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CA1 2020 > Chapter 18, 19 and 20: Modelling, Assumptions and Data > Flashcards

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

1
Q

The conditions of Popi

APPFIOSD

A

Accountability -The processor should be compliant
Processing limitation - fair and lawful processing
Purpose specification - PI only kept if there is a purpose for it
Further processing limitation - Further processing in line with initial purpose
Information quality - Accuracy and completeness holders responsibility
Openness - Documentation kept on all processing
Security - safe storage, processing only by aurthorised person
Data subject participation - Subject may request update or deletion of data

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2
Q

Data risks FHAG V

A 
Form of data inappropriate 
Historic data not relevant 
Accuracy of data is poor 
Grouping of data may be inefficient
Volume may lack (Especially to model extreme cases)
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3
Q

Uses of data PREMISES FAAr

A
  • Premium rating, product costing, determining contributions
  • Risk management
  • Experience stats
  • Marketing
  • Investment
  • Statutory returns
  • Experience analytics
  • Setting provisions
  • Financial control, management info
  • Admin
  • Accounting
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4
Q

Sources of data TRAINERS c

A 
Tables eg actuarial mortality tables
Reinsurers 
Abroad (data from overseas contracts)
Industry data
National statistics
Experience investigations on the existing contract
Regulatory reports and company accounts
Similar contracts
Current economic conditions
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5
Q

Causes of poor data quality RUS RAD

A

Recording or verification
Underwriting Policy / Rating factors
System design

Insufficient:

  • Relevance
  • Amount
  • Detail
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6
Q

Quality and quantity of data influenced by SEC

A

Size of business
Experience in line of business
Characteristics of the product (long tailed or short tailed)

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7
Q

Ways to Improve data quality TeCSReFS P

A 
Technological resources to collect relevant real-time data 
Checks on data collected 
Systems to integrate newly collected and historical data
Retain Record new data Robust 
Form improvement:  
-Proposal form  RECTUREQ
-Claim form 
Secure and sufficient storage 
Processing capacity
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8
Q

Ways to improve the claim and proposal form RECTURE Q

A 
Relevant + reliable info collected 
Ease of analysis 
Cross-checking with claims form when processing claims
Tick Boxes 
Underwriting info - LI
Rating Factors - GI 
Ease of recording into the system
Quantitative as far as possible
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9
Q

Assertions to assess in data TACA /

CATA OPI SpotS

A

That A or L exists or is held
Appropriate value of A and L
Complete data – no unrecorded A or L
Accounting period of events correlate

Opening + movements = closing
Policy data vs Accounting data
Investment data vs Accounting data
Spot Checks for unusual values
Summary statistics
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10
Q

Ways to check assertions: Checks to perform SCARFACE MVR
/
Ways to check assertions Reconcile, Consistency, DRaM

A

Spot checks Random check for weird values
Contribution(salary related)/benefit(in payment) consistent with accounts
Asset income 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
Movement data against appropriate records( accounting data)
Validation of dates
Reconciliation of premiums and benefit amounts and changes in them

Reconcile NBA
Number of member and policies - previous and movement data
Benefit amounts and premiums and possible changes - previous + movement
Assets value correctly displayed - market value, and admissible assets

Consistency SACIS
SA and Premium in – class of business and previous
Changes in variable benefits and contributions consistent between policy and accounts
Investment income implied vs. accounting data
Shareholding

DRaM
Deed audit
Random spot check
Movement vs accounting data

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11
Q

For past data consider how to deal with:

CRC CHEAT PRUDT

A

Changes in recording of data
Random fluctuations
Changes in experience over time - Ideas!

Changes in balance of homogenous groups (Business Mix) 
Heterogeneity in groups
Errors in Data
Abnormal fluctuations
Standard Tables:
  -Relevance and adjustments
  -National vs. Industry data 
Changes in:
Product design
Rates changed (Mortality, withdrawal, investment)
Underwriting practices 
Distribution channels 
Target market
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12
Q

Concerns with summarised data MiRC/ RelCanS

A

Miss significant differences between benefits
Reliability reduced
Cannot value client specific options or guarantees

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13
Q

Merits of industry wide data CA FLOQ HP

A

Comparison
Assess possible new business opportunities

Formats can differ
Less flexible
Out of date
Quality will vary  
Heterogenity 
Not for provisions
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14
Q

Heterogeneity in data industry-wide data caused by difference in
PReCReS SNaP

A 
Practices – Underwriting, i.e. 
Reinsurance
Coding of risk factors
Regulation 
Sales methods

Socio-economic
Nature of data sections
Policies sold

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15
Q

Different options when in need of a model:

NEC

A

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

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16
Q

Factors to consider when choosing a model:

C U FLIP

A 
Cost 
Usage frequency 
Flexibility 
Level of needed accuracy 
In house expertise available 
Purpose of the model
17
Q

Requirements of a good model- VIVA RAPER DAD

A

Valid – relevant to purpose and economic principles assumed
Implement in various cases
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 in business and economic principles and assets and liabilities
Easily communicate and appreciate workings and results
Reflects risk profile of business modelled

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

18
Q

Deterministic model development

SCCACCARD

A
  1. Specify purpose
  2. Collect, sort and modify data
  3. Choose the form of the model, identifying parameters and variables
  4. Ascribe the parameter values
  5. Construct the model based on the expected cash flows
  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
19
Q

Stochastic model development

SCCSACCRP

A
  1. Specify the purpose of the investigation
  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 based on the expected cash flows
  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
20
Q

Deterministic vs. Stochastic models differences CUBE PO

A 
Capital intensivity/Cost
Understabability
Build and run ease
Economic scenario tested
Parameters
Output
21
Q

Merits of Deterministic model LECE COS No

A

Less Capital intensive
Explainable - since no distributions are applied
Clarity on scenario tested
Easy and quick to design

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

22
Q

Merits of a stochastic model WAQA SLICAH:

A

Wider range of scenarios tested
Assess financial guarantees/assumptions tested
Quality result / Quantify risks better - Statistical analysis performed on the results
Allows for uncertainty and covariance between parameters

Spurious accuracy
Longer construction and run time
Interpretation and communication difficulty
Complex programming/ Costly
Additional capital intensity
Higher risk of model and parameter error due to complex nature

23
Q

Uses of a model PROF P:

A 
Pricing - setting premium of charging structure
Risk management 
Options and guarantees valuation
Set Financing strategies 
Individual Provisions valuation
24
Q

Using models for pricing consider BAD CoMPA DiSCo

A

Business strategy
Assess capital requirements
Discount rate used

Competitiveness
Model point used
Profit requirements
Assumptions of future experience

Distribution channel
Contract design
Size of market

25
Q

Factors affecting assumptions: DIE FULCEN

When setting assumptions it is important to consider:

A

Demographic factors
Investment strategy
Economic factors

Financial significance of assumptions
Use of model/ Use to which the assumptions would be put
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

26
Q

Factors that increase the risk of product design:

GLOC U

A 
Guarantees and options
Lack of historical data 
Overheads
Complexity
Untested market
27
Q

When considering accuracy and prudence of assumptions BIPACS O

A

Best estimate vs Including uncertainty(Prudence) (overstatement)
Implicit assumptions:
-consistency of population distribution
-closed or open to new business
Purpose of the valuation
Accuracy of assumptions vs. Accuracy of outcome
Correlation between assumptions
Significance of assumption error

Once off cashflows

28
Q

Assumptions made form historic data/ Current data

BIDS/ REIS FW

A

Benefit growth - past inflation
Investment returns
Demographic data
Salary levels and growth

Regulation and legislation 
Economic factors – central bank policy
Inflation - Index linked bonds interest rates
Scheme sponsor provides info on: 
Future salary increase 
Withdrawals
29
Q

Demographic assumptions:

Morty Will Never FallS

A 
Mortality/ Morbidity 
Withdrawal rates
New business volumes/mix
Future contributions
Salary/Promotional increases
30
Q

Examples of financially significant economic assumptions

5CIET - Also for monitoring!

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
31
Q

Contract Features Captured in a model point BAGTSAD

A 
Basis 
Age 
Gender 
Term of policy 
Sum Assured
Duration in force / Demographics
32
Q

Setting the risk discount rate when modelling RASS

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
33
Q

The risk discount rate (project appraisal) SyNC WACS

A 
Systematic risk considered
Nature of cash flows (real vs. nominal)
Current cost of raising capital
WACC (Explain fully)
Alternative investments considered
Compare riskiness – similar projects, other companies
Sensitivity testing
34
Q

Requirements on the data used RRASH VoC

A 
High volume 
Stable
Consistent - similar form, source and time
Homogeneous
Reliable
Automated
Regularly received
35
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 = (MVECOE)/(MVE + MVD) + (MVDCOD)/(MVE+MVD) *(1-T)

36
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

37
Q

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

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

38
Q

Data governance:
Definition
guidelines:
RCaSCM

ADACSIA SAS

A

A data governance policy is a documented set of guidelines for ensuring the proper management of an organisations data.

Guidelines:
The roles and responsibilities of individuals in the organisation with regards to the data

How data would be Captured, processed and analysed

Issues with respect to data security and privacy

Controls put in place to ensured that the required data standards are applied

How the adequacy of the controls will be monitored on an ongoing basis with respect to data usability , accessibility, integrity and security

Access to data controlled
Disclosure on the use of data
Accountability held of managers for data
Confidentiality agreements with service providers
Sharing of data controlled between departments, subs and industry
IT requirements
Anonymity in the data used

Structure of the data should be good
Auditing done of the data
Sensitive information protected

CA1 2020 (32 decks)

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