2013 Seminar for the Appointed Actuary Colloque pour l actuaire désigné 2013 Session 13: Preparing for ORSA - Some practical issues Speaker: André Racine, Principal Eckler Ltd.
Context of ORSA Agenda Place of ORSA in overall regulation Concepts inherent to ORSA Actuary s role in ORSA Translating statement of risk appetite into capital and risk exposure requirements Stochastic tools Challenges for small and medium-size insurers 2 2
Context of ORSA Board and management commitment ORSA seeks to transform regulation into governance ORSA must be developed and endorsed by the Board and Management ORSA must not be seen as compliance ORSA must not be seen as an actuarial process Statement of Risk Appetite Integrating risk in decision-making Capital decision Business plan Operations Metrics 3 3
Purpose of the ORSA To help companies to manage their specific short term and long term risks pro-actively, including risks not included in the SCR Identification and assessment of the material risks Follow up of the significant changes in the risk profile To align the business strategy with its risk appetite Maintain consistency between risk, capital and performance Efficient capital management To help to demonstrate the continuous compliance of the capital needs given the Risk Appetite ORSA and Solvency II (source : Milliman CCF) Risk profile / Risk Assessment ORSA framework Risk Appetite Business strategy & performance Overall Solvency Needs Continuous compliance Performance Assessment 4 4
ORSA under Solvency II (source : Milliman CCF) The structure of the regime is similar to Basel II (for Banks): Pillar 1 Quantitative requirements Pillar 2 Qualitative requirements Pillar 3 Disclosure requirements Balance sheet (technical provisions, assets, etc) Capital rules (MCR, SCR), different approaches (standard formula vs internal model) Eligible elements Governance and risk management ORSA Supervisory intervention (capital add-on) To the supervisor (RSR) To the public/market (SFCR) Quantitative reporting ORSA is usually seen as a Pillar 2 requirement. However it is also closely linked to Pillar I (technical specifications of the ORSA model) and Pillar III (production of an ORSA reports) 5 5
Statement of Risk Appetite Not about where you are now (MCT) Not about where you might be (DCAT) About where you want to be Forward-looking over several years What risks are you willing to take to get there What will you not risk? Emphasis on business continuity, not only on solvency 6 6
Statement of Risk Appetite All forms of risk Insurance (liabilities and exposure to future events) Market and credit (all assets including reinsurance) Operations Soft (reputational, regulatory) Strategic Acquisitions Diversification (products, geography, distribution Innovation Consider multiple risks at once Consider risk not only to capital, but also to other key metrics Ability to grow Financial rating Stability of earnings 7 7
Statement of Risk Appetite Commitment to adhere Business plan must comply Operations must comply Follow-up Develop metrics Report on compliance 8 8
Actuary s role in ORSA ORSA does not belong to the actuary ORSA is a qualitative process ORSA must be developed and endorsed by the Board and Management The actuary s input is essential to ORSA s implementation to establish Confirm reasonability of statement of risk appetite Risk measurement leading to business decision Compliance metrics 9 9
Risk measurement within ORSA Full internal model not a viable option for a small or medium insurer Insurance risk Can be measured for the most part with stochastic tools, with caveats Credit and market risk Can be measured for the most part with stochastic tools, with caveats Operational risk Relation to the first two seems the best approach Reputational risk Important but not measurable Strategic risk Quantifiable by judgment only 10 10
Insurance risk measurement within ORSA For most insurance covers, a well adjusted bootstrap model will provide reasonable distributions for Unpaid claim liabilities Prospective claims Data can usually be adjusted for Exposure change over time and heteroscedasticity Outliers with limited credibility The issue of correlation can be dealt with by Overall distribution, if mix has been constant for many years Successive cover to cover correlation analysis Binary events (absent from the data) must be modeled separately Reinsurers often provide PML for catastrophes A model based on data net of reinsurance is reliable only if retentions have remained stable over the years in relation to gross exposure 11 11
Probability Insurance risk measurement within ORSA Typical outcome from the reserve variability model we use: Unpaid claim liabilities Best Estimate (Weighted) Paid Chain Ladder Incurred Chain Ladder Paid BF Incurred BF 0 50.0K 100.0K 150.0K 200.0K 250.0K 300.0K 350.0K Unpaids 12 12
Probability Insurance risk measurement within ORSA Typical outcome from the reserve variability model we use: Unpaid claim liabilities 55.4K 66.8K 78.1K 89.4K 100.8K 112.1K 123.4K 134.8K 146.1K 157.5K 168.8K 13 13
Insurance risk measurement within ORSA Typical outcome from the reserve variability model we use: Unpaid claim liabilities Estimated Unpaid - Best Estimate (Weighted) Mean Standard Error Coefficient of Variation Minimum Maximum 50,0% 60,0% 75,0% 80,0% 90,0% 95,0% 97,5% 99,0% 99,5% 99,9% Total 94 682 13 231 14,0% 54 857 169 367 93 764 97 173 103 112 105 465 112 072 117 485 122 711 129 781 134 508 143 787 Normal Percentiles 94 682 13 230 14,0% 94 682 98 034 103 606 105 817 111 637 116 444 120 613 125 460 128 761 135 567 Lognormal Percentiles 94 683 13 244 14,0% 93 770 97 136 103 000 105 425 112 082 117 896 123 183 129 628 134 209 144 172 Gamma Percentiles 94 682 13 154 13,9% 94 073 97 433 103 191 105 538 111 882 117 306 122 147 127 939 131 983 140 580 TVaR 105 172 107 596 112 132 114 091 119 696 124 904 130 018 136 464 140 764 150 631 Normal TVaR 105 238 107 460 111 499 113 202 117 901 121 972 125 612 129 943 132 943 139 230 Lognormal TVaR 105 165 107 598 112 189 114 191 119 919 125 129 129 986 136 020 140 366 149 937 Gamma TVaR 105 149 107 502 111 882 113 769 119 093 123 844 128 199 133 515 137 282 145 399 14 14
Insurance risk measurement comments Models usually do not assume correlation between unpaid claims and prospective claims Correlation is not between the distributions, but between unpaid claim development and initial expected loss ratio (IELR) for future claims What is predicted by the model is really the distribution around the IELR, so that the distribution is still valid if we assume an impact on the IELR When data is scarce, initial assumptions (expected values, coefficients of variation, etc.) can be selected outside of the model In most cases, the difference between the actual model and a probability distribution analysis (even using the Normal distribution) is not large in ORSA s context Even with a model, testing the impact of change in per risk or per event retentions and limits is challenging 15 15
Market and credit risk measurement within ORSA Economic scenario generators (ESG) are commercially available in Canada to predict stochastically the separate and joint distributions of : Bond yields for various durations Bond yield spread for bonds of various grades Stock market prices Inflation Unemployment Gross domestic product Similar or more detailed models are also available for other economies if an insurer is exposed to them ESG is useful in assessing Market risk for bonds and common stocks that form the bulk of most insurers investment portfolio Interest rate risk relating to asset/liability mismatch Insurance risk impacted by: Inflation Unemployment General economic conditions 16 16
Yield Market risk measurement within ORSA Typical outcome from the economic scenario generator model we use: Bond spot yield (source: GEMS by Conning) Average Annual Nominal Spot Yield Curves as at 2011Q4 0.06 0.05 0.04 0.03 0.02 0.01 0 Maturity 2011M12 2012M03 2012M06 2012M09 2012M12 2013M03 2013M06 2013M09 2013M12 2014M03 2014M06 2014M09 2014M12 2015M03 2015M06 2015M09 2015M12 2016M03 2016M06 2016M09 2016M12 17 17
Market risk measurement within ORSA Typical outcome from the economic scenario generator model we use: CPI index (source: GEMS by Conning) CPI distribution forecasted to June 2013 seen at December 2011 Statistics Statistics avg 1,96% min -3,03% std 1,83% 0,01-1,56% coeff_var 0,9303 0,05-0,80% inf 1,0749 0,10-0,25% skew 0,5066 0,25 0,67% kurt 0,4043 0,50 1,81% 0,75 3,10% 0,90 4,39% 0,95 5,18% 0,98 6,19% 0,99 6,87% max 10,85% 18 18
Limitations to models for market and credit risk measurement within ORSA EGS works only at a class level Does not predict the performance of an individual title Model is national while insurer s interest may be regional, thus different for inflation and economic activity Does not (for Canada) split common stock prices by industry sector The EGS average moves from current position to long-term level May conflict with short term predictions from other sources Many elements of the market and credit risk are not measured Credit risk for bonds Downgrading Default Market and credit risk for other investments Credit risk for reinsurance Credit risk for receivables Foreign exchange risk 19 19
Operational risk measurement within ORSA In the MCT, capital required for operational risk is based on Gross premiums written Ceded premiums written Gross premium growth A ratio to capital required for insurance, market and credit risk A similar approach seems reasonable for ORSA Measure of risk for ORSA should reflect Dependence on systems Dependence on key providers and key resources Automated controls Vulnerability to aggressive competition Vulnerability to fraud Etc. 20 20
ORSA challenges for small and medium insurers Define risk appetite Board and management are often not familiar with risk measurement concepts such as risk tolerance Think of the multiple sources of risk Small insurers often concentrate on the insurance risk and not to the combination of bad underwriting and investment results Before fair value accounting, insurers could offset underwriting results with capital gains, but this is no longer possible Understand the cost of risk avoidance Small insurers will often keep low retentions at considerable cost Small insurers will often keep very conservative investments, waiving substantial return Implement risk compliance metrics 21 21