market risk, etc.)
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25
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

Internal Risk Model (IRM): Corporate Risk Tolerance

A

Corporate Risk Tolerance

  • Corporate Risk Tolerance is needed to quantify the impact of possible aggregate loss outcomes and assign a cost to each amount (impact)
  • Combination of the following factors is Corporate Risk Tolerance:
    • organization size
    • financial resources
    • ability and willingness to tolerate volatility
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26
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

Internal Risk Model (IRM): Cost of Capital Allocated

A

Cost of Capital Allocated

  • Cost of risk capital is allocated to the individual risk sources (e.g. line of business)
  • RAROC = Risk-Adjusted Captial • Hurdle Rate*
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27
Q

Brehm Chapter 2: Corporate Decision Making using ER Model

Internal Risk Model (IRM): Cost-Benefit Analysis for Risk Mitigation

A

Cost Benefit Analysis for Risk Mitigation

  • Any risk mitigation effort with a positive EVA is worth doing. This means the reduction of capital cost is greater than the cost of risk-mitigation.
  • EVA = NPV Return - Cost of Capital*
  • Cost of Capital = Risk Capital • Hurdle Rate
  • Using the EVA approach determines the cost of capital directly
  • If we use the capital allocation approach (where we have allocated capital) we would pursue activities where the decrease in capital required exceeds the cost of implementation.
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28
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Advantages of Economic Capital

A

Advantages of Economic Capital

  1. ​Provides unifying measure for all risks across an organization
  2. More meaningful to management than risk-based capital or capital adequacy ratios
  3. Forces the firm to quantify the risks it faces and combine them into a probability distribution
  4. Provides a framework for setting acceptable risk levels for an organization as a whole AND for individual business units
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29
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Moment-Based Measures:

Advantages & Disadvantages

A

Moment-Based Measures - use the moment of a random variable

  • random variable could be change in capital over an accounting period
  • measured by variance, standard deviation, skewness, etc.

Advantages

  • Reflects all losses in the distribution

Disadvantages

  • Standard Deviation treats favourable deviations the same as unfavourable ones
  • Exponential Moment - responds better to large losses*
  • Semi-Standard Deviation - only uses unfavourable deviations*
  • Skewness or higher moments may reflect market attitude better*
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30
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Tail-Based Measures: VaR

Advantages & Disadvantages

A

VaR - percentile of the probability distribution

Advantages

  • Emphasizes large losses

Disadvantages

  • Only looks at one point in the distribution
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31
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Tail-Based Measures: TVaR

Advantages & Disadvantages

A

TVaR - expected loss given a loss above a selected percentile of the probability distribution

  • VaR uses one point as the expected loss (e.g. 99th percentile) while TVaR uses the average of all the losses above the 99th percentile)

Advantages

  • Reflects losses that exceed VaR

Disadvantages

  • Losses are reflected linearly in the tail
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32
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Tail-Based Measures: XTVaR

A

XTVaRp% = TVaRp% - Mean

  • The mean loss might be funded by other means (other than capital such as premium) so capital is only needed for losses above the mean.
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33
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Tail-Based Measures: Expected Policyholder Deficit (EPD)

A

EPD = (TVaRp% - VaRp%) • (1-p%)

  • For EPD, the probability level is set so that the capital is VaR at that level.
  • The (TVaRp% - VaRp%) term is the expected value of default conditioned on that a default occurs.
  • The second term, (1-p%), makes the first term unconditional so it becomes the expected value of default.
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34
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Tail-Based Measures: Value of Default Option

A

Value of Default Option

When capital and/or reinsurance is exhausted, the firm has the right to default on its obligations and put the claims to the policyholders.

The market value of this risk is the value of the default put option which can be estimated based on option pricing.

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35
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Probability Transforms

A
  • Probability transforms measure risk by shifting the probability towards the unfavourable outcomes and then computing a risk measure with transformed probabilities

Examples:

  • Expected loss with transformed probabilities
  • Minimum martingale transform and minimum entropy martingale transform, Wang transform
  • Weighted risk measures: WVaR, WTVaR, WXTVaR
    • losses twice as large are twice as bad - for example, removes the linearity in the tail for TVaR which assumes that each loss above the tail gets the same weight (simple average in tail)
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36
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Generalized Moments

A

Generalized Moments - expectations of a random variable that are NOT simply powers of that variable.

Example:

  • Blurred VaR, which adds weight to losses around the percentile so we are not just looking at the loss percentile
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37
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

The amount of capital an insurance is required to hold is a function of several things:

A

The amount of capital an insurance is required to hold is a function of several things:

  • Customer Reaction - some care about insurers ratings so if the rating drops there could be a decline in business (more likely to see greater impact when rating decline vs. improve)
  • Capital Requirements of Rating Agencies - different requirements differ by agency
  • Comparative Profitability of New and Renewal Business - renewal business tends to be more profitable due to more informed pricing and underwriting
    • e.g. if renewals comprise 80% of the book then the insurer should be able to maintain 80% of its capital (coming from renewal business which is more profitable). Therefore, in a bad year if the company wishes to maintain this, then they should hold enough capital so that 20% of its capital could cover the adverse event.
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38
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Purpose of Capital Allocation

A

Purpose of Capital Allocation

  • helps to show the contribution of each business unit to the overall risk
  • can be used for calculating the risk-adjusted profitability by line of business and also setting capacity controls for those lines
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39
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Risk Allocation can be done in two ways

A
  1. Allocate the overall risk to the individual business units
  2. Estimate the contributions of the individual units to the overall risk
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40
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Proportional Capital Allocation

A

Proportional Capital Allocation

  • Allocates total risk down to business units

Steps:

  1. Calculate overall risk measure
  2. Calculate the risk measure for each individual business unit
  3. Allocate the overall risk measure to the individual business units in proportion to their individual risk measures
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41
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Allocating Captial - Risk Decomposition

A

Risk Decomposition

  • Decomposition uses co-measures to calculate the contribution of each business unit to the overall risk measure.
  • e.g. Co-TVaR
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42
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Allocating Captial - Marignal Allocation

A

Marginal Allocation

  • Measures the change to the total risk measure for the company if there is a small change in a business unit’s volume
  • The change to the total risk measure is assigned to the business unit.

Marginal Impact of the jth business component:

r(Xj) = lim as ε→0 {[⍴(Y+εXj) - ⍴(Y)] / ε}

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43
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Allocating Captial - Marignal Allocation

Advantages

A

Advantages

  • Marginal allocation also produces co-measures
  • Marginal attributions sum to the total risk measure
  • Leads to consistent strategic implications
    • e.g. growing a business with an above average profit-to-risk ratio increases the company’s profit-to-risk ratio
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44
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Risk-Adjusted Profitability

A

If the risk measure is proportional to the market value of risk, then a higher risk-adjusted profitability ratio means a business unit is more profitable relative to its risk.

Risk-Adjusted Profitability Ratio = Profit / Risk Measure

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45
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Allocating the Cost of Capital

A

Allocating the cost of capital to business units sets a minimum profit target to each unit.

  • If a business unit’s profit exceeds the minimum profit target, this excess is added to the firm

The cost of capital reflects the risk that a business unit has the right to access the insurer’s entire capital.

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46
Q

Brehm Chapter 2: Risk Measures & Capital Allocation

Disadvantages of Allocating Capital Compared to Allocating the Cost of Capital

A

Disadvantages of Allocating Capital

  • it’s arbitrary because different risk measures allocate capital differently
  • it’s artificial because each business unit has access to the entire company’s capital
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47
Q

Brehm Chapter 2: Regulatory and Rating Agency Capital Adequacy Models

Practical Models for Setting Capital:

Leverage Ratios

A
  • Leverage ratios are generally a per dollar of surplus measure such as premium to surplus or reserve to surplus
  • Can be used to compare to a threshold for testing capital adequacy (12 IRIS ratios)

Advantages

  • Easy to calculate and monitor

Disadvantages

  • Doesn’t distinguish between lines of business
  • Ignores risks other than underwriting risks
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48
Q

Brehm Chapter 2: Regulatory and Rating Agency Capital Adequacy Models

Practical Models for Setting Capital:

Risk-Based Capital Models

A

Risk-based capital models combine multiple aspects into a single number.

Included Risk Aspects:

  • Invested Asset Risk
  • Credit Risk
  • Premium Risk
  • Reserve Risk
  • Accumulation/CAT Risk (not in US or S&P models)
  • Covariance Adjustment (in some models US and AM Best)
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49
Q

Brehm Chapter 2: Regulatory and Rating Agency Capital Adequacy Models

Practical Models for Setting Capital:

Risk-Based Capital Models - Reasons for Significant Differences between RBC Models

A

Different Model Uses

  • Rating agency models (AM Best, Moody’s, S&P) focus on long-term viability of the firm
  • Regulatory models (MCT, US RBC, etc.) focus on 1-year solvency so these models will have relatively lower factors

Covariance Adjustment

  • Models with a covariance adjustment (like AM Best) will have relatively higher factors than models without it (like S&P)
    • the covariance adjustment will reflect that not all risks will occur at the same time: reflects the independence between risks so that that overall capital is less than the sum of the individual risks
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50
Q

Brehm Chapter 2: Regulatory and Rating Agency Capital Adequacy Models

Practical Models for Setting Capital:

Scenario Testing

A

An insurer may do its own risk assessment using scenario testing or stochastic modeling which would be reviewed by a regulator.

Requirements:

  1. A one to five year financial projection model
  2. Probability distributions for sources of uncertainty
  3. Correlations between risks
  4. Management responses to adverse financial results
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51
Q

Brehm Chapter 2:

Asset-Liability Management

A

Asset Liability Management - the analysis and management of the asset portfolio, reflecting current liabilities, future cash flows, future premium flows, and the existing asset and liability portfolios.

The GOAL of ALM is to help the insurer make better risk-return decisions.

*more extensive than just asset-liability matching

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52
Q

Brehm Chapter 2:

Asset-Liability Matching

A

Asset-Liability Matching - setting an investment portfolio to have the same duration as the duration of the liability portfolio to protect the firm from changing interest rates.

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53
Q

Brehm Chapter 2:

Additional Risks and Actions that ALM Considers

A

Beyond interest rate risk, ALM considers:

  • Inflation Risk
  • Credit Risk
  • Market Risk
  • Equities and Reinsurance as methods for hedging
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54
Q

Brehm Chapter 2:

Layers of Complexity in ALM

A

Each item below adds complexity - we start off with just the asset portfolio then start adding the liabilities, timing and then UW cashflows.

  • Analysis of the asset portfolio in isolation (risk vs. return)
  • Adding fixed liabilities into the analysis
    • reinvestment risk if asset duration is shorter
    • risk of selling depressed assets if asset duration is longer (interest rates high)
  • Adding variability to the amount/timing of liability cash flows
  • Adding variable underwriting cash flows

With the above real-world complexities, a true enterprise-risk model is needed.

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55
Q

Brehm Chapter 2:

How does a company’s choice of risk-return metrics impact the optimal investment strategy?

A

Statutory Accounting Metrics

Since bonds are amortized and liabilities are not discounted, this approach show little hedging from duration-matching.

GAAP Accounting Metrics

Bonds are valued at market value, but this also shows little hedging form duration-matching.

True Economic Metrics

Duration-matching lowers interest rate risk, but including cash flows complicates the analysis.

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56
Q

Brehm Chapter 2:

Asset-Liability Modeling Steps

A

Step 1: Model assets, existing liabilities and current business operations

Step 2: Define risk metrics, can be income based or balance sheet based

Step 3: Define return metrics - income or balance sheet based (should be consistent with step 2)

Step 4: Set the analysis time horizon - single year or multi-year

Step 5: Include Model Constraints - regulatory restraints

Step 6: Run the model - with different investment, underwriting and reinsurance strategies, calculating risk-return metrics

Step 7: Plot an efficient frontier based on the different portfolios

Step 8: Test the effects of different reinsurance structures

Step 9: Review simulations where portfolios performed poorly

  • hedging strategies or new policies may help to reduce downside risk
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57
Q

Brehm Chapter 2:

Naive Approach to Measuring Reinsurance Value

A

Comparing ceded premium (cost of reinsurance) to reinsurance recoveries and ceding commissions (benefit) over many years typically shows a negative benefit.

Reinsurance expect to make a profit, so simple cost-benefit analysis is a poor way to assess reinsurance value.

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58
Q

Brehm Chapter 2: Measuring Reinsurance Value

Paradigm 1

A

Reinsurance Provides Stability (Paradigm 1)

  • Protects surplus from adverse results
  • Improves predictability of earnings and growth
  • Improves customer confidence that they’ll recover insured losses
  • Ceded Premium - Recoveries*
  • is a better cost measure under this paradigm
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59
Q

Brehm Chapter 2: Measuring Reinsurance Value

Paradigm 2

A

Reinsurance is a Substitute for Risk Capital (Paradigm 2)

  • Increased stability lowers required risk capital
  • ROE Cost of Reinsurance = Reinsurance Cost / Capital Freed*

If the ROE cost of reinsurance is less than the company’s target return, getting reinsurance is a good deal (i.e. want a small ratio).

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60
Q

Brehm Chapter 2: Measuring Reinsurance Value

Paradigm 3

A

Reinsurance adds value (Paradigm 3)

  • Ideally, we could measure the value of reinsurance by the incremental increase in market value to the company.
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61
Q

Brehm Chapter 2:

Disadvantage of the Quantifying Stability Paradigm for Measuring Reinsurance Value

A

Disadvantage

Significant judgement is needed to evaluate the benefit of stability against the net cost of reinsurance for the different programs.

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62
Q

Brehm Chapter 2:

Reviewing Probability Distributions of Financial Measures for Reinsurance Options

A

Look for:

  • Which option protects against the worst losses
  • Options that don’t limit profitable scenarios too much
  • Compare which options produce better results in different areas of the probability distribution
63
Q

Brehm Chapter 2:

Box/Space Need View for Comparing Reinsurance Options

A
  • Shows the probability in different ranges

Compare programs based on:

  • which program protects from the most unfavourable scenarios
  • which sacrifices profitable good years
64
Q

Brehm Chapter 2:

Cost-Benefit Diagram for Reviewing Reinsurance Options

A

X-axis

Cost of reinsurance = ceded premium - expected recovery

Y-axis

Loss Amount = Net Premium - Net Loss

  • Plot multiple probability levels (e.g. 1-in-10, 1-in-100, 1-in-250)

Look for:

  • which option(s) perform best at the different probability levels
  • if a program is more costly but has worse loss outcomes at each probability level, its inefficient and shouldn’t be considered
65
Q

Brehm Chapter 2:

Efficient Frontier for Reviewing Reinsurance Options

A

Efficient Frontier

Graphs the risk-return of different reinsurance programs at various probability levels.

  • Programs that are below the efficient frontier are inefficient
66
Q

Brehm Chapter 2:

Two Classes of Models for Measuring the Change in Capital due to Reinsurance

A

Theoretical Models

Required capital is calculated with risk measures using an enterprise risk model

  • Advantage - required capital is more consistent with management’s view about risk

Practical Models

Required capital is calculated using rating agency or regulatory models or actual capital (e.g 200% of BCAR)

  • Advantage - easier to implement
  • Disadvantage - uses proxies for risk such as premium to measure capital instead of modeling risks directly
67
Q

Brehm Chapter 2:

Measuring the Marginal ROE Cost for Buying More Reinsurance

A
  • The cost of buying more reinsurance is NPV of Ceded Premium less Ceded Loss compared to the current program which should be negative
  • This should release capital which also is negative

Marginal ROE (cost) = Δ(Ceded Premium - Ceded Losses) / ΔCapital

If the marginal ROE (cost) is less than the cost of capital, then buying reinsurance is a good deal.

68
Q

Brehm Chapter 2:

Measuring the Marginal ROE Benefit for Buying Less Reinsurance

A
  • The benefit of buying less reinsurance is NPV (Ceded Premium less Ceded Loss) compared to the current program which should be positive
  • This should consume capital which also is positive

Marginal ROE (benefit) = Δ(Ceded Premium - Ceded Losses) / ΔCapital

If the marginal ROE (benefit) is more than the cost of capital, then buying less reinsurance is a good deal.

69
Q

Brehm Chapter 2:

Why accumulated risk must be considered when evaluating the value of reinsurance for long-tail lines.

A
  • Marginal ROEs for measuring reinsurance value are based on capital for a single year.
  • Long-tail lines have loss reserves and accumulated risk that must be supported by capital for multiple years.
  • Can use as-if loss reserves to account for multi-year
70
Q

Brehm Chapter 2:

What are “as-if” Loss Reserves?

A

As-if Loss Reserves

For an accident year of new business, the as-if loss reserves are the reserves that would exist at the beginning of the accident year if that business had been written in a steady state in all prior years.

We can approximate the present value of required capital for an accident year over time by modelling the capital needed for both:

  • the current year for the current accident year
  • the as-if loss reserves (represents the accumulation of reserves from prior years)
71
Q

Brehm Chapter 2:

Advantages of using “as-if” Loss Reserves

A

Advantages

  1. This approach can measure the impact that correlated risk factors have on the accumulated risk
  2. It can fully measure the impact of reinsurance by applying reinsurance to the accident year and as-if reserves
72
Q

Brehm Chapter 3:

Considerations when Implementing an ERM:

Staffing & Scope

A

Staffing & Scope

  • Organization Chart - modeling team reporting line, solid line vs. dotted line reporting
  • Functions Represented - reserving, pricing, finance, planning, UW
  • Resource Commitment - mix of skill set, full-time or part-time
  • Critical Roles & Responsibilities - control of input parameters, control of output data, analyses and uses of output
  • Purpose - is the goal of the model to quantify variation around the plan?
  • Scope - prospective UW year only, or including reserves, asset, operational risks?
73
Q

Brehm Chapter 3:

Considerations when Implementing an ERM:

Staffing & Scope Recommendations

A

Staffing & Scope Recommendations

  • Reporting Relationships - team leader should have reputation of fairness and balance
  • Resource Commitment - the team should have full-time commitment to the implementation
  • Inputs & Outputs - should be controlled similarly to the general ledger
  • Initial Scope - prospective underwriting period, variation around plan
74
Q

Brehm Chapter 3:

Considerations when Implementing an ERM:

IRM Parameter Development

A

IRM Parameter Development

  • Modeling Software - what are the capabilities, scalability, learning curve, integration with current systems
  • Developing Input Parameters - data driven, requires expert opinion, involve functional areas of the firm
  • Correlations - line of business representatives cannot set cross-line parameters, need to have corporate-level ownership of these parameters
  • Validation & Testing - as this is new, there is no existing IRM to compare
75
Q

Brehm Chapter 3:

Considerations when Implementing an ERM:

IRM Parameter Development Recommendations

A

IRM Parameter Development Recommendations

  • Modeling Software - compare existing vendor software with user-built options, ensure final software choice align with capabilities of the IRM team
  • Developing Input Parameters - include product expertise from UW, claims, planning and actuarial; develop systematic way to capture expert opinion
  • Correlations - modeling team recommends correlation assumptions which are ultimately owned at the corporate level (CRO/CEO/CUO)
  • Validation & Testing - validate and test over extended period, provide basic education on probabilities and statistics
76
Q

Brehm Chapter 3:

Considerations when Implementing an ERM:

IRM Implementation

A

IRM Implementation

The following details need to be addressed:

  • Priority Setting - priority and timeline must be driven from the top executives
  • Interest and Impact - implement communication and education plan across the enterprise
  • Pilot Test
  • Education Process - run parallel with pilot test to bring leadership to the same point of understanding regarding probability and statistics
77
Q

Brehm Chapter 3:

Considerations when Implementing an ERM:

IRM Implementation Recommendations

A

IRM Implementation Recommendations

  • Priority Setting - top management should set priority for implementation
  • Interest and Impact - plan for regular communication to broad audiences
  • Pilot Test - do pilot testing to prepare stakeholders for the magnitude of the change as a result of using the IRM
  • Education Process - target training to bring leadership to similar BASE level of understanding
78
Q

Brehm Chapter 3:

Considerations when Implementing an ERM:

Integration and Maintenance

A

Integration and Maintenance

  • Cycle - integrate model runs into corporate calendar and ensure that IRM output supports major company decisions
  • Updating - determine frequency and magnitude of updates
  • Controls - ensure there is centralized storage and control of inputs and outputs, control of analytical templates
79
Q

Brehm Chapter 3:

Considerations when Implementing an ERM:

Integration and Maintenance Recommendations

A

Integration and Maintenance Recommendations

  • Cycle - integrate into the corporate calendar at minimum
  • Updating - major updates to inputs no more than semi-annually
  • Controls - maintain centralized control of inputs, outputs and templates
80
Q

Brehm Chapter 3: Modeling Parameter Uncertainty

Impact of Parameter Risk on Small vs. Large Companies

A

Small Company

A small insurer already has significant uncertainty so the added impact of parameter risk isn’t too large.

Large Company

Without parameter risk, the loss ratio modeled for a large insurer is unrealistically stable. Parameter risk isn’t diversified away with more insureds, so it significantly increases uncertainty for a large insurer.

81
Q

Brehm Chapter 3: Modeling Parameter Uncertainty

Three Aspects of Parameter Risk

A

The risk of assuming incorrect distributions or parameters for those distributions manifests itself in the following ways:

  1. Estimation Risk - arises from using only a sample of the universe of possible claims to estimate the parameters
    • risk that parameter estimates used are not the “true” parameter estimates for the underlying process
  2. Projection Risk - arises from projecting past trends into the future
  3. Model Risk - arises from having the wrong models to begin with
82
Q

Brehm Chapter 3: Modeling Parameter Uncertainty

Projection Risk Models - Simple Trend Model

A

A trend line is fit to historical loss costs to project future severities.

Weaknesses

  • Loss cost data is based on historical claims that haven’t settled which adds uncertainty
    • adds uncertainty to frequency and severity
  • Assumes a single constant trend for historical data that will continue into the future (same factor applied)
83
Q

Brehm Chapter 3: Modeling Parameter Uncertainty

Projection Risk Models - Severity Trend and Inflation Model

A

Models the trend as the sum of the general inflation and “superimposed inflation” (social inflation)

Advantage

  • An ERM model can reflect the dependency between trend and inflation
84
Q

Brehm Chapter 3: Modeling Parameter Uncertainty

Projection Risk Models - Time Series Model

A

This accounts for the trend changing over time and not being constant

Advantage

  • Reflects more uncertainty than a simple trend model
  • Models trend as a time series as opposed to assuming a single, constant trend

Disadvantage

  • A substantial number of data points is needed
  • If the data is limited then the model will understate the uncertainty
85
Q

Brehm Chapter 3: Modeling Parameter Uncertainty

Estimation Risk

How to include parameter estimation risk in an Enterprise Risk Model

A

To assess estimation risk, we use the covariance matrix that results from the standard MLE procedure, but the parameters are assumed to follow a joint log-normal.

  • *Process:**
    1. Parameter estimates are calculated using MLE
  1. Fit a joint lognormal distribution to the covariance matrix from the MLE
  2. In the ERM model, each simulation draws a random sample from the joint lognormal distribution to be the parameters for the loss distribution

This approach adds variability to the parameters used in the model.

Note: Lognormal preferred over normal as it works for large and small datasets.

86
Q

Brehm Chapter 3: Modeling Parameter Uncertainty

Model Risk

How to include Model Risk in an Enterprise Risk Model

A

Recommendations to help guide model selection

  1. Assign probabilities of being “correct” to each of the better-fitting distributions.
  2. For each simulation:
    i. Select a distribution form the set of distributions
    ii. Select the parameters from the lognormal distribution of parameters (to add estimation risk)
    iii. Use this fixed distribution for all losses in the simulated scenario.
87
Q

Brehm Chapter 3: Modeling Dependency

Why incorporating dependency is an Enterprise Risk Model is important

A

Each individual model may be realistic, but if dependencies between different lines and risks are unrealistic, then the ERM model in aggregate will be unrealistic.

88
Q

Brehm Chapter 3: Modeling Dependency

The difference between correlation and dependency and why dependencies may be better modeled with a copula

A

Correlation uses a single value (the correlation coefficient) and doesn’t differentiate between different levels of dependency in different parts of the distribution.

Dependency between different risks and lines may be higher in the tail than the rest of the distribution.

  • This is better modeled with a copula
89
Q

Brehm Chapter 3: Modeling Dependency

How do copulas reflect dependency?

A

Copulas are a way to aggregate different distributions into a combined multivariate distribution.

Different copulas reflect different levels of dependency at different parts of the distribution.

90
Q

Brehm Chapter 3: Modeling Dependency

Frank Copula

A
  • Light-tailed copula - produces wark correlation in the tails
  • R(1) = 0
91
Q

Brehm Chapter 3: Modeling Dependency

Gumbel Copula

A
  • More tail concentration that Frank’s copula
  • Asymmetric with more weight in the right tail
  • R(1) > 0
92
Q

Brehm Chapter 3: Modeling Dependency

Heavy Right Tail (HRT) Copula and Joint Burr

A
  • HRT copula produces less correlation in the left tail and more correlation in the right tail
  • R(1) > 0
93
Q

Brehm Chapter 3: Modeling Dependency

Normal Copula

A

Advantages

  • Easy simulation method
  • Generalized to multi-dimensions (> than 2 dimensions)
  • Heavier tail than Frank copula but lighter tail than Gumbel or HRT
  • R(1) = 0
94
Q

Brehm Chapter 3: Modeling Dependency

t-Copula

A
  • Multivariate copula which can be used for more than two variables
  • Has an additional parameter for the heaviness of the tail
95
Q

Brehm Chapter 3: Modeling Dependency

Describing Copulas: Left and Right Tail Concentration Functions

A

R(z) = Pr(U>z|V>z)

L(z) = Pr(U<z|V<z)

The right tail concentration function, R(z), shows the probability that losses from one distribution will be large given that losses from another distribution are large.

96
Q

Brehm Chapter 3: Modeling Dependency

Describing Copulas: Upper Tail Dependence Coefficient

A

Upper Tail Dependence Coefficient

The limit of R(z) as z→1

If this limit is greater than zero, then there is high tail dependency.

There still can be significant dependence near the limits so it’s useful to look at R(z) when z is near 1.

97
Q

Brehm Chapter 3: Modeling Dependency

Describing Copulas: Joint Distributions

A

The joint probability distribution of two distributions combined with a copula shows areas with high joint probability densities.

This can be used to compare how different copulas behave; especially in the tail.

98
Q

Brehm Chapter 3: Modeling Dependency

Describing Copulas: Fitting Copulas to Data

A

Left and Right-Tail Concentration Functions

Look near the limit of R(z) as z→1

J Function

Compare the fitted J to the empirical J function to evaluate which copula fits the data best.

Chi Function

Compare the fitted Chi function to the empirical Chi function.

99
Q

Brehm Chapter 4: Operational Risk

A

Operational Risk

The risk of loss resulting from inadequate or failed internal processes, people and systems or from external events.

  • Includes legal risk
  • Excludes strategic and reputational risks
100
Q

Brehm Chapter 4:

List 7 Types of Operational Risk

A
  1. Internal Fraud
  2. External Fraud
  3. Employment practices and workplace safety
  4. Clients, Products and Business practices
  5. Damage to Physical Assets
  6. Business Disruption and System Failures
  7. Execution, Delivery and Process Management
101
Q

Brehm Chapter 4: Types of Operational Risk

Internal Fraud

A

Internal Fraud

Acts by internal party that defraud, misappropriate property (i.e. unfairly take) or circumvent the regulations (either the law or company policy).

  • employee theft
  • insider trading
  • claim falsification
102
Q

Brehm Chapter 4: Types of Operational Risk

External Fraud

A

External Fraud

Acts by a third-party that defraud, misappropriate property or circumvent the law.

  • Robbery, forgery, and computer hacking
  • Claims fraud and falsifying application information
103
Q

Brehm Chapter 4: Types of Operational Risk

Employment Practices & Workplace Safety

A

Employment Practices & Workplace Safety

Acts that are inconsistent with employment, health and safety laws

  • WC claims,
  • Violation of employee health and safety rules
  • Discrimination claims
  • Organized labor activities
  • General Liaiblity (e.g. customer slip and fall at branch office)
  • Repetitive Stress (insurer specific)
104
Q

Brehm Chapter 4: Types of Operational Risk

Clients, Products and Business Practices

A

Clients, Products and Business Practices

Unintentional or negligent failure to meet a professional obligation to specific clients or the nature/design of product.

Insurer Examples

  • Client Privacy
  • Bad Faith Claims
  • Red-lining

General Examples

  • Fiduciary breaches
  • Misuse of Confidential Customer Information
  • Money Laundering
  • Sale of Unauthorized Products
105
Q

Brehm Chapter 4: Types of Operational Risk

Damage to Physical Assets

A

Damage to Physical Assets

Loss or damage to physical assets from natural disasters or other evens such as terrorism/vandalism. This excludes policyholder losses which is an insurance hazard risk.

Insurer Examples

  • Damage to insurer’s office building and its own automobile fleets

General Examples

  • Terrorism
  • Vandalism
  • Earthquakes
  • Floods
106
Q

Brehm Chapter 4: Types of Operational Risk

Business Disruption & System Failures

A

Business Disruption & System Failures

Disruption of business or system failures

Insurer Examples

  • Processing centre downtime
  • System interruptions

General Examples

  • Hardware and software failures
  • Telecommunication problems
  • Utility outages
107
Q

Brehm Chapter 4: Types of Operational Risk

Execution, Delivery & Process Management

A

Execution, Delivery & Process Management

Failed transaction processing or process management, and relationships with vendors

Insurance Examples

  • Policy Processing Errors
  • Claim Payment Errors

General Examples

  • Data Entry Errors
  • Incomplete Legal Documentation
  • Unapproved Access given to Claimant Accounts
  • Vendor Disputes
108
Q

Brehm Chapter 4: What are the primary causes of P&C company failures?

A
  • Deficient Loss Reserves (1st)
    • root reason for insurer failure is the accumulation of too much exposure for the supporting asset base
  • Rapid Growth (2nd)
  • Underpricing
  • Alleged Fraud
  • Overstated Assets
  • Catastrophes
  • Reinsurance Failure
  • Reckless Management
109
Q

Brehm Chapter 4: Why should operational risk be unbundled from underwriting risk?

A

Operational risk should be unbundled from UW risk and analyzed separately as these can be significant sources of risk.

Supported by capital risk charges applied to premium, reserves and growth (operational risk is implicitly included in the capital charges)

110
Q

Brehm Chapter 4:

What do the authors say is the “fulcrum” of operational risk?

A

The plan loss ratio determination process is considered the fulcrum of operational risk.

  • Deficient carried reserves are indicators of deficient initial reserving, which is driven by optimistic plan ratios, OR premature reserve releases
111
Q

Brehm Chapter 4: Plan loss ratios are set using a “bridging model”. What is a bridging model and what are the issues with it?

A

Bridging Model

  • Ultimate losses from mature years are trended forward (using loss cost trend and price level changes) to set the new plan loss ratio
  • Ultimate loss ratios of immature prior years are calculated with the BF method using ELRs set to the initial plan loss ratio for each prior year.

Problem with Bridging Model

  • Prior year ultimate loss ratios become highly correlated.
    • If the older prior year loss ratios were too optimistic, the plan loss ratios would be set too low. This causes many of the prior year ultimate loss ratios to be too low.
    • Eventually, older years deteriorate, causing many AY’s to deteriorate
      • Reserve conflagration which is represents the pure operational risk
112
Q

Brehm Chapter 4:

Possible explanations if an insurer failed due to reserve deterioration from a bridging model

A
  • The model could not have accurately forecasted the loss ratio (or reserves)
    • Operational Risk → Process and System failure
    • Not Operational Risk → inherent uncertainty and all competitors were wrong with their estimates as well
  • The model could have accurately forecasted the loss ratios (or reserves) but was improperly used.
    • Operational Risk → People failure
  • The model did accurately forecast the loss ratio, but the indications were unpopular and ignored
    • Operational Risk → Process and governance failure
113
Q

Brehm Chapter 4:

Operational Risk and Underwriting Cycle Management

A

Underwriting Cycle Management

The management of UW capacity as market pricing fluctuates due to the underwriting cycle.

Assess different cycle management strategies based on:

  • stability
  • availability
  • reliability
  • affordability
114
Q

Brehm Chapter 4:

Naive Underwriting Cycle Management and its Possible Results

A

Naive Cycle Management

Writing business at inadequate prices during a soft market in order to maintain market share.

Possible Results:

  • Insurer may be downgraded, causing policyholders to switch insurers
    • Failure of stability/availability
  • Insurer may go insolvent with partial recoveries on claims for policyholders
    • Failure of reliability/affordability
115
Q

Brehm Chapter 4:

Effective Underwriting Cycle Management and its Possible Results

A

Effective Cycle Management

Disciplined UW:

  1. Soft Market - decrease written premium (don’t write inadequately priced business)
  2. Hard Market - increase written premium volume

To implement, must overhaul UW decisions and processes simultaneously which can be difficult.

The following area are affected:

  • Planning
  • Underwriting
  • Objective Setting
  • Incentive Bonuses
116
Q

Brehm Chapter 4:

How can an insurer implement effective UW cycle management?

A

An insurer can implement by focusing on 3 key areas:

  1. Intellectual Property
  2. Underwriter Incentives
  3. Market Overreaction
  4. Owner Education
117
Q

Brehm Chapter 4: Implementation Effective UW Cycle Management

Intellectual Property

A

Intellectual Property

  • Retain and continue to develop top talent through soft markets
  • Maintain a presence in the core marketing channels
  • Maintain consistent investment in systems, models and databases

Note: Intellectual property is an intangible asset and can drive the company’s franchise value.

118
Q

Brehm Chapter 4: Implementation Effective UW Cycle Management

Underwriter Incentives

A

Underwriter Incentives

  • Incentive plans should be flexible so underwriting decisions are aligned with corporate objectives which change with market conditions
  • E.g. if prices drop too low, UWs may need to stop writing new business, but their bonuses and employment should be at risk
119
Q

Brehm Chapter 4: Implementation Effective UW Cycle Management

Market Overreaction

A

Market Overreaction

  • When prices fall too low in a soft market, the insurer should maintain underwriting discipline and write less. (i.e. not over-react)
  • When the market hardens and prices overcorrect, the insurer will have the capacity to write significantly more business profitably.
120
Q

Brehm Chapter 4: Implementation Effective UW Cycle Management

Owner Education

A

Owner Education

  • Some financial figures may diverge from those of peer companies such as:
    • Premium Volume - will drop during soft markets
    • Overhead Expense Ratio - maintaining intellectual property during a premium decline will cause the expense ratio to rise
  • Practicing effective UW cycle management means owners should not over-react when their ratios diverge from their competitors
121
Q

Brehm Chapter 4:

What are the goals of Agency Theory?

A

Agency theory is important because there are potential operational risks when management and owner interests are misaligned.

Goals of Agency Theory

  • How to align management and owner interests
  • Understanding the impacts when management and owner interests are different
122
Q

Brehm Chapter 4:

Risks due to Different Management Incentive Compensation Plans

A

Incentive - Percentage of the increase in market cap after n years

Result - Management may take too much risk to increase firm value because

  • High upside benefit - big payoff if there’s a major increase in firm value
  • Low downside risk - don’t lose anything as they are gambling with someone else’s money

Incentive - Stock grants or stock options

Result - Management may become too risk averse because:

  • Shareholders may have diversified portfolios but management does not since their compensation is tied directly to the firm
  • Management has a large portion of their wealth tied to the company so they will be less willing to take on risk
123
Q

Brehm Chapter 4:

Provide examples of how to manage different types of operational risks.

A
  1. Pension Funding Issues - quantify this risk with models that include financial risk and firm demographics
  2. IT Failure Risk - monitor and control with contingency planning. The remaining risk could be quantified and funded.
  3. Other HR Risks - identification and control of these risks is most important (more important than quantifying them)
    • e.g. loss of important staff, employee fraud, etc.
  4. Reputational Risks - identification and control of these risks is most important (more important than quantifying them)
    • product tampering, bad press coverage, etc.
  5. Lawsuits - monitoring of this risk is most important
124
Q

Brehm Chapter 4:

Control Self Assessment

A

Control Self Assessment

A process to examine and assess the effectiveness of internal controls with a goal of providing reasonable assurance that all business objectives will be met.

125
Q

Brehm Chapter 4:

Control Self Assessment - Primary Objectives of Internal Controls

A

Primary Objectives of Internal Controls are to ensure:

  • Reliability and integrity of information
  • Compliance with policies, plans, procedures, laws, regulations and contracts
  • Safeguarding of assets
  • Economical and efficient use of resources
  • Accomplishment of established objectives and goals for operations or programs
126
Q

Brehm Chapter 4: What are Key Risk Indicators (KRIs)?

A

Key Risk Indicators

Measures used to monitor the activity and status of controls in a business area for a given operational risk category.

KRIs are leading indicators of risk and can be measured frequently to flag attention if a threshold is hit.

  • KRIs are forward-looking indicators of risk unlike historical losses which are backward looking
127
Q

Brehm Chapter 4:

Examples of KRI’s

A

Examples of Insurer Key Risk Indicators:

  • Production - hit ratios, retention ratios, pricing levels, rate per unit of exposure
  • Internal Controls - audit results, audit frequency
  • Staffing - employee turnover, training budget, premium per employee, policies per employee
  • Claims - frequency, severity, new classes of loss
128
Q

Brehm Chapter 4:

Six Sigma

A

Six Sigma - its a management framework; based on customer-specified tolerances for product defects are +/- 3σ from the mean

  • in insurance, can help firms identify and eliminate process issues such as inefficiencies, errors, overlaps and gaps in communication

Examples of insurer processes to improve with six sigma:

  • Underwriting - exposure data verification/capture, classification/hazard selection
  • Claims - coverage verification, ALAE, use of outside counsel to set initial case reserves
  • Reinsurance - treaty claim reporting, coverage verification, reinsurance recoverables, disputes, LOC and collaterization
129
Q

Brehm Chapter 4:

Operational Risk Modeling - Steps Needed for Operational Risk Portfolio Management

A

Operational Risk Modeling Steps:

  1. Identify exposure base - payroll, head count, policy count, premium volume
  2. Measure exposure level
  3. Estimate the loss potential (frequency and severity) per unit of exposure
  4. Create loss frequency and severity distributions by combining step 2 and step 3 above
  5. Estimate the impact of risk mitigation, process improvement or risk transfer on the frequency and
130
Q

Brehm Chapter 4:

Strategic Risk

A

Strategic Risk - is the risk to the company form making the right or wrong strategic decisions, not making a decision or not recognizing that a strategic decision needs to be made.

It’s the risk of choosing the wrong plan given market conditions.

131
Q

Brehm Chapter 4:

List the Types of Strategic Risk

A

Types of Strategic Risks:

  1. Industry
  2. Technology
  3. Brand
  4. Competitor
  5. Customer
  6. Project
  7. Stagnation
132
Q

Brehm Chapter 4: Types of Strategic Risk

Industry

A

Industry - capital intensiveness, overcapacity, commoditization, deregulation, cycle volatility

Insurer Risk - very high

Examples - UW cycle, insurance as a commodity

133
Q

Brehm Chapter 4: Types of Strategic Risk

Technology

A

Technology - technology shift, patents, obsolescence

Insurer Risk - low

Examples - data management, innovation in distribution over the internet

134
Q

Brehm Chapter 4: Types of Strategic Risk

Brand

A

Brand - erosion or collapse

Insurer Risk - moderate

Examples - reputation loss through bad press or class action lawsuits

135
Q

Brehm Chapter 4: Types of Strategic Risk

Competitor

A

Competitor - global rivals, gainers, unique competitors

Insurer Risk - moderate

Examples - predatory pricing from competitors, entrance into new markets with inadequate expertise or systems or multiple competitors targeting the same market segment poses a risk as well

136
Q

Brehm Chapter 4: Types of Strategic Risk

Customer

A

Customer - priority shift, power, concentration

Insurer Risk - moderate

Examples - risk is worse for commercial insurance companies (can be concentrated in certain areas rather than spread over the country)

137
Q

Brehm Chapter 4: Types of Strategic Risk

Project

A

Project - failure of R&D, IT, business development or M&A

Insurer Risk - high

Examples - value-destroying M&A (don’t contemplate integration costs, timelines, reserve deficiencies, etc), under-investing in underwriting in R&D and IT

138
Q

Brehm Chapter 4: Types of Strategic Risk

Stagnation

A

Stagnation - flat or declining volume, price decline, weak pipeline

Insurer Risk - high

Examples:

  • response to changes in the UW cycle is poor,
  • difficulty in redeploying assets
    • insurer’s assets are largely intellectual assets (people) which have a large degree of specificity)
  • extensive reporting lags
  • mismatched revenue/expenses
139
Q

Brehm Chapter 4:

Steps for Scenario Planning to Manage Strategic Risk

(10 steps)

A

Steps:

  1. Define the scope of the analysis - time frame, level of analysis such as geographic, product segments
  2. Identify major stakeholders - customers, suppliers, competitors, employees, shareholders, regulators
  3. Identify basic trends - include their influence on the organization
  4. Identify key uncertainties
  5. Construct initial scenario themes - combine key elements to create scenarios
  6. Check consistency and plausibility - scenarios should be realistic and internally consistent
  7. Develop learning scenarios - identify themes that are strategically relevant (e.g. “soft market” scenario)
  8. Identify research needs - better understand the trends and uncertainties
  9. Develop quantitative models
  10. Evolve toward decision scenarios - scenarios used to test different strategies
140
Q

Brehm Chapter 4:

Advantages of Scenario Planning for Insurers

A

Advantages

  1. Company thinks through responses beforehand
    • Can select the best response to different market conditions
  2. Reduces organizational inertia
    • Provides more flexibility compared to trying to “make plan” even if the market conditions change
141
Q

Brehm Chapter 5:

Define Underwriting Cycle

A

Underwriting Cycle

The recurring pattern of increases and decreases in insurance prices and profits.

142
Q

Brehm Chapter 5:

List the Characteristic Stages of the Underwriting Cycle

A
  1. Emergence
  2. Control
  3. Breakdown
  4. Reorganization
143
Q

Brehm Chapter 5:

Characteristic Stages of the Underwriting Cycle

Emergence

A

Emergence - for a new line, there is a cycle between soft and hard markets:

  • Price wars between competitors

price corrections when weak competitors leave→period of profitability→new competition to restart the cycle

  • Competition drives pricing dynamics
144
Q

Brehm Chapter 5:

Characteristic Stages of the Underwriting Cycle

Control

A

Control

  • Stabilization as rating bureaus and state DOIs regulate prices
  • Statistical data lags drive pricing dynamics
145
Q

Brehm Chapter 5:

Characteristic Stages of the Underwriting Cycle

Breakdown

A

Breakdown

  • Controls breakdown due to technological and societal changes
  • Competition and data lags drive pricing dynamic
146
Q

Brehm Chapter 5:

Characteristic Stages of the Underwriting Cycle

Reorganization

A

Reorganization

  • A new version of the line of business or marketplace emerges, returning to the emergence phase
  • Competition driving pricing dynamics
147
Q

Brehm Chapter 5:

Theories of what Drives the Underwriting Cycle

A

Theories of what Drives the Underwriting Cycle

  1. Institutional Factors - the cycle time is driven by time lags involved in pricing. Time lags due to reporting and regulatory delays also contribute to the underwriting cycle.
  2. Competition - the cycle is driven by competition between insurers toward lower rates. Underwriting strategies fluctuate between growth and price maintenance.
  3. Supply and Demand, Capacity Constraints and Shocks - shocks to capital (catastrophes, etc.) reduce capacity causing price increases.
  4. Economic Linkages - insurance profitability and cost of capital are driven by macroeconomics.
148
Q

Brehm Chapter 5:

Potential Predictor Variables for Modeling the Underwriting Cycle

A

Potential Predictor Variables for Modeling the Underwriting Cycle

  1. Previous time period profitability
  2. Internal financial variables
    • reserves, investment income, catastrophe losses, capital flows
  3. Regulatory and rating variables
    • rating upgrade/downgrade
  4. Reinsurance sector financials
  5. Macroeconomic variables
    • inflation, unemployment, GNP
  6. Financial market variables
    • interest rates, equity returns
149
Q

Brehm Chapter 5

Modeling the Underwriting Cycle

Soft Approaches

A

Soft Approaches - focus on gathering data and doing competitor intelligence, which analysts look at to gauge the state of the UW cycle

The following techniques are used:

  • Scenarios
  • Delphi Method
  • Competitor Analysis
150
Q

Brehm Chapter 5

Modeling the Underwriting Cycle

Technical Modeling

A

Technical Modeling - the underwriting cycle is modeled as a time series such as an autoregressive model.

The model can then create forecasts for the future, which can be used in an ERM model for strategic planning.

151
Q

Brehm Chapter 5

Modeling the Underwriting Cycle

Economic Modeling (Behavioural Modeling)

A

Economic Modeling (Behavioural Modeling)

Balances soft approaches, which seek to understand the structure of the UW cycle, and technical modeling, which seeks to create a statistical model.

152
Q

Brehm Chapter 5

Components of an Econometric Model:

Supply & Demand Curves

A

Supply & Demand Curves

  • Shocks to capital (such as catastrophes) decrease supply which potentially decrease demand if quality decreases
  • Increased competition increases supply which potentially increases demand if insurance quality rises
  • Supply curve could be modeled with a minimum and maximum price
153
Q

Brehm Chapter 5

Components of an Econometric Model:

Capital Flows

A

Capital Flows

  • High expected profits result in capital inflows (new competitors, capital infusions)
  • Low profits result in capital outflows (firms exiting the market)
  • Normal dividends result in steady outflows of capital
154
Q

Brehm Chapter 5

Components of an Econometric Model:

Assembling the Model - Underwriting Cycle Relationships

A

Assembling the Econometric Model

  • Economic factors influence supply and demand curves
  • Capital flows impact supply and demand curves
  • Current level of prices and losses influence supply
  • Price and quantity of insurance is modelled at the intersection of supply and demand
  • Premiums and losses affect capital
  • Profitability impacts capital flows

CAS Exam 7 (7 decks)

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