Chapter 18, 19 and 20: Modelling, Assumptions and Data Flashcards by jp swanepoel

The conditions of Popi

APPFIOSD

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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Data risks FHAG V

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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Uses of data PREMISES FAAr

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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Sources of data TRAINERS c

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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Causes of poor data quality RUS RAD

Recording or verification
Underwriting Policy / Rating factors
System design

Insufficient:

Relevance
Amount
Detail

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Quality and quantity of data influenced by SEC

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

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Ways to Improve data quality TeCSReFS P

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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Ways to improve the claim and proposal form RECTURE Q

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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Assertions to assess in data TACA /

CATA OPI SpotS

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

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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For past data consider how to deal with:

CRC CHEAT PRUDT

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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Concerns with summarised data MiRC/ RelCanS

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

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Merits of industry wide data CA FLOQ HP

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

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

Socio-economic
Nature of data sections
Policies sold

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Different options when in need of a model:

NEC

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

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Factors to consider when choosing a model:

C U FLIP

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

Requirements of a good model- VIVA RAPER DAD

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

Deterministic model development

SCCACCARD

Specify purpose
Collect, sort and modify data
Choose the form of the model, identifying parameters and variables
Ascribe the parameter values
Construct the model based on the expected cash flows
Check the goodness of fit is acceptable
Attempt to fit a different model if the model does not fit well
Run model using estimates of the values of future variables
Do sensitivity testing on the parameter values

Stochastic model development

SCCSACCRP

Specify the purpose of the investigation
Collect group and modify data
Choose a sensible density function for each of the variables to be modeled
Specify correlation between variables
Ascribe values to the variables not being modeled
Construct a model based on the expected cash flows
Check the goodness of fit
Run the model many times each time using a different sample
Produce a summary of the results that shows the distribution of modeled results

Deterministic vs. Stochastic models differences CUBE PO

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

Merits of Deterministic model LECE COS No

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

Merits of a stochastic model WAQA SLICAH:

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

Uses of a model PROF P:

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

Using models for pricing consider BAD CoMPA DiSCo

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

Factors affecting assumptions: DIE FULCEN | When setting assumptions it is important to consider:

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

Factors that increase the risk of product design: | GLOC U

``` Guarantees and options Lack of historical data Overheads Complexity Untested market ```

When considering accuracy and prudence of assumptions BIPACS O

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

Assumptions made form historic data/ Current data | BIDS/ REIS FW

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 ```

Demographic assumptions: | Morty Will Never FallS

``` Mortality/ Morbidity Withdrawal rates New business volumes/mix Future contributions Salary/Promotional increases ```

Examples of financially significant economic assumptions | 5CIET - Also for monitoring!

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

Contract Features Captured in a model point BAGTSAD

``` Basis Age Gender Term of policy Sum Assured Duration in force / Demographics ```

Setting the risk discount rate when modelling RASS

``` 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 ```

The risk discount rate (project appraisal) SyNC WACS

``` 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 ```

Requirements on the data used RRASH VoC

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

WACC Formula

(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)

How to determine the Cost of equity (CAPM model)

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

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

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

Data governance: Definition guidelines: RCaSCM ADACSIA SAS

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