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CA1 Neels > Part 5: Chapter 18, 19 and 20: Modelling, Assumptions and Data > Flashcards

Part 5: Chapter 18, 19 and 20: Modelling, Assumptions and Data Flashcards

1
Q

Data risks

A

Structure of data inappropriate
Historic data not relevant
Accuracy of data is poor
Volume may lack (Especially to model extreme cases)

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2
Q

Uses of data

A

Pricing
Risk management
Marketing
Investment
Setting provisions
Admin
Accounting

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3
Q

Sources of data

A

Tables eg actuarial mortality tables
Reinsurers
Abroad (data from overseas contracts)
National statistics
Regulatory reports and company accounts
Similar contracts

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4
Q

Causes of poor data quality

A

Recording or verification errors
Poor underwriting
Poor system design

Insufficient:

  • Relevance
  • Amount
  • Detail
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5
Q

Ways to Improve data quality

A

Technological resources to collect real-time data
Data checks built in
Systems to integrate newly collected and historical data
Improve proposal/claim forms
Secure and sufficient storage

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6
Q

Ways to improve the claim and proposal form

A

Relevant + reliable info collected
Structure for easy analysis
Tick Boxes rather than free hand
Ease of recording into the system
Quantitative as far as possible

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7
Q

Assertion checks to perform on data

A

Spot checks for outliers/impossible values
Income, outgo consistent with accounts
Reconciliation of asset value between company and third party
Full deed audit on certain assets (like property)
Average sum assured and premium compared to previous investigation
Consistency of shareholdings (start and end of period)
Equate/reconcile member numbers and changes in them
Validation of dates
Reconciliation of premiums and benefit amounts and changes in them

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8
Q

For past data consider how to deal with

A

Changes in recording of data
Random fluctuations
Changes in balance of homogenous groups (Business Mix)
Errors in Data
Standard Tables:
-Relevance and adjustments
-National vs. Industry data

Changes over time in:
Experience
Product design
Rates changed (Mortality, withdrawal, investment)
Underwriting practices
Distribution channels
Target market

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9
Q

Merits of industry wide data

A

Comparison against own experience
Assess possible new business opportunities

Formats can differ
Less flexible
Out of date
Quality will vary
Heterogeneity in populations

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10
Q

Heterogeneity in industry-wide data is caused by difference in

A

Practices:
Underwriting
Reinsurance
Coding of risk factors
Regulation
Sales methods

Socio-economic
Policies sold

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11
Q

Different options when in need of a model

A

New model built from scratch (outsourced or in house)
Existing model modified
Commercially purchased model

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12
Q

Factors to consider when choosing a model
C U FLIP

A

Cost
Usage frequency
Flexibility
Level of accuracy needed
In house expertise available
Purpose of the model

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13
Q

Requirements of a good model
VIVA RAPER DAD

A

Valid – relevant to purpose and economic principles assumed
Implementation possible in different ways
Verify reasonableness of outputs independently
Appropriate inputs and outputs - data and parameters

Rigorous – Realistic under several circumstances
Avoid over complexity - costly, non-flexible
Parameters - allow for significant features
Easily communicated
Reflects risk profile of business modelled

Documented – Key assumptions stated
Adjustable, Refine-able and Testable
Dynamic – Interaction between parameters and relevant variables that affect cash flows

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14
Q

Deterministic model development

A
  1. Specify purpose
  2. Collect, group and modify data
  3. Choose the form of the model, identifying parameters and variables
  4. Ascribe the parameter values
  5. Construct the model
  6. Check the goodness of fit is acceptable
  7. Attempt to fit a different model if the model does not fit well
  8. Run model using estimates of the values of future variables
  9. Do sensitivity testing on the parameter values
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15
Q

Stochastic model development

A
  1. Specify purpose
  2. Collect group and modify data
  3. Choose a sensible density function for each of the variables to be modeled
  4. Specify correlation between variables
  5. Ascribe values to the variables not being modeled
  6. Construct a model
  7. Check the goodness of fit
  8. Run the model many times each time using a different sample
  9. Produce a summary of the results that shows the distribution of modeled results
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16
Q

Merits of Deterministic model

A

Cost effective
Explainable - since no distributions are applied
Clarity on scenario tested
Easy and quick to design

Carefully consider which scenarios will be tested
Some scenarios may be missed
Only point estimates produced
Not good for valuing options and guarantees

17
Q

Merits of a stochastic model

A

Wide range of scenarios tested
Assess financial guarantees/assumptions tested
Allows for uncertainty and covariance between parameters

Spurious accuracy
Longer construction and run time
Interpretation and communication difficulty
Complex programming/training
Cost
Higher risk of model and parameter error due to complex nature

18
Q

Uses of a model

A

Pricing
Risk management
Options and guarantees valuation
Set Financing strategies
Provisions valuation

19
Q

Using models for pricing consider

A

Business strategy
Capital requirements
Discount rate used
Competitiveness
Model parameters used
Profit requirements
Assumptions of future experience
Distribution channel
Contract design
Size of market

20
Q

Factors affecting assumptions

A

Demographic factors
Investment strategy
Economic factors
Financial significance of assumptions
How model will be used
Legislation and regulation / Level of prudence
Consistency of assumptions / Correlation between assumptions
Expert guidance should be allowed for
Needs of client and company in terms or risk appetite

21
Q

Examples of financially significant assumptions

A

Claim amounts
Claim numbers
Investment returns
Expenses such as legal fees in court claims
Commission
Claims inflation
Claims expense inflation
Tail length of business

22
Q

Setting the risk discount rate when modelling consider:

A 
Required return of the company 
Average risk faced by business put into a single rate (simplicity) 
Statistical risk (Risk causing variation in cashflows and other model risks) 
Stochastic Discount rate
23
Q

Requirements of data used in models

A

High volume 
Stable
Consistent - similar form, source and time
Homogeneous
Automated
Regularly received

24
Q

WACC Formula

A

(Market value of equity* Cost of equity)/(Market value of equity Plus market Value of Debt) + (market value of debt* Cost of debt)/(Market value of equity +Market value of debt)(1- Company tax rate)

WACC = (MVE*COE)/(MVE + MVD) + (MVD*COD)/(MVE+MVD) *(1-T)

25
Q

How to determine the Cost of equity (CAPM model)

A

COE = Risk free rate of return (Yield on G bond with similar duration) + Beta(sensitivity of equity to the market return) *(Market rate of return - Risk free rate of return)

COE = RF + B * ( RE - RF)

Usually around 10% for a listed company
Around 20% for a private company

26
Q

Causes of differences between past experience from future events/company to company/products/before and after changes to a contract design

A

Contract design differences – the benefits provided (so many ideas!)
Underwriting – discuss the different rating factors to consider
Business mix (the demographic/rating factor differences – ideas!)
Externals – regulation, tax
Reinsurance agreements
Distribution channels
Investment strategy
Claims controls and experience
Rate changes

27
Q

Factors to consider when evaluating a source of data

A

Credibility (i.e. volume)

Completeness

Relevance

CA1 Neels (11 decks)

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