Financial and Regulatory Reporting. Five Stages of Evolution The Critical Role of Financial Models

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1 Insights November 2011 Financial and Regulatory Reporting Five Stages of Evolution The Critical Role of Financial Models This is the second in a three-part series examining the five stages of evolution of financial reporting functions at life insurance companies (Figure 1). In this article, we discuss the importance of model design in moving from the technical or production stage to the analysis stage. In the final installment of the series, we will discuss the crucial role of models in driving strategic decision making. As discussed in our fi rst article, insurance regulations and fi nancial reporting requirements are undergoing signifi cant changes, and preparing for these changes will be challenging for most. According to a recent Towers Watson survey of life insurance CFOs ( united-states/research/4717), companies knowledge and preparedness for both International Financial Reporting Standards (IFRS) and U.S. Generally Accepted Accounting Principles (GAAP) reporting and new principle-based regulations vary widely, and generally appear to be insuffi cient. As new insurance fi nancial reporting requirements unfold, even the most sophisticated companies will fi nd themselves at the initial stage of Towers Watson s evolutionary chain as they come to grips with the new standards. However, companies that recognize and plan for continued evolution will achieve it more quickly, to the benefi t of their bottom lines. Figure 1. Five stages of financial and regulatory reporting evolution Clear line of sight from fi nancials to company actions? Stage 1: Technical Stage 2: Controls/ Governance Stage 3: Analysis Stage 4: Forecasting/ Budgeting Stage 5: Driving Decisions Able to produce realistic forecasts under multiple scenarios? Able to explain results and why they differ from expectations? Is the process controlled and auditable? Able to produce results correctly?

2 Stage One: Technical The technical stage of the fi nancial reporting evolution comes fi rst. The organization must apply appropriate and specifi c methodologies and bases to the relevant business, with a focus on producing the required information and fi nancial statements correctly. The technical stage requires: Knowledge of the required information Knowledge of the appropriate guidance/standards Ability to calculate the required amounts Companies must commit the time and resources necessary to acquire the knowledge of new standards at a time when many fi nancial departments are already understaffed and burdened with multiple reporting requirements. It is important not to underestimate the ramp-up time needed to prepare for these changes. Effective dates for new regulations often seem to be in the distant future and then suddenly arrive before many are ready. Insurers that wait too long to implement will be at a serious disadvantage. What s more, companies that don t allow ample time for testing will have diffi culty understanding how to interpret results. The calculation of the required amounts will almost certainly require the use of actuarial models, typically very complex sets of variables and functions designed to represent some segment of the insurance company. The models may exist within an actuarial modeling system (e.g., MoSes), or in some other system (e.g., Microsoft Excel ). For enterprises at this stage, the primary goal typically is to ensure the technical accuracy of the model calculation engine. As a result, most models at this stage tend to be very specifi c and transparent. For example, many models in this stage are developed in open development systems (such as Microsoft Excel), which allow for a high level of transparency, customization and accessibility. As the new standards emerge, many companies may fi nd that their current models are not adequate for one or more reasons, such as: The model calculation engine is unable to perform the revised calculation. The model outputs are not suffi cient to meet the reporting requirements. The model run time is not practical. Often, in the short term, the reporting function will implement a series of manual workarounds to perform the minimum required to meet reporting deadlines. While this may get the job done, it is diffi cult to move beyond this stage without signifi cant improvement in the models underlying the process. Moving to Stage Two: Governance/Controls Gaining confi dence in the reporting results is the next important stage. At this point, controls and protocols are put in place to validate the inputs going into the model, the calculations performed by the model and the results coming out of the technical phase. Essentially, the focus shifts from output to process. The ability to simplify processes and have greater control over the assumptions, data and the quality of that data depends on having strong, robust models in place. It is easier to maintain a streamlined end-to-end process with fewer control points than it is to manage complex processes with multiple points of contact where errors can be introduced into the calculations and results. Comprehensive, integrated models lead to a more linear process containing fewer touch points. Often, in the short term, the reporting function will implement a series of manual workarounds to perform the minimum required to meet reporting deadlines. While this may get the job done, it is difficult to move beyond this stage without significant improvement in the models underlying the process. 2 towerswatson.com

3 Companies operating under a standard process (such as the one shown in Figure 2) face multiple challenges: Data integrity. The use of multiple data systems will require validation that the data elements are consistent across systems. In one example we encountered, fund values were different across systems due to differences in the timing of data uploads. Multiple modeling platforms. Using multiple platforms increases the need for controls, as data input formats may not be applicable across multiple systems. This can signifi cantly complicate the data translation processes (e.g., scenario fi les), and each process may need unique controls to confi rm data accuracy. In addition, users may need to be crosstrained on multiple systems and defi ne product features more than once, or the company may suffer from signifi cant key-person risk. Consolidated model functionality will reduce the amount of effort spent on maintenance. Multiple models for multiple purposes. This often leads to the use of inconsistent assumptions. There should be only one set of best-estimate assumptions used for pricing, valuation and forecasting. A streamlined process enhances the ability to govern the implementation of the assumptions set by the organization. Multiple data transfers. This creates a logistical challenge: As errors are discovered in the process, the updates may not be pushed through the system completely. For example, an error in Model 1 may be identifi ed, but it may not be apparent that Model 2 is affected. Figure 2. Alternative production processes Production Process Inefficient General Ledger Multiple Locations and Formats Multiple Locations and Formats Multiple Locations and Formats Engine #1 Engine #2 Engine #3 Multiple Output Locations and Format Multiple Output Locations and Format Multiple Output Locations and Format Management Reports Financial Statements Manual Adjustments Engines Manual Output Adjustments Production Process Streamlined General Ledger Single Warehouse and Format Engine #1 Engine #2 Engine #3 Single Output Warehouse and Format Management Reports Financial Statements Engines Controls and Governance Reporting and Process Improvement 3 towerswatson.com

4 These challenges combine to make it diffi cult to have signifi cant confi dence in standard process results without a major investment in internal audit/review. Moving to a streamlined process allows for greater effi ciency, both in initial production and in review. This is a clear benefi t, but it is only possible if the modeling tools are fl exible enough to allow for the complexity of all the required calculations and powerful enough to handle the volume of calculation intensity required. Of course, using a single-platform solution may not be possible or even optimal in all cases. A best of breed approach may be effi cient if the process minimizes manual intervention, and ensures inputs come from a common secure location and results can be funneled to a common destination. The implementation of a streamlined process requires planning and coordination with IT. Data management and warehousing are critical components. The IT considerations (including database design, disaster recovery and support services) must be incorporated into the fi nancial reporting planning exercise in order to ensure an acceptable outcome. Moving to Stage Three: Analysis and Analytics What Does It All Mean? Once a company can produce the required fi nancials effi ciently and reliably, it is ready to move to the next stage analysis. At this stage, actuaries can create the tools that explain the fi nancial results to the CFO as well as to investors, shareholders and analysts, providing insights as to how and why the company s results met or did not meet expectations. Stage One companies spend too much time producing fi nancial reports to provide a robust and material analysis. Highly developed models solve this problem in four ways: Shortening production and adding more time to analyze results Building in appropriate granularity to better analyze results and answer questions without rerunning computationally intensive projections Preparing management reports more quickly Limiting manual intervention by automating how data populate and output leaves the model to populate reports, reducing the risk of errors Run time is often a signifi cant constraint to robust analysis. More complex reporting requirements will make required projections more run-intensive, creating a need for greater scale and capacity to accommodate large numbers of model points and scenarios. Companies may need to run thousands of scenarios, and IT involvement will be more important. The high demand for distributed processing and memory capacity will generally require a major investment in IT that will further integrate valuation and analysis in production. Working with IT on data management and data fl ow can also impact the model calculation engine design, and anticipating desired analytics will create the appropriate level of granularity. The potential for new requirements is also a strong argument for a model that is fl exible, transparent and adaptable. Incorporating analytics into the model allows for consistency and reliability in analysis. While commonly used, spreadsheet analytics are susceptible to the same limitations as spreadsheet models: a lack of material control functionality. Knowing what is important and how to present the key drivers of the results is important for effective analysis. Too often, valuable time is lost on producing and analyzing ineffective or redundant reports. A company should be diligent in monitoring the inventory and usefulness of its reports and analytics. The perceived credibility of the fi nance department and the entire organization is highly correlated to the ability to explain the fi nancial results in a coherent and timely manner. The CFO must be able to distinguish accounting results from the economic performance of the business. Producing accurate, meaningful analytics and reports will greatly enhance the ability to communicate an effective message. The perceived credibility of the finance department and the entire organization is highly correlated to the ability to explain the financial results in a coherent and timely manner. 4 towerswatson.com

5 Summary The expected changes in fi nancial and regulatory requirements for IFRS and the implementation of principle-based approaches for determining reserves and capital are presenting resource challenges for insurance company fi nance departments. While CFOs at insurance companies indicate they will be ready for the new reporting requirements when they become effective, we believe they underestimate the challenges that lie before them. Emerging accounting rules and regulatory requirements will place greater demands on the organization at every stage on the evolutionary chain: At the basic, technical level, new methodologies will need to be interpreted and implemented, and thousands of scenarios will replace the current handful, placing stress on personnel, systems and IT. At the stage of governance/controls, stronger controls and documentation validating data input, assumptions, model calculations and output will need to be embedded into multiple phases of the production environment. At the analysis and analytics stage, the fi nancial function must produce the summary high-level/ dashboard analysis to aid in decision making by management as well as the more granular data necessary for audits and detailed analysis. New, robust actuarial models will be needed to support the fi nancial function at all stages of the evolutionary scale. Many older and weaker models cannot function without manual interventions and workarounds, placing burdens on personnel and systems, and raising troublesome questions around the issue of governance and controls. More effi cient actuarial models will allow the company s fi nancial reporting function to: Maintain a single, streamlined, end-to-end process with fewer touch points, eliminating workarounds and manual interventions, and allowing the organization to effi ciently produce the necessary calculations Validate model inputs and the results coming out of the technical phase by embedding controls at numerous checkpoints in the more streamlined process and producing documentation of the controls, building confi dence in the accuracy of the results Produce meaningful, more detailed analytics and benchmarks that enable management, investors and analysts to have a clearer understanding of the numbers and the story behind those numbers The fi rst three stages in the evolutionary scale provide the foundation for the last two stages, which are to provide projections and forecasts, and enable strategic decision making by management. The next article in this series will examine what the CFO must do to reach those stages. 5 towerswatson.com

6 About the Authors John Dieck is a senior consultant with Towers Watson, and a leading fi nancial and regulatory reporting expert in the U.S. He has advised life insurance companies on many aspects of statutory, U.S. GAAP and IFRS reporting during his 20-plus years in the industry. For more information, contact John Dieck: john.dieck@towerswatson.com Steve Verhagen is a senior consultant with Towers Watson, specializing in life insurance fi nancial modeling and actuarial process optimization. He focuses on the management and oversight of process transformations, helping life insurance companies develop more effective fi nancial reporting processes and more effi cient actuarial models. For more information, contact Steve Verhagen: steve.verhagen@towerswatson.com About Towers Watson Towers Watson is a leading global professional services company that helps organizations improve performance through effective people, risk and financial management. With 14,000 associates around the world, we offer solutions in the areas of employee benefits, talent management, rewards, and risk and capital management. Copyright 2011 Towers Watson. All rights reserved. TW-NA towerswatson.com