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2008 General Meeting Assemblée générale 2008 Toronto, Ontario
Canadian Institute of Actuaries L’Institut canadien des actuaires 2008 General Meeting Assemblée générale 2008 Toronto, Ontario
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2008 General Meeting Assemblée générale 2008 Toronto, Ontario
TS-7 Pricing and Strategic Planning using Economic Capital and Risk Assessment Models “Risk Assessment Models” Solvency Framework Sub-Committee 2008 General Meeting Assemblée générale 2008 Toronto, Ontario
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TS 7 – Risk Assessment Models
Today’s Session will summarize an extensive report released in August 2008 by a CIA Working Group. The Report is a survey of considerations for any Advanced Model. The full report is available at:
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TS 7 – Risk Assessment Models
Members of the Working Group were: William Beatty Ron Harasym Trevor Howes Jean-Guy Lapointe Steven Prince Sylvain St-Georges Stuart Wason
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TS 7 – Risk Assessment Models
While each member was there as a professional, not an advocate, the members’ employers include: Insurance Company Regulator Consultant Software Vendor
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TS 7 – Risk Assessment Models
Objectives of the Report are to promote: Accuracy Comparability - between companies Consistency - between risks and dates Transparency Reliability Practicality
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TS 7 – Risk Assessment Models
An “Advanced Model” is typically one built to reflect company specific features. It is usually a simulation program. By contrast, a “Standard Model” means an established set of factors or methods. e.g., MCCSR factors per 1000 of insurance, or recalculate with PfAD increased X%.
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Most Advanced Models are Monte Carlo simulations. Much thought goes into computerizing in an optimal manner. It is possible to have Closed Form probabilistic models, i.e., Formulas. In practice, these become less workable as the number of interrelated factors increase.
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“Considerations” are things an actuary could expect to get asked about in a review of the model. “I didn’t think of that” is not a good answer. “It isn’t a big factor here because…”, or “We know it’s weak, but we’re doing…” are better answers. No model is perfect. The point is, awareness of the considerations.
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One of the messages is that everything should be considered, if not necessarily explicitly built into the model. Approximations are necessary to produce a workable model, but approximations need to be validated.
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Care should be taken than unintentional approximations do not creep into a model. e.g., for the sake of running time, number of scenarios or number of time periods was reduced and the impact turned out to be more significant than thought.
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The report refers to maintaining a “consistent level of sophistication”. Very precise calculations in one part of a model might not improve the overall result yet do slow it down. If little reliable data is available, precise calculations won’t make it any better. All of this within practical limits. e.g., first go-around has some known weaknesses that will be addressed in near future.
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However accurate a model is at the start, equal care needs to go into: Change control. Periodic updating of parameters, assumptions, data, and so on.
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TS 7 – Risk Assessment Models
The report describes Model Risk, i.e., risk that the model itself is flawed: Model Misspecification (right model, wrong setup) Assumption Misspecification (wrong assumptions) Inappropriate us of model Inadequate testing Lack of understanding by user or management Inadequate systems and/or change control Error and negligence
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End Users, Regulators, Investors, policyholders place considerable reliance on the models and those who build and use them. Errors have serious financial consequences and possible loss of reputation for the people and the company.
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TS 7 – Risk Assessment Models
Resources (IT, hardware, staff) Need to be adequate for the job, considering frequency of use, volume of data, and reporting time-lines. If resources are not adequate, either need more resources or need another approach.
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In-House vs. Third Party Software Neither approach guarantees accuracy. Whichever approach taken, need to be: Flexible. Able to advance with technology. Well-tested and robust. Well controlled code and managed updates. Permanence that vendor or developer will be available in future.
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Approximations Are essential but need to be validated. Several Categories Formulas. Assumptions and Parameters. Operational – Frequency of projection. Data – Grouping or Compression in place of seriatim.
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Risk Integration / Multiple Risks Can be formulaic relationship e.g. lapses depend on economic assumptions Can apply correlations to input variables e.g. claims rates on different products have a correlation factor. Can apply correlations when summing models of separate business lines.
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Diversification Benefits Related to Risk Integration Assuming all risks are independent leads to overstatement of Diversification Benefits. Assuming no Diversification leads to overstating capital requirements (unless you can show there is no actual diversification).
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Testing, Testing, Testing! Report contains many pages on dimensions in which testing occurs: Valid theoretical basis. Valid programming of that theory. Valid user of that program. Valid data in that program. Checking incremental changes, sample cases, etc. Adequate documentation and control
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Testing, Testing, Testing! Results are sometimes counter-intuitive. They aren’t necessarily wrong, but they need to be understood. A counter-intuitive result might lead to a key insight about the true risks.
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Pervasive Use Good models are widely used within a company for many purposes: Planning, Pricing, Capital Calculation and Allocation The more widely used a model is, the more consistent a company’s decisions will be. If multiple models exist in a company, need to understand why they might be giving different answers.
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Pervasive Use Risk Management and Modeling should be integrated into decision making, not added on at the end. That is a conceptual, not an organizational, comment. The report talks about ensuring the right information reaches all the relevant users. It doesn’t recommend any particular organizational structure to achieve this.
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Summary Models are inherently complex. Checking, validating, explaining, understanding, refining are all ongoing processes. This Report is a step, and only a step, in the continuing evolution of modeling.
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Discussion
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