SHAPING THE FUTURE OF INSURANCE RISK MODELLING: INSIGHTS FROM A SURVEY OF U.K. GENERAL INSURERS WHITE PAPER RISK MANAGEMENT

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1 WHITE PAPER RISK MANAGEMENT SHAPING THE FUTURE OF INSURANCE RISK MODELLING: INSIGHTS FROM A SURVEY OF U.K. GENERAL INSURERS BY NEIL COVINGTON DIRECTOR OF SOLUTIONS MANAGEMENT GENERAL INSURANCE FIS INSURANCE BUSINESS AND SIMON SHEAF HEAD OF GENERAL INSURANCE ACTUARIAL AND RISK GRANT THORNTON

2 Contents 1 Introduction: Completing the risk modelling revolution 2 Validation as a case in point 4 Engaging the business 5 The wider challenge of resourcing 6 Risk modelling as a firm-wide, end-to-end process 10 Multiple platforms, resourcing, and the need for agility 11 Conclusion: Shaping the future of risk modelling 12 About the survey 13 Appendix: How teams spend their time

3 Shaping the Future of Insurance Risk Modelling 1 INTRODUCTION COMPLETING THE RISK MODELLING REVOLUTION General insurers have worked hard for a number of years to put themselves in a position to be compliant with Solvency II on 1 January 2016, but few think the work is over. In the run up to the compliance deadline, risk professionals have had to build many new processes to improve their approach to risk modelling for capital adequacy and related activities such as loss reserving and pricing. Risk modelling has been transformed from a standalone statistical exercise into a large collection of interrelated activities delivering business analytics, with each activity governed by a host of new regulatory standards and related internal policies (from statistical, documentation, and data quality standards to validation procedures themselves). Firms have now reached the point where they believe each individual activity meets the given technical standard, but the intensity of their focus has not always allowed them to take in the wider view of their risk modelling processes as a functioning whole. The component parts may not work together efficiently or be supported by the right infrastructure. This is not unusual in the financial services industry: large projects often have life-cycles that involve throwing resources and energy at a problem, shaping initial efforts into a reasonable approach that meets key standards, and finally re-working the solution so that it is efficiently integrated into the business. This can make sense when regulatory and industry standards present moving targets with uncertain deadlines and difficult-to-interpret practical implications. The process is akin to the classic stages of team formation (Figure 1), 1 as teams move from forming (a period of orientation and initial setup), to storming (a period of tensions with questions asked and changes made), norming (new standards, processes and roles evolve) and eventually performing (efficient and cohesive). Figure 1: Classic stages of team formation Effectiveness Forming With the industry now starting the norming stage, leading firms are considering how to refashion their responses to Solvency II into a more coherent and durable end-to-end risk management process that moves them to the performing stage and delivers additional value from the investments they have made. In this white paper we look at objective evidence for this in the form of a recent FIS and Grant Thornton survey of senior risk professionals (Table 1), discuss the key challenges, and examine how risk professionals and their colleagues might now take steps towards shaping more valuable risk modelling processes for the business as a whole. Table 1: Selected survey results Time Storming Norming Performing 50 percent of surveyed senior risk professionals think their risk modelling processes need re-engineering within the next three years. More than a quarter of those responding say the re-engineering need is urgent, i.e., as soon as possible. Resourcing ranks as the top concern for senior risk professionals with over 60 percent naming it as one of their top three risk modelling challenges. Speed, operational control, reporting to regulators and data quality are also top three risk modelling challenges for around a third of respondents percent of those responding say they lack time and resources to carry out validation as effectively and efficiently as they would wish. 1: THE CLASSIC MODEL OF TEAM FORMATION WAS SET OUT BY PSYCHOLOGIST BRUCE TUCKMAN IN THE 1960S.

4 2 Shaping the Future of Insurance Risk Modelling Validation as a case in point The area of validation illustrates the issue well, as it encompasses an ongoing cycle of assessment and corrective actions. In a Solvency II context, validation is also wideranging and covers risk identification, data, models, systems and processes, as well as how model output is reported and applied, e.g., for business decisions as well as capital adequacy. Teams appear less concerned over their ability to meet technical validation standards by the 1 January 2016 deadline than by the amount of time and skilled labour it is taking to put them in that position for each calculation cycle. Figure 2 shows the degree to which survey respondents, as a group, felt they lacked the time and resources to effectively carry out a range of key activities. Validation is clearly the activity that causes the most concern, though it is not the only trouble spot. The level of concern is striking, although the important word here is effectively. Companies are reasonably comfortable with the systems and processes they have in place for validation, and they expect to be Solvency II compliant, but they feel that they lack the time and resources to carry out the process as effectively and efficiently as they might wish. What exactly is causing the worries? Table 2 sets out some challenges generally acknowledged in the industry as firms prepared for Solvency II. However, the fundamental challenge is that validation touches on every activity in the risk modelling process from the earliest preparations making sure all risks are covered, sourcing data to the final reporting and how this is employed in making decisions. It also covers both the technical, quantitative aspects of risk modelling and the more judgmental qualitative aspects. In practical terms, this means an efficient validation process needs to be supported by quality standards and operational controls embedded throughout both the risk modelling process and the wider business. The standards must be fully documented and the efficacy of the controls must be easy to trace by appropriately skilled validation personnel. At the same time, the validation process (and hence team) must be appropriately independent of the risk modelling team and able to challenge them. The need to keep validation independent exacerbates the resourcing implications, with firms often uncertain whether the best tactic is to hire internally or to turn to external consultants. Meanwhile, the more complex and multi-threaded the firm s risk modelling processes, the more time consuming the validation process. Figure 2: Which processes do you lack the time and resources to effectively carry out? We lack the time and resources needed to effectively carry out: Validation Reporting Data cleansing Assumption setting Calculation runs Data extraction and loading Data gathering Disagree Agree Strongly agree

5 Shaping the Future of Insurance Risk Modelling 3 Table 2: Validation creates resource and process challenges Challenge Validation must be independent Consequence The validation process should be independent from the people developing and operating the firm s internal model, leading to key resourcing challenges in smaller firms Validation covers every aspect of modelling Validation teams need wide-ranging skills to check firm-wide risk identification and materiality, data quality, model concept and implementation, modelling process quality, use of results, and more Validation is technically demanding Validation skills may need to embrace statistical quality standards, calibration standards, profit and loss attribution, sensitivity and stability testing, documentation standards, and more Internal models require assessing over time Not only is the validation process ongoing, but firms must assess modelling results against the experience of the firm as time goes on, including accuracy of profit and loss attribution to business units Expert judgment requires validation Expert judgment is pervasive in risk modelling, e.g., assumption and parameter setting, and validation must cover these judgments including how they are documented, their basis and rationale, whether judgments are updated, and other factors Validation tests must be clear Tests should be set out clearly, and their appropriateness and limitations discussed, with the results explained and justified Follow up actions It is not enough just to perform a test: appropriate follow-up actions must be determined and managed. These may include obvious remedial actions associated with a failure of the test and also improvement actions even when a test is passed Validating the use test can be challenging Validation extends far into the business, as firms need to show that modelling for Solvency II also supports relevant decision making within the firm, e.g., governance and business strategy Difficult implementation choices Validation standards are now clearer, but insurers have had to work out how to achieve them through practical decisions including selecting personnel, setting up validation processes, determining and defining ownership, selecting and implementing validation tools, interpreting results, and embedding validation within existing business and modelling processes

6 4 Shaping the Future of Insurance Risk Modelling Engaging the business Validation also requires the co-operation and input of other areas of the business, which may not understand the value of the activity or feel justified spending significant time on it. Yet reaching out into the wider business is important, not least to make sure that modelling results are applied in business decisions under Solvency II s use test. Commenting on the end-to-end risk modelling process, one survey respondent said their approach was fit for purpose but that, validation and reporting of model output needs improving, particularly in terms of efficiency and embedding within business decisions. The practical difficulty in reaching out to the wider business was a common theme, with another respondent admitting, Validation controls need further development, particularly engagement and challenge from business units. While validation must be independent, it also needs to be embedded within the day-to-day business as a regular task and the business must be responsive to the findings of the validation team, e.g., in terms of implementing improvements. Unless businesses engage with the modelling process agreeing its assumptions, supplying robust data, understanding how the output is generated and what it means they will not see modelling results as credible enough to apply to business decisions. This may threaten the firm s ongoing compliance with Solvency II (through the business use test ). 2 Business engagement is vital if firms are to gain advantage from a better use of risk data, e.g., improved company performance. Validation is also often the channel through which senior management and the board engage in questioning the robustness and comprehensiveness of the risk modelling process. Preparing for this scrutiny can be challenging even when the risk modelling process is transparent and wellintegrated and validation tools are appropriately embedded. At the moment, validation is a resource-hungry activity (Box 1). Business engagement is vital if firms are to gain advantages from a better use of risk data BOX 1 THE NEW PINCH POINT IN THE CALCULATION CYCLE: VALIDATION The single most time consuming process in risk modelling is now validation. Teams on average spend 21 percent of their time on this activity (Figures A and B in the Appendix), taking up a significant proportion of the average 20 person day calculation cycle. Reporting (18 percent) and assumption setting (18 percent) also eat up time. Other activities take up less time, including the actual calculation runs (14 percent of team time). Of the three data-related activities, gathering the data and cleansing the data (11 percent and 10 percent, respectively) take more time than extracting the data and loading it into the calculation system (eight percent). Some teams feel data cleansing offers a particular challenge in terms of time and resources. Figure B in the Appendix sets out the typical process waterfall timeline. It is clear that the more qualitative aspects assumption setting and checking, validation and external reporting contribute considerably to the overall time taken up by today s risk modelling processes. Validation and reporting of model output needs improving. Validation controls need further development, particularly engagement and challenge from business units. 2: FOR EXAMPLE, THE U.K. S PRUDENTIAL REGULATION AUTHORITY HAS RECENTLY POINTED OUT WEAKNESSES IN HOW EFFECTIVELY FIRMS ARE DEMONSTRATING THE USE OF RISK MODELLING IN THIS CASE OWN RISK AND SOLVENCY ASSESSMENTS TO INFORM BUSINESS DECISION MAKING. SEE BANK OF ENGLAND, PRUDENTIAL REGULATION AUTHORITY, SOLVENCY II: OWN RISK AND SOLVENCY ASSESSMENT, 15 JUNE 2015.

7 Shaping the Future of Insurance Risk Modelling 5 The wider challenge of resourcing Resourcing concerns do not just centre on validation activities. The survey showed that senior risk professionals think resourcing, particularly hiring the right staff, is the most significant single challenge to their wider risk modelling process (Figure 3a-b). Figure 3a: What percentage ranked each factor as one of their top three risk modelling challenges? Ranked a top 3 modelling challenge Figure 3b: Which factors are significant challenges? The following are significant challenges to our end-to-end risk modelling process: Resources Speed Operational control Reporting to regulators Data quality Transparency Auditability Coping with data Cost Usability of models (internal or vendor) Reporting internally Validation Resources Data quality Operational control Reporting to regulators Coping with data Transparency Speed Auditability Usability of models Cost Reporting internally Error reduction Percent Agreement

8 6 Shaping the Future of Insurance Risk Modelling Resourcing is tough to get right because firms can find it difficult to hire and retain experienced staff with the skills to perform or validate the risk modelling process and, more often than not, to operate and improve the specialist systems used for insurance modelling. Companies often end up with a mix of permanent and contract staff performing these roles. The temporary nature and additional cost of contract staff pose their own challenges. Resourcing problems are hard to solve because they are usually linked to industry-wide shortages of specialist skills. This has been particularly true during the Solvency II implementation process with the pool of skilled resources growing much more slowly than demand. Firms found that skilled staff sometimes left to join other firms with similar needs or to become contractors. This is easing as Solvency II comes into force, but the lesson of the past few years is that it can be difficult for firms to manage access to a potentially small pool of talent able to operate and extend the mosaic of specialist systems employed in insurance risk modelling. The resourcing challenge is fuelling the dissatisfaction some risk professionals feel about the efficiency of their risk modelling processes. Survey responses and qualitative comments made by team leaders show that senior risk professionals understand they need to strike a critical balance between: securing the right human resources; and creating efficiency in the end-to-end risk modelling process The more efficient and flexible the process and the systems that staff employ, the more effectively the existing level of resources can be leveraged and the less likely it is that the firm will find itself stretched to complete critical tasks at critical times. Process improvements today prevent resourcing problems tomorrow. Risk modelling as a firm-wide, end-to-end process Firms are working hard to comply with Solvency II but are often tempted to treat their risk modelling process as a series of individual compliance hurdles rather than as a cascade of interdependent processes. The emphasis has been on meeting a standard in each activity rather than on firm-wide efficiency and coherence. Many teams are obliged to work with multiple legacy systems to conduct modelling tasks, in terms of the modelling platforms themselves or the repositories for policy and claims data. This can further exacerbate the fragmentation of processes. Building a truly robust and efficient risk modelling process means adopting a more firm-wide, end-to-end perspective that recognizes process interdependencies and the need to forge stronger vertical connections (e.g., between models and data) and horizontal connections (e.g., between related modelling activities such as capital modelling and reserving). In turn, an end-to-end perspective supports key process and resourcing efficiencies, for example: 1. Improving operational controls: To keep control of model implementations, a senior professional may be required to authorize any changes to underlying model assumptions. However, even if the authorization is carefully recorded, e.g., as a stored , it will not be immediately visible to others in the business and may prove difficult to locate and verify during audits. Ideally, the authorization would be embedded in an end-toend system that allowed only authorized changes and made authorizations immediately transparent and traceable. 2. One version of the truth: Any financial data employed to calculate risk and capital adequacy, and to create business intelligence, should be consistent with that used for financial reporting, allowing the firm to build one version of the truth in terms of its core data and information. At the moment, in many firms, there are parallel but slightly different versions of the truth circulating simultaneously, depending on the activity under inspection, e.g., risk modelling or general ledger reporting.

9 Shaping the Future of Insurance Risk Modelling 7 3. Leveraging the same data for multiple purposes: In order to build one version of the truth, the firm may need to add metadata on business context to risk data, even though this metadata is not relevant to the immediate job in hand. Further down the line, the metadata will allow the firm to attribute the output of the analysis at the required level (e.g., product level, class of business) for various business intelligence and regulatory reporting purposes. An end-toend perspective allows the firm to plan an information highway, servicing multiple destinations. 4. Information flow and transparency: Risk modelling is a journey with a number of staging posts, including data gathering, cleansing, modelling routines, and the collation of results into reports. Endto-end perspectives help firms to improve the flow and transparency of information between these staging posts, e.g., by reducing the need for inputting and other manual interventions. Ideally, prepared and checked data should flow seamlessly to risk models, with the output from various risk models and other financial information automatically compiled in specific ways to perform particular tasks, such as producing a particular regulatory report. An end-to-end process can be designed to incorporate automated checks, e.g., on data accuracy and completeness, and to radically reduce manual intervention, leading to fewer errors and reduced operational risk. 5. Reporting consistency and accuracy: The goal of adopting a firm-wide end-to-end perspective is not only to make processes more efficient. To manufacture regulatory and business reports, risk modellers often need to pull in financial and other information from non-risk systems such as the general ledger. Making sure that financial information is consistently selected and applied by different teams to various risk modelling tasks is tough outside an endto-end process. End-to-end processes can be designed to select automatically the correct sets of data, and to monitor exceptions. Importantly, end-to-end processes can be designed to support the kind of parallel validation process discussed earlier, driven by both triggers (e.g., process exceptions) and specific points in the process. The independent validation process can then provide disciplined feedback and remedial actions to improve the end-to-end architecture. Figure 4: Common core data and results streaming Data1 Financial statements Data2 Data warehouse Risk modelling Results warehouse Business reports Data3 Regulatory reports Validation

10 8 Shaping the Future of Insurance Risk Modelling The inefficiency of less streamlined approaches mattered less a few years ago. However, the use of risk modelling outputs for an ever-increasing range of regulatory and business intelligence reporting places a large premium on creating core consistencies and efficiencies, e.g., from drawing on one stream of prepared data for multiple tasks (Figure 4). The need to do everything faster also demands further efficiency. The effects of more fragmented approaches can also be seen in qualitative comments made during the survey (Table 3). Many senior risk professionals said they wanted to improve the links between model inputs and outputs, and ease the inefficiencies arising from multiple separate modelling activities. By contrast, today s more fragmented approaches explain why resourcing, speed, operational control, reporting, data quality and transparency were voted the top risk modelling challenges by senior risk professionals in our survey (Figure 3a-b, above). Table 3: Improving the end-to-end process: typical comments Integrating data and modelling Linking risk modelling activities Ideal goals [better] linkage between inputs and outputs software tools need to be embedded into the overall processes Less time on the how, more on the why more integration more linkage between the tools [Improved] reporting, e.g., Pillar 3 efficiency and improved controls integration between the three tooling sets work faster and be less clunky easy access to data, consistent data formatting, single portfolio view better end-to-end process coverage balance flexibility with operational control and consistency improved accessibility and transparency spreadsheet orientated which creates many problems improve granularity and speed of analysis by better linking analysis tools to data warehouse transparency of expert judgment to meet regulatory requirements in a sustainable, ongoing manner

11 Shaping the Future of Insurance Risk Modelling 9 Dissatisfaction with current approaches is leading many senior risk professionals to consider fundamental changes to their processes. More than a quarter of survey respondents felt their risk modelling processes needed urgent re-engineering, with just over half saying reengineering is needed within the next three years (Figure 5). Figure 5: How many agree that their risk modelling processes need to be re-engineered? Our risk modelling processes need to be re-engineered Percent Urgently as soon as possible this year Mid-term within the next 2-3 years Longer term within the next five years Agree Strongly agree

12 10 Shaping the Future of Insurance Risk Modelling Multiple platforms, resourcing, and the need for agility The fact that many firms run multiple platforms is exacerbating the resourcing problem identified by senior risk professionals as their top challenge. Each system demands specialist skills, which introduces a form of key man risk to the risk modelling process because only a few people fully understand how to run and adapt each system. In the longer term, some firms may defuse this risk by moving to a single, common platform so that any of their modelling experts can produce results for different business areas and across a range of output, e.g., reserving as well as capital modelling, life as well as non-life. The ability to make changes quickly to a risk modelling system is also important because it helps insurers to be more strategically agile. At the moment, upgrading multiple systems to reflect business, market and regulatory changes can be cumbersome, even impossible. This has the effect of discouraging firms from new market initiatives or obliging them to introduce inefficient workarounds. Across financial industries, professionals understand that systems can liberate businesses or act as shackles that deter change. Running an end-to-end risk modelling process on a single platform also promises a more resilient environment that is easier to maintain over time. Some firms have gone further, outsourcing platform management to the cloud with the aim of improving robustness, durability, capacity at peak times, flexibility and performance the tactic can also remove the need for separate recovery plans. Especially where they are compatible with existing systems, cloud-based solutions with outsourced managed services also present a low-risk alternative to sinking further investments into in-house systems. They can help insurers to trial and incrementally move to a more efficient end-toend process while gaining confidence in specific component solutions on the way. The ultimate goal is to release risk modelling experts from critical but mundane chores so they can spend more time understanding what is driving the results and addressing the implications. Less time on the how, more on the why.

13 Shaping the Future of Insurance Risk Modelling 11 Conclusion: Shaping the future of risk modelling The argument for making risk modelling processes faster, more robust and more flexible is often framed in terms of compliance, cost and efficiency. However, improving firm-wide, end-to-end risk modelling in the ways discussed in this report is also important for strategic business reasons. In the past, businesses have often proved reluctant to apply the information generated by actuarial teams to business management for a host of reasons that include: Speed and responsiveness: Resource-intensive modelling simply takes too long. Completeness: Comprehensive, consolidated views of different risks were too hard to build. Lack of transparency and credibility: Businesses could not drill down into results to see what was driving them and build credibility, e.g., with regard to underlying assumptions. Given the level of investment set in motion by Solvency II, there is now a major opportunity for firms to remove these objections. The business use test has already provided the motivation to begin the task. However, if risk modelling is to reach an escape velocity in terms of its speed and responsiveness to business demands, firms will need to consider some bold next steps (Table 4) as they plan out their process re-engineering. Table 4: Steps towards a stronger future state Adopt a firm-wide, end-to-end perspective to help re-engineer key risk modelling processes Identify and minimize resourcing challenges through process re-engineering Embed operational controls and automatic checks to aid validation Integrate processes within the wider business and risk management function Build single streams of multi-purpose data wherever possible

14 12 Shaping the Future of Insurance Risk Modelling About the survey This paper is based in part on a survey conducted in 2015 by FIS and Grant Thornton of 31 senior general insurance actuaries and risk managers in the U.K. The firms represented included insurance companies of differing sizes and members of the London insurance market. The survey focused on the leaders of the teams responsible for insurance risk modelling, with nearly 90 percent of the respondents involved in capital modelling and about half also responsible for reserving and/or pricing (Figure 6). On average, survey respondents led teams with about five members (Figure 7) though some managed teams with ten members or more. Figure 6: What are your areas of responsibility? Figure 7: How big are risk modelling teams excluding yourself? 0 Capital modelling % 10+ 7% 19% 29% Pricing 3% 7% Reserving 3%

15 Shaping the Future of Insurance Risk Modelling 13 Appendix: How teams spend their time ABOUT THE AUTHORS Figure A: How do teams spend their time during each calculation cycle? Percent 14 Validation Reporting Assumption setting Calculation runs Data gathering Data cleansing Data extraction and loading Neil Covington is director of solutions management in FIS insurance business, and is responsible for leading global strategy for the business general insurance risk management solutions. His background includes more than twenty years of actuarial experience in insurance, working in both insurers and consultancy. This includes the design and development of models, as well as experience using, interpreting and explaining them across the business and to boards. In previous roles, he served as chief actuary and head actuary for multi-line, multi-national businesses. Neil is a qualified U.K. actuary with the Institute and Faculty of Actuaries, FIA, and holds both an MBA and a first class honours degree in mathematics, BSc. Additionally, Neil is a certified Prince 2 project manager. Simon Sheaf leads Grant Thornton s General Insurance Actuarial and Risk practice. He has worked in the general insurance industry for more than 25 years and specialises in providing clients with solutions that are both technically sound and pragmatic. He is a Fellow of both the Institute and Faculty of Actuaries and the Society of Actuaries in Ireland. Simon advises clients on issues including Solvency II preparations, capital modelling, estimation of insurance liabilities, strategic reviews of insurance operations, business planning, reviews of rating adequacy, pricing of portfolios and individual risks, management information systems, Part VII transfers, Section 166 Skilled Person reviews, Schemes of Arrangement, and reinsurance arrangements. Figure B: Calculation cycle process waterfall (with percentage of time spent on each task) Data gathering Data cleansing Data extraction and loading Assumption setting Calculation runs Validation Reporting Percent

16 About Grant Thornton Grant Thornton is one of the world s leading organisations of independent assurance, tax and advisory firms, providing bespoke solutions to the finance sector. We help dynamic organisations unlock their potential for growth by providing meaningful, forward looking advice. With more external issues affecting the financial services sector than ever before, knowing which way to turn is an increasing challenge. Our clients tell us they are confident in our ability to proactively anticipate market activity, we listen to their needs and create fit-for-purpose solutions rather than off-the-shelf services. For more information, please visit twitter.com/grantthorntonuk linkedin.com/company/grant-thornton-uk-llp About FIS FIS is a global leader in financial services technology, with a focus on retail and institutional banking, payments, asset and wealth management, risk and compliance, consulting and outsourcing solutions. Through the depth and breadth of our solutions portfolio, global capabilities and domain expertise, FIS serves more than 20,000 clients in over 130 countries. Headquartered in Jacksonville, Florida, FIS employs more than 55,000 people worldwide and holds leadership positions in payment processing, financial software and banking solutions. Providing software, services and outsourcing of the technology that empowers the financial world, FIS is a Fortune 500 company and is a member of Standard & Poor s 500 Index. For more information about FIS, visit twitter.com/fisglobal getinfo@fisglobal.com linkedin.com/company/fisglobal 2016 FIS FIS and the FIS logo are trademarks or registered trademarks of FIS or its subsidiaries in the U.S. and/or other countries. Other parties marks are the property of their respective owners. 1030

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