Financial Condition Reporting for South African Short Term Insurers

Transcription

1 Draft Report Financial Condition Reporting for South African Short Term Insurers Calibration Project December 2005 Prepared for: Financial Services Board Prepared by: Deloitte and Insight ABC 1

2 Table of Contents 1 Glossary of Terms Overview Background, scope and overall framework Global comparisons Reliances and limitations Structure of the report Insurance Capital Charge Data Received Claims paid data store Ultimate Loss Ratio Investigation Framework Underlying risks and Risk Factors Notional companies Underwriting risk Reserving risk Earnings patterns Claims payment patterns Dynamic Financial Analysis Risk Measures Tables/Surfaces of Gross Risk Capital Diversification & Correlation Investment allowance Investment Capital Charge Overview Investment capital requirements in other territories and in Life assurance Estimation of capital factors for SA short term insurance Calculating the investment risk capital charge Other charges Operational risk Total Capital Required Covariance effects and Grossing-up Liability Estimation Claims reserves IBNR Investigation Premium reserves

3 8 Results of the calibration Overall results of the final calibration of the model Applicability of the proposed default model Applying the industry calibration in practice Recommendations Recommendations Acknowledgements References Appendices Appendix A: ULR s vs Gross Earned Premium for Different Classes of Business Appendix B: Means and Standard Deviations of ULR s Appendix C: Reserving Ratios per Class of Business Appendix D: CV of Reserving Ratio vs Gross Reserves Appendix E: Gross Stand-Alone Capital Simulation Results Appendix F: Graphs of Gross Stand-Alone Capital Appendix G: Description of The Smith Model Appendix H: Allocation of FSB Asset categories to modelled asset classes Appendix I: Parameters for the calculation of the diversification/correlation factor Appendix J: Example of individual company feedback

4 1 Glossary of Terms ASSA: Actuarial Society of South Africa AURR: Additional Unexpired Risk Reserve BE: Best Estimate DFA: Dynamic Financial Analysis EP: Earned Premium FCR: Financial Condition Report / Reporting FSB: Financial Services Board GIBNR: Gross IBNR GOCR: Gross OCR GWP: Gross Written Premium GUPR: Gross UPR IBNR: Incurred But Not Reported MCR: Minimum Capital Requirement NIBNR: Net IBNR NOCR: Net OCR NWP: Net Written Premium NUPR: Net UPR OCR: Outstanding Claims Reserves PGN: Professional Guidance Note (of ASSA) PM: Prescribed Method or Prescribed Margin RBC: Risk Based Capital TSM: The Smith Model ULR: Ultimate Loss Ratio UPR: Unearned Premium Reserve URR: Unexpired Risk Reserve VAR: Value at Risk WP: Written Premium 4

5 2 Overview 2.1 Background, scope and overall framework Actuarial & Insurance Solutions at Deloitte, and Insight ABC, were appointed by the Financial Services Board (FSB) in April 2005 to calibrate Financial Condition Reporting (FCR) requirements for the Short Term Insurance Industry in South Africa. Broadly, our aim was to construct a formula, on the basis of data from Star Returns, and Dynamic Financial Analysis, which would be an appropriate basis for a solvency requirement for the industry, for those companies that choose not to use an approved Internal Model. This had to be done in accordance with work performed by the FSB and by the Financial Condition Reporting Sub-Committee of the Short Term Insurance Committee of the Actuarial Society of South Africa. The formula has to take into account international developments, but at the same time be suitable for application in South Africa given available data in the STAR returns. Throughout our project, there were two major constraints, namely that STAR returns did not contain the data required or that data was not reliable. Secondly, the application of a central formula to the Short Term Industry as a whole will inevitably lead to situations where the formula does not fit individual companies with specific circumstances. Given the above, we sent individual results to all registered companies in South Africa, requesting feedback. This process was successful in the sense that it made companies aware of the project, highlighted areas where the modelling or data had to be modified, and indicated specific companies or sectors of the market where the application of an industry framework may not give optimal results for the individual companies involved. In line with the work of the Financial Condition Reporting Sub-Committee, we therefore support a regulatory framework that would allow companies to apply to the FSB for approval to determine a different level of capital, in accordance with a set of guidelines to be specified. For those companies that do not construct a complete risk-based internal model, the option of a certified model allows them to adapt elements of the regulatory framework to take into account their specific circumstances without having to set up a complex internal model. Further, we understand that the FSB would be open to approaches by companies with particular circumstances that may not be taken into account in the industry framework, or where a certified model or internal model specifies capital requirements that the company would struggle to meet. These companies would in practice be allowed to motivate why a special dispensation should apply to them, and the FSB would be free to consider the circumstances of the company and decide whether to grant permission for the company not to meet the capital requirements determined under any or all of the three models. It is envisaged that the FSB would, however, for benchmarking purposes check the industry calibration for every company registered in South Africa, whether the company in the end applies the industry framework, a certified model or an internal model. 5

6 The framework of industry calibration vs certified model vs internal model, can be represented graphically in the following way: Increasing Complexity Industry Calibration Certified Model Internal Model Increasing Appropriateness for Individual Company Increasing Cost Increasing usefulness in risk management Given the above, the characteristics of each of these different models can be summarised as follows: Industry Calibration Certified Model Internal Model Necessarily approximate Must be prudent for all companies May not be appropriate for circumstances of individual companies More precise for liabilities & individual circumstances of companies Involves judgment Hence requires professional certification Maximum precision for liabilities and assets Also requires professional certification Leads to greatest understanding of risks Provided models are transparent & realistic 6

7 The above overall framework is preferable to the existing capital requirement (effectively 25% of Net Written Premium), which: does not take into account the real risks faced by companies (i.e. it does not take into account the size of the insurer 1, the class of business written, the combination of classes of business written (i.e. correlation and diversification), expenses, and so on) requires a level of capital which is prudent for some companies but not prudent for others The only advantage of the current model is its simplicity. We hope that the industry calibration would be easy to apply in practice (i.e. driven by spreadsheets, possibly contained in the STAR returns) even though the mathematics operating in the background may be complex. The industry calibration should balance the following opposing factors: a desire for greater complexity to allow as accurately as possible for the individual circumstances of companies a desire to keep the model as simple as possible to apply and use We believe that a spreadsheet-based model contained in statutory returns would achieve this. Such a model would allow companies also to test new levels of capital required should they consider expansion, merger or other management actions. We now describe the regulatory framework in more detail. The following graphical representation of the new solvency requirements, taken from a presentation by Ms Hantie van Heerden of the FSB in August 2005, applies regardless of whether a company uses the industry calibration, a certified model or an internal model: Free assets Fair value of assets Fair value of admissable assets Excess assets Minimum capital requirement M inimum o f R10m. Prescribed basis. Internal model method. Liabilities Consists of best estimate plus additional prescribed margins. Prescribed method or internal model method. Risk management Financial condition report 1 Of course, the required Rand amount of capital increases as the net written premium increases, but as shown below, a capital requirement which is a constant percentage of net written premium does not accurately reflect the considerably higher risks faced by smaller companies, and lower risks faced by larger companies. 7

8 This model indicates that the new framework would establish the following principles for a Financial Condition Report: 1. Assets should be valued at fair value 2. Some assets will continue to be inadmissible for solvency calculation purposes (e.g. art) 3. A certain proportion of assets will be regarded as covering, or allocated to, insurance liabilities, or reserves. 4. For this purpose, insurance liabilities will consist of: a. Claims reserves, which in turn consists of: i. Incurred-but-not-reported (IBNR claims); and ii. Outstanding reported claims; and b. Premium reserves, which consist of: i. The Unexpired Premium Reserve (UPR); and ii. Where appropriate and needed, the Unexpired Risk Reserve (URR); and 5. The claims and premium reserves should be determined to be best estimates of the appropriate reserves. 6. A prescribed margin added to this takes the insurance liabilities up to the 75 th percentile Once this has been done, the minimum capital requirement is determined in such a way that the total capital minus the prescribed margins will reflect a certain level of sufficiency: 98%, 99% or 99.5%. In other words, the total required capital (which would be sufficient at the 98 th, 99 th or 99.5 th percentile) minus the prescribed margins would represent the minimum capital requirement, or MCR. 8. For smaller companies, the MCR would be subject to a minimum of R10m. 9. Excess assets are the admissible assets in excess of insurance liabilities (at a 75% level of sufficiency), and the MCR must be covered by excess assets under this framework. In assessing capital adequacy, this framework was developed by considering a one year time horizon. In other words, we did not consider the probability of insolvency over 5 years, but looked at the probability that a company would be insolvent within a one year period if it held a certain level of capital. In the rest of this report, we describe the methodology followed and assumptions made to calibrate an industry capital requirement that would meet the above objective. Our calibration aimed to provide an MCR that would take into account insurance risks and asset risks, and the determination of insurance liabilities (or reserves). Throughout our analysis, we fitted the model such that the total capital requirement is set at three levels of sufficiency: 98%, 99% and 99.5%. The reason is that we wanted to measure the impact of different levels of sufficiency on the capital requirement, in order to allow the FSB to judge the 2 Throughout this report, terminology such as 50 th percentile, or sufficiency at the 75% percentile level or 99% sufficient will be used interchangeably. All of these phrases express the same concept, namely that we can construct a probability distribution of outcomes for a company (i.e. a distribution reflecting the number of times out of many possible scenarios where a company s total capital would not be sufficient to meets its liabilities), and then measure the capital requirement at a point which would be sufficient to protect a company against insolvency half the time (set at the 50 th percentile), or against the 3 rd worst loss out of 4 (75 th percentile), or against the worst loss out of 200 (99.5 th percentile) 8

9 conservatism or otherwise of its final requirement. In the recommendations of this report, we also include a recommendation on the level of sufficiency that we believe should be adopted. 2.2 Global comparisons This section highlights some similarities and differences between our approach and that of other countries, particularly the UK and Australia. It must be noted that due to the multi-faceted nature of each country s regulatory solvency framework it is very difficult to make comparisons. Globally there has been a strong trend for regulators to move towards a risk-based capital approach for short-term insurance (and other financial services). Our proposal and the FSB s requirements are in line with this regulatory trend. Among others, the following countries have regulatory regimes applying an RBC approach: Australia United Kingdom USA Canada Germany Holland Switzerland Some broad similarities are the following: A value at risk (VAR) approach in determining capital requirements with a one year time horizon and a 99.5% level of sufficiency/confidence. A total balance sheet approach where risk in both assets and liabilities are considered Sufficiency in technical provisions set at a 75% level The framework addresses underwriting, reserving, credit and market risks with operational risk being difficult to quantify The allowance for internal models to be used It must be noted that there are still differences in the above from country to country. Some countries (most notably Switzerland) have adopted a Tail VAR approach as a measure of risk. It is recognised that Tail VAR may be mathematically more appealing since it is a consistent measure of risk. Despite this most countries have adopted a VAR approach to capital requirements. This may be due to the relative complexity of communicating tail VAR to stakeholders as compared to VAR. Our scope, as specified by the FSB, was to consider a VAR approach. In this regard we have produced results at the 99% and 98% levels of sufficiency as well as the 99.5% level. We have split our analysis into the eight classes of business set out in the Star returns. In the UK the comparable classes were further sub-divided into classes for proportional and non-proportional reinsurance. Unfortunately, the SA data available did not allow us to perform such a detailed analysis by reinsurance type. In Australia classes of business are grouped into three types that are felt to be broadly homogeneous and they also make allowance for a differential factor to be applied to reinsurance (without differentiating between proportional and non-proportional reinsurance). 9

10 The allowance for extreme events is an example of an area handled differently by different regulatory models. For example, in Australia they have explicitly modelled extreme events and made allowance for them. In the Star return data we did not have the ability to separate extreme events from attritional losses and our calibration thus implicitly models these different types of losses together. We have taken the opportunity of allowing our insurance risk charges to vary by the size of the account. This is an outgrowth of the investigations we performed where we noticed a distinct trend for the results to reduce in volatility as company size increased. We have taken into account risk dependencies as per the International Actuarial Association s (IAA) global framework. We have built-in a company-specific allowance for diversification and correlation and our base charges by class of business allow for this. This may also make comparison between our stand-alone charges and those in other regimes potentially misleading. Our diversification and correlation factor approach is similar to a study on reserving in Australia where adjustments to the sum of stand-alone reserves are made with reference to a predetermined model. This model explicitly links certain factors (like number of lines of business and concentration of business within one line) to a recommended diversification and correlation factor. We have been more direct in allowing the company in question to calculate a factor more specifically attuned to their circumstances. Due to the complexity of the resulting formula this can only be achieved through providing a pre-programmed spreadsheet for companies to use for this calculation. Another principle from the IAA global framework is the allowance for risk management measures. We have allowed for these in the form of reinsurance and believe that coinsurance should be treated similarly. Unfortunately not all risk management measures are straightforward to quantify and calibrate on an industry-wide basis, in particular those of cell captives. Bearing this in mind, the application of the framework needs to be flexible enough to allow for such situations. The allowance for covariance effects between the insurance capital charge and the investment capital charge as well as the grossing-up of these charges is similar in nature to that allowed for in the South African Life Insurance CAR calculations. 2.3 Reliances and limitations This report is produced on instruction of and for the purposes of the Financial Services Board. It is based on data and information provided to us, which, although we checked and cleansed such information as far as possible, we cannot guarantee the accuracy of. The approach taken in this report is for the purposes of an industry calibration, and should be interpreted in the context of an industrywide regulatory framework. We make no guarantees on the effectiveness of this framework applied to a particular company or a group of companies and we accept no responsibility for the solvency of one or more companies in the industry where this framework is applied, as solvency can be impacted by many other factors that cannot be taken into account in an industry capital requirement, such as inappropriate management action, etc. We do not accept responsibility for the application of the concepts explained here to a specific company and recommend that professional advice be obtained if a company wishes to determine an appropriate level of risk-based capital. The scope of this report is limited to an industry calibration for Financial Condition Reporting purposes. Whilst we make comments on the application of certified models and internal models, the details of such models and how they will be applied in practice are still to be determined and beyond the scope of this report. Further, at several stages of the project we were faced with inadequate data that prevented us from refining our calibration of the model. We therefore make some comments on areas where it may be appropriate to collect more data in STAR returns. It is beyond the scope of this report to make complete recommendations on the way in which STAR returns should be modified, and these comments and recommendations should be seen as preliminary. 10

11 This report represents our recommendations to the FSB and does not necessarily represent the final format and way in which the capital requirements will be implemented in practice. It is beyond the scope of this report to consider other factors that may impact on the format in which the FSB finally decides to implement an industry calibration, such as Black Economic Empowerment within the industry. As discussed in s2.1 above, the FSB may well allow a company to meet capital requirements under a special dispensation. 2.4 Structure of the report We first describe how the insurance risk capital charge was determined. This includes, among other matters, discussion of: data used and data cleansing the investigation of ultimate loss ratios the analysis of underwriting risk and reserving risk the dynamic financial analysis applied allowance for diversification and correlation We then describe the determination of the asset risk capital charge, which includes: the allocation of asset categories to liabilities the determination of capital factors using The Smith Model, including a description of The Smith Model The next section of the report describes how the insurance risk capital charge and investment risk capital charge are combined to determine the total capital required. We then describe the calibration of industry reserving levels, which includes claims reserves and premium reserves. We also discuss the determination of the prescribed margin, to take the estimate of liabilities up to the 75% level of sufficiency. Once this has been done, we are in a position to describe the Minimum Capital Requirement (MCR) for each company in the industry. We show how the suggested MCR affect the different sectors of the market, and how it changes if we set capital at different levels of sufficiency. Finally, we make recommendations. 11

12 3 Insurance Capital Charge 3.1 Data Received The main source of information for this exercise was the historic Star returns of all companies in the short-term insurance industry in South Africa. Where available, the FSB provided us with STAR returns dating back to The first phase of this project consisted of extracting the appropriate data from the Star returns and creating the appropriate data stores for the investigations needed (such as those for underwriting risk and reserving risk). In total 1075 star returns were included in the analysis. Initial calibrations of the model were performed using subsets of the data in order the speed up calculations. Initially analyses were performed on 33 companies, thus covering about 95% of the market by written premiums. The final calibration of the model was performed including all 118 companies present in our sample at one time or another. Approximately 35 companies had not submitted their 2004 returns when the initial batch of data was received from the FSB. The FSB then forwarded the returns as they became available. At this stage the 2004 returns were not available for 27 companies. In most cases this was because the respective company had ceased to exist, a restructuring such as a merger had taken place or the company had changed its name. Consequently after allowing for all these factors, returns for only two active companies were outstanding. Data for years before1999 was generally less reliable. Most of the files were in Excel format, with a number of returns for years prior to 1999 being in Lotus format. These were converted to Excel format. The table below indicates the number of returns received for each year: Year Number of STAR returns Total

13 3.1.1 Duplicates After removing blank and corrupted files, allowing for changes in company names as well as restructurings duplicate file were found for 5 companies. In most cases these simply represented duplicates and one of the files was excluded. However in some cases one of the files represented a data update received from the company. These were identified and the updated versions were included in the analyses Formats The format of the STAR return changed in A mapping of the old format onto the new format was constructed and subsequently allowed for when extracting data from the STAR returns. The line of business classification also changed, with the Miscellaneous class being further subdivided into Miscellaneous, Liability and Engineering. The consistency of the return formats was checked and a small number of files were found in which the layout and/or location of certain sections was different from that of the standard STAR return format. These were corrected and included in the analyses. It should be noted that the STAR returns showed considerable deviations from the standard format from section 13 onwards. However these sections did not contain information crucial to our analysis and as a result this posed no problem Consistency of dates Considerable time was spent cleaning the date fields in the returns. This arose because dates were either left blank or entered in incorrect formats. For example: 1999, 25th of January as opposed to 25-Jan This problem related mainly to the old STAR return format as the new format has separate cells for the day, month and year values. Financial year end changes also required individual consideration to avoid inappropriate financial years being recorded Consistency of company names As mentioned earlier, some companies underwent name changes and restructurings. This had to be considered when extracting our data to ensure that the relevant STAR return data be included under the appropriate companies. A great deal of time was also spent on cleaning the company names as recorded in the STAR returns. This arose because a company s name would be spelled differently for each year s submitted returns. The following are examples of the spelling variations present in the data: ABC XYZ A.B.C. XYZ INS CO. LTD A.B.C. XYZ INSURANCE CO LTD ABC Xyz Insurance Company Limited A B C XYZ Ins Company Limited In addition, a few STAR returns were found to have blank company name fields. These names were inserted after identifying which companies the data related to. 13

14 3.1.5 Invalid/erroneous entries A phenomenon which resulted in a significant amount of cleaning and subjective consideration was the presence of blank cells. It would seem that many companies simply leave cells blank where in fact they should be recording zeros. This presented a problem because it was also found that companies often neglect to complete certain sections of the STAR return. Consequently, it becomes difficult to distinguish between incomplete data and zeros. In these cases it became necessary to investigate the matter individually. Invalid characters were occasionally found in the data. For example, some companies entered the text character - instead of a zero. The character was also found which indicated the use of nonstandard characters in the cells Points relating to the various analyses performed: Triangle extraction This exercise highlighted to a great extent where data was unreliable or incomplete. Blank diagonals were often noticed, indicating that companies did not complete a particular development triangle in a particular year. Interpolation and/or extrapolation were applied in certain cases to estimate individual blank cells to avoid losing a significant amount of otherwise valuable data. This exercise of trying to salvage data was of particular importance since the data was generally sparse to begin with ULR analysis While calculating and analysing claims run-off patterns, we found the calculated development factors were very useful in identifying tainted data. Claims were considered in relation to premiums, thus providing a better view of the reasonability of the magnitudes of the recorded claim amounts. Extreme ULR s (e.g. 3000%, -80%) indicated potentially incorrectly recorded earned premiums. Where these were identified the points were excluded from our analyses. Where it was fairly obvious that a data capturing error had been made (e.g. a zero left off the end of a figure), the data was corrected and included in further analyses. Where claims triangles had one or two blanks but otherwise contained reasonable data, linear interpolation was used to estimate the blanks from the surrounding data. This was done so that we were still able to calculate development factors for a company from its data and include the resulting ULR estimates in the subsequent statistical analyses. Where no earned premium data was available, we could not calculate ULR s for the relevant companies. However, where earned premium values were not recorded for a year but a reasonable amount of data existed around the missing value we estimated it by linear interpolation. In doing so we avoided losing potentially valuable data. 14

15 The following table indicates the number of data points used per class for the ULR analysis: Class Number of data points included in analysis Accident 358 Engineering 98 Guarantee 217 Liability 117 Miscellaneous 167 Motor 435 Property 522 Transport Reserving Risk analyses As with the ULR analyses, further insight into the overall reasonability of the data was obtained by considering the claims development triangles in relation to the OCR and IBNR development triangles. The OCR and IBNR development areas in the STAR returns were often filled out poorly. Cells are often left blank as opposed to explicitly being recorded as being zero. Often it is not clear whether the data is not available or actually zero. Judgement had to be applied on a company-by-company basis to determine which of the above applies. The following table indicates the number of data points used per class for the Reserving Risk analysis: Class Number of data points included in analysis Accident 332 Engineering 74 Guarantee 249 Liability 93 Miscellaneous 158 Motor 490 Property 441 Transport IBNR Analysis We extracted the IBNR triangles from the STAR returns for this analysis. In some cases insufficient data existed for the purposes of calculating chain ladder development factors, yet the data was sufficient to calculate at least two years IBNR figures. In these cases industry average development factors (weighted by gross earned premium) were calculated from the reliable data and used to estimate the relevant companies IBNR figures. 15

16 The following table indicates the number of data points used per class for the IBNR analysis: Class Number of data points included in analysis Accident 192 Engineering 67 Guarantee 87 Liability 66 Miscellaneous 77 Motor 242 Property 238 Transport Other points: Values in separate areas of STAR returns did not always reconcile. More automatic cross-checks in place could reduce the amount of invalid data input conditional formatting could be used. STAR returns are supposed to be locked with a password. In many cases it became evident that this was somehow sidestepped or overridden especially where structural changes had been made to the layout of the spreadsheet Extraction tool To check the consistency and validity of the data, we created a spreadsheet tool that runs through the STAR returns and extracts a specified field. This tool helped us to identify a number of data issues such as: Duplicate, blank and corrupted files Inconsistent financial year ends Inconsistencies or changes in company names and structures (e.g. mergers) The matters identified were presented to the FSB and most were resolved. An issue which received particular attention was the consistent naming of companies. It was necessary to construct a mapping of the companies names to obtain a list of companies which allowed for spelling errors and the fact that some companies legally changed names or ownership. This was done with the help of the FSB. A similar mapping had to be done on the financial year ends to allow for companies who either entered invalid dates or underwent a change of financial year end. The spreadsheet described above also allows the user to compile a custom database of information contained in the STAR returns and has been made available to the FSB for their own future use. Where possible we included the most recent Star returns (2004). Some companies submitted their 2004 Star returns during our investigation. 16

17 3.1.9 Grouping tool An additional tool that we created during our data extraction and analysis was one that enables the user to create various custom combinations of STAR returns. For example if two companies merged in 2003 and the user wishes to analyse their consolidated returns for years before 2003, he/she could join the STAR returns for the required periods required using this spreadsheet tool. The spreadsheet allows the user to define custom groupings. For example all re-insurers could be grouped and analysed together. The results produced by this spreadsheet are in the latest STAR returns format. The user also has the flexibility of being able to specify exactly which areas of the STAR returns he/she would like to have amalgamated. This spreadsheet has also been made available to the FSB for internal use. Custom combinations of STAR returns 3.2 Claims paid data store The two primary data investigations centred around the analysis of claims paid triangles (for the purposes of determining suitable ultimate loss ratios) and reserve run-off triangles (for the purpose of determining reserving uncertainty). This section describes the extraction of this data and the creation of this data store for each class of business. We applied the spreadsheet extraction tool described above which loops through the available STAR returns for every company and extracts the relevant data. Payment and reserve triangles are then constructed from this data. This spreadsheet allows the user to extract the following data for each development year: Claims payment run-offs Outstanding claims reserves IBNR reserves Relevant earned premium 17

18 Each line in the data corresponds to a particular historic accident year for a particular company. It is then possible to see how claims and reserves have developed for that particular accident year. The program also automatically creates a cumulative paid claims triangle from the incremental claims paid triangle. This enables the analysis of the claims run-off via traditional methods such as the Chain- Ladder method and Bornheutter-Ferguson method. Development triangle extraction tool 3.3 Ultimate Loss Ratio Investigation This section describes our use of the claims paid data store for the purposes of analysing ultimate loss ratios. Incurred loss ratios are included in the STAR returns. They include estimates of reserves for outstanding claims at the end of each period that are subject to estimation uncertainty. Our investigations attempted to separate this reserving uncertainty from the underwriting uncertainty by looking at how claims paid had historically run-off in each historic accident year. For each of the accident years that was fully run-off we had an estimate of the ultimate loss ratio and for those years that were not fully run-off we made an estimate of the ultimate loss ratio by applying grossing-up factors based on Chain-Ladder and/or Bornheutter-Ferguson ( BHF ) methods, where appropriate. In a sense, some of the ultimate loss ratios represent partially manufactured data. The primary reason for this is the benefits of having observations undistorted by reserving estimates. The following steps were applied for each company, for each accident year: The traditional chain ladder method was used to calculate the development factors for each of the development periods. Unfortunately, the information available in the STAR returns only extends for five development years. To estimate the tail factor (the cumulative development beyond development year five) we fitted a company specific curve to the development factors and used this as a basis for extrapolation. 18

19 For all companies with sufficient data in their triangles we consolidate their results to estimate an industry-wide development pattern. Where the data/triangle for a particular company is sparse we use the industry estimate for each class as discussed above. This then yields a first estimate of the ultimate loss for each accident year based on fitted chainladder factors. A second independent estimate of the ultimate loss was calculated using the BHF method based on observed historical loss ratios and earned premiums specific to the company in question. If the claims are near to being fully run-off we use the first estimate whereas if the claims from the accident year are less than 50% developed we use the BHF estimate. The resulting ULR s give our best estimate of the ultimate claims emerging for each company from each accident year. These manufactured observations form the basis of later investigations into the distribution characteristics of the loss ratio and reserving risk. The following two graphs show cumulative claims development data from the motor class for two example companies: Cumulative claims development 120.0% 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% -20.0% Company A Development Year

20 120.0% Company B Cumulative claims development 100.0% 80.0% 60.0% 40.0% 20.0% 0.0% -20.0% Development Year The above two graphs confirm the short-tailed nature of the motor class. For both companies shown claims are close to 100% developed by the second development year. Due to the short tailed nature of the motor line of business, the BHF method is not strictly necessary. However, we believe it to be more appropriate for the other, longer tailed classes. As such we have built in a measure of flexibility regarding the level at which the BHF method would apply. 3.4 Framework We are proposing a building-block approach for the calculation of the required insurance capital to promote transparency and flexibility in the use of the framework. In setting the insurance capital the following aspects are allowed for: The type of business underwritten and the relative risks and rewards of the type of business The amount of business written and concomitant diversification effects The relative amount of underwriting risk versus reserving risk The mitigation of risk through use of reinsurance Expenses Correlation between classes of business Diversification effects of writing different classes of business The diagram below shows how the insurance capital of a short-term insurer is built-up from various components. This diagram is best understood from the bottom-up, since each component of risk feeds upwards into the component above it. 20

21 Insurance Capital Required Investment income and capital gains Diversification / Correlation Net stand-alone capital ACC ENG GTEE LIAB MISC MOT PROP TRANS Retention Retention Expenses Expenses Gross stand-alone risk capital ACC ENG GTEE LIAB MISC MOT PROP TRANS GWP GWP GUPR GUPR Insurance capital for a short-term insurer The workings of the diagram above are given below, with more detailed explanations of each component Gross stand-alone risk capital This is an estimate of the capital required for pure insurance risks (underwriting and reserving) before allowance for reinsurance and expenses. Stand-alone capital is what an insurer needs to hold without reference to any of the other classes of business being underwritten. Our framework differentiates between: Different classes of insurance business Different account sizes within each class of insurance business 21

22 We calibrated each class of business within the STAR returns separately on the basis that each class has different underlying risk and return characteristics. These classes are: Accident (ACC) Engineering (ENG) Guarantee (GTEE) Liability (LIAB) Miscellaneous (MISC) Motor (MOT) Property (PROP) Transport (TRA) Each class was investigated individually, though the same types of investigations were performed for each class. We are aware that a more detailed split within each class may have yielded a more accurate identification of risk. However, sufficient quantities of data was not available to achieve statistical significance at this level. Some factors that may affect risk within a class are: Policy term Different types of policy Differing target markets To further allow for the specific risks inherent within each class of business we also analysed each class/account by size. An account with a higher gross written premium and/or gross unearned premium is likely to exhibit lower volatility due to having a greater number of risks on the books. This is an example of diversification within a class of business. We allow for diversification between classes of business at a later stage. The data suggests that larger accounts tend to exhibit lower volatility and in some cases the larger accounts also exhibited greater pricing power. This can be seen in Appendix A where we show the scatter-plot for each class of business of the ultimate loss ratio versus the gross earned premium. In general, as gross earned premium increases we see greater stability in the resulting loss ratios. The following two items are used as a measure of the size of the account: Gross written premium (GWP) Gross unearned premium (GUPR) In essence we are building up the capital required in the coming financial year so we need to consider the specifics of each class of business in the coming financial year. As such, the gross written premium used needs to be an estimate of gross written premium in the coming year. Note that such an estimate would have to be made at the time of submission of STAR returns, and hence always with at least a three month lag. For this reason, we believe that the estimate of gross written premium over the following year would not present problems to most companies, and it would be easy for the FSB to check whether a company consistently under- or over-estimates its gross written premium for the purposes of setting capital. The estimate of gross written premium can be made with reference to the past financial year if it felt that the business level will remain broadly constant. Where changes to the level of business are projected and/or budgeted for these should be taken into account. Since the capital requirements will be calculated at the same time as the sending of the statutory returns, insurers will already be partially into the new financial year and will thus have a further indication of business levels. 22

23 3.4.2 Net stand-alone capital As with gross written premium above, companies will need to consider the specifics of each class of business in the coming financial year. As such, the following estimates need to be made for each class of business: Retention Expenses The retention percentage for the coming financial year can be estimated with reference to the past financial year if the reinsurance strategy for that class has not changed. Where changes in reinsurance strategy are known, they should be taken into account in determining the retention percentage to use. The gross stand-alone risk capital is multiplied by the anticipated retention percentage in each class of business to give a net stand-alone risk capital. It is important to note that the allowance for reinsurance in the calculation is not ideal, in particular for non-proportional reinsurance. However, we did not have detailed data available in STAR returns for a more in-depth calibration taking into account the type of reinsurance taken out by a company. We recommend that more detailed statistics be collected about proportional and non-proportional reinsurance in STAR returns. We discuss the allowance for reinsurance in more detail below, in the section on Underwriting Risk (s3.7). In the event of a worst-case insurance loss (as envisaged by the net stand-alone risk capital calculated at this point) the company will also incur expenses in the normal course of business. These expenses thus need to be allowed for in the capital requirements of each class of business by adding them to the net stand-alone risk capital. Companies can use their current level of expenses as an indication of the likely level of expenses in the coming year. Further, should companies feel that their expenses would rise significantly in times of high insurance losses they should estimate their expenses on this basis Allowance for diversification and correlation The total net stand-alone capital above does not take into account the following effects from writing multiple lines of business: Diversification effects due to writing more than one class of business (these will in general reduce the capital required) Correlation effects between the different classes of business (these will in general increase the capital required since classes tend to be positively correlated) If it is expected that the worst-case insurance event will occur at the same time for each class of business then we would hold the sum of the net stand-alone capitals calculated above as insurance capital against such an event. However, the risks in the eight separate classes of business are not perfectly correlated and we allow for this by multiplying the sum of the net stand-alone capitals by a statistically determined factor that allows for the relative mix of business in each class. The derivation of this factor is discussed later Allowance for investment returns The final allowance is for investment returns on assets backing the relevant liabilities. The argument is that even in a worst case scenario, a company will still earn some return on its existing assets, and this can be used to reduce the capital requirement. The default approach will be for companies to take into account investment return on all assets backing the premiums liabilities (unearned premium reserve, unexpired risk reserve) and claims liabilities (outstanding reported claims reserve and incurred but not reported claims reserve). We discuss our suggested allowance for this in more detail below. 23

24 3.5 Underlying risks and Risk Factors This section discusses the main components in the calibration of gross stand-alone risk capital and sets the scene for the various sections that follow Underlying risks Within each class of business we are proposing a framework that links the underlying risks to certain observable risk factors. The main risks underlying the insurance capital charge can be labelled as: Underwriting risk Reserving risk Underwriting risk is the risk that premium earned in future periods will be insufficient to cover claims incurred in those periods. Inherently, underwriting risk is forward looking. Reserving risk is the risk that claims incurred in historic periods will be greater than anticipated (and reserved for) in those periods. Inherently, reserving risk is backward looking. Calibrating the uncertainty in both of these risks formed a major part of this exercise and the resulting data analysis is discussed more fully in later sections. We use the results of this analysis to parameterise our simulation model. These parameters then flow into the DFA engine where underwriting results are calculated. Underwriting Risk Reserving Risk DFA Engine Simulation Results Gross Written Premium Gross Unearned Premium Simulation Model 24

25 We performed 100,000 simulations for each class of business and company size to ascertain a reliable estimate of capital required at each level of sufficiency. These estimates were then linked to company size via the following risk factors, as shown in the graphic illustration above: Gross written premium Gross unearned premium Risk Factors This section details how we derived the stand-alone capital charge for each line of business. In line with discussions above we have applied different assumptions for accounts of differing size. In general this implies that larger accounts have lower capital requirements relative to written premium (essentially representing a diversification credit). Conversely smaller accounts have higher relative capital requirements. The factors we have chosen to measure the size of the account are: Gross written premium (projected over the next financial year) Gross unearned premium The reason for our choice of these two figures is that they are broadly verifiable and are seen to be less subject to manipulation than other alternative figures. The main alternative candidate for use was outstanding claims reserves. A historic shortfall of regulatory frameworks has been the inability to distinguish between regular companies, young companies growing rapidly and older companies running-off their existing book of business. The two factors above allow for this differentiation to some extent. The reason for this is that the written premium is a forward looking measure and the unearned premium is a retrospective measure. Young, rapidly growing companies will have a lower component of unearned premiums (relative to regular, established companies). Companies in run-off may project lower future premiums but may well have a high component of unearned premiums. A potential weakness using these factors is that a company may be in run-off, have low future premiums and low unearned premiums but still may exhibit significant risk from its outstanding claims reserves. This is provided for, to an extent, in the prescribed margin included in insurance liabilities. 3.6 Notional companies It is not possible (nor necessary) to run models for every single possible size of insurance account (measured by gross written premium and unearned premium). Instead, we have calculated the exact gross stand-alone capital requirements for a broad and representative sample of hypothetical accounts (to be discussed below) with the aim of applying these results in a smooth manner across the range of account sizes. For each class of business we have run DFA models for 99 different account sizes. These allow for: 11 different levels of gross written premium 9 different levels of gross unearned premium Future growth in account sizes from present levels Our approach can therefore be illustrated approximately in the following way: out of a universe of 36 potential or observed combinations of GUPR and GWP, we choose 4 and model these explicitly. Any actual combination of GUPR and GWP that falls within these four points can then be estimated by 25