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This article discusses the ways in which insurers can use their analytical and operational data to measure risk and performance, and support their business decision-making processes.

The insurance marketplace is more competitive today than ever, owing to the pressure to reduce operating costs, the impetus to provide better returns to shareholders, the emergence of new distribution channels, and the climate of low investment returns.

Consequently, making the right business decisions is absolutely crucial – decisions that are directly linked to high-quality data.

Equally, insurers have to contend with an ever-increasing regulatory burden, including Solvency II (SII), International Financial Reporting Standards (IFRS), Dodd-Frank, and the Retail Distribution Review (RDR) in the UK, and equivalent distribution legislation across Europe. Compliance with this legislation is challenging, considering the operating environment. While most of the legislation is primarily concerned with governance, much of the practical management is driven by data – particularly Solvency II and IFRS.

The connection between decision-making and regulatory compliance data

Data is the common theme between decisionmaking and regulatory compliance. A significant amount of analytical data is required for Solvency II and IFRS, but much of this data is also relevant for making business decisions. While additional data may be required for decision-making, the key to success is developing a common approach and standards to data management and storage that can meet both regulatory and business needs. A further crossover is that informed business decisions are at the heart of Solvency II – the Own Risk and Solvency Assessment (ORSA) and Use Test processes.

Analytical versus operational data

Analytical data, or risk data, refers to the actuarial, finance, investment, and risk data required for SII/IFRS and multi-faceted management and business reporting. Operational data is the day-to-day information insurers use, such as client, claims, customer relationship management (CRM), and distribution data. Traditionally, insurers are more apt at handling operational data than analytical data. Figure 1 illustrates these main differences.

Risk data is key

As the fundamental business of an insurer is to underwrite and pool risk, strategic business decisions are often based on risk data (or analytical data). Granular data on all these risk factors is required to support the decision-making process and regulatory reporting. Much of this data is analytical in nature, but some operational data is needed, such as policy and claims data for input into an insurer’s actuarial modeling engines.

While much of the regulatory risk data is defined and can effectively be reused for decision-making purposes, new data may be required, particularly for generating risk-adjusted metrics – for instance, risk-adjusted return on capital (RAROC) or return on risk-adjusted capital (RORAC) – and for forward-looking planning (e.g., multi-year projection of balance sheets). This latter aspect is the most problematic and, as well as base data, it may require new models, methodologies, and macroeconomic scenarios to project into the future.

Figure 1. Stress testing focus: assessing shocks and how they transmit
Stress testing focus: assessing shocks and how they transmit
Source: Moody's Analytics

Presentation and structure of information

From a business perspective, a plethora of unstructured data stored in a database is of little value. Value is added when that data is structured and made available to users in a format that is readily understandable – “translated” so that business users and senior management can understand it.

A significant amount of data will already exist (particularly data required for Solvency and IFRS reporting); however, new data sets will often be required to generate the information to support the decision-making process. This requires the business to provide IT with the exact information needed. IT can then ensure that the data is first available and then structure the data into Online Analytical Processing (OLAP) cubes.1

In terms of strategic decision-making, a significant amount of information may be required to meet key questions, such as:

  • How much risk capital will be needed to survive the next five years?
  • What is the most effective and profitable use of the firm’s capital?
  • How can the business grow profitably?
  • What should the firm’s product portfolio look like?
  • What are the scenarios that might put the firm out of business?
  • How would an acquisition impact the firm’s capital requirements?

In order for insurers to remain competitive, they must be able to react quickly to change, which involves instant access to accurate and relevant information. This information is often provided via interactive dashboards that are produced at a set time or when certain events occur.

Storing data – a repository is crucial

An insurer’s data, both analytical and operational, is typically scattered across many systems in a series of non-integrated silos. There are usually no common enterprise data models and standards. Instead, there are disparate data architectures, applications, and methodologies. To overcome this problem, some insurers have already built operational data repositories, but few have built analytical repositories. Figure 2 illustrates how an analytical repository would plug into an insurer’s risk architecture with potential links to an operational repository. Some data from existing operational data repositories is part of the analytical process.

A major benefit of a centralized repository to store data is that risk and capital data and metrics are available to the whole enterprise to access and analyze. This approach also avoids duplicated and unnecessary movement of data.

Data storage – centralized repository

At the heart of the architecture is a centralized repository that works for both operational and analytical data. This section speaks to an analytical repository, yet applies to both operational and integrated repositories. A major benefit of a centralized repository is that risk and capital data and metrics are available to the whole enterprise to access and analyze. This approach also avoids the duplicated and unnecessary movement of data.

A risk and capital literate development team is essential for providing insight into how successful the repository is in supporting risk-based decision-making. Nonetheless, building the repository can be a complex task because it involves numerous detailed steps:

  • Business owners and the users must define the reports, dashboards, and data they require and the drill-down (granularity) capability needed
  • IT can then accordingly build a flexible data model and the structure of the repository
  • IT will have to build the data extraction and transformation processes required prior to loading the data into the repository
  • IT will have to construct the OLAP cubes necessary to support the generation of multidimensional reports and dashboards specified by the business
  • Many insurers already have sophisticated actuarial engines, which may have to be extended or supplemented with new tools to provide the required metrics
Figure 2. An analytical repository plugging into an insurer’s risk architecture
An analytical repository plugging into an insurer’s risk architecture
Source: Moody's Analytics

Capital metrics – ORSA

The ORSA and Use Test put risk and capital-based decision-making at the core of the strategic planning process and need a significant amount of analytical data. Insurers can adopt a minimal compliance approach to the ORSA or adopt a proactive risk and capital culture throughout the business by embedding it within the overall strategic planning and decision-making process. The latter is the most advantageous approach.

Figure 3 highlights the ORSA, which comprises, at the center, three key process layers: risk identification and processes, risk and capital calculations, and management controls and action.

The key outputs of the ORSA process are the metrics that the process generates. Analytical data is critical as it feeds the engines that produce the metrics – data from actuarial systems, risk systems, capital projection engines, and finance systems – which ideally will be stored in a central repository. The metrics also have to be stored at a low level of granularity so that the business can not only view the figures, but also have the capability to drilldown into the underlying data to obtain a better understanding of the metric makeup.

Figure 3. Moody’s Analytics ORSA Framework
Moody’s Analytics ORSA Framework
Source: Moody's Analytics

Investment information

Volatility in the financial markets and historically low yields mean that making the right investment decisions is of paramount importance, but what type of investment information (and hence data) should decision makers and senior management look for? The following provides some areas they could consider:

  • Market risk dashboard: Shows capital adequacy across a range of measures based on current market prices and is typically produced daily or weekly
  • Optimal asset portfolio: Provides information that compares yield and capital allocated to assets. It indicates the best yields, capital, and risk ratios
  • Credit/concentration risk: As insurers seek better yields, they increasingly look to invest in alternative credit assets, such as infrastructure and corporate loans, and credit default swaps (CDS). These provide a better yield than bonds and match the insurer’s long-term liabilities, but more granular credit risk information is required to fully understand the risks and returns

It can be a challenge for insurers to obtain granular asset information from investment managers and internal investment systems so they may evaluate returns and assess how differing investment portfolios impact the level of capital required. Such information, however, is essential to supporting the decision-making and the regulatory reporting processes (e.g., the D1-D6 of the EIOPA quantitative reporting templates).

Product portfolios

Low yields on bonds and the capital requirements of Solvency II have encouraged insurers to switch from insurance products with inherent guarantees, such as with-profit life contracts, toward unit-linked contracts, where all investment risk is effectively transferred to the policyholder. As a consequence, insurers are reassessing their product portfolios with the aim of reducing capital held in relation to products. To support this, however, insurers need a whole raft of actuarial modeling and capital data at their disposal.

Legal entity structure and diversification Solvency II is also driving insurers to closely examine their legal entity structures and how it impacts capital and where capital is held. For instance, some multi-national insurers have restructured their legal entities to different groupings or converted subsidiary companies into branches. To obtain regulatory and capital advantages, others have sought to relocate the group geographically. Clearly tax and fungibility rules come into play, but there is a definite trend toward legal entity simplification. For this to happen, granular analysis of finance, actuarial, asset, and risk data at entity levels are required.

The advantages of seeking diversification benefits are widely endorsed, which has led many insurers to carefully consider what businesses and books of business to acquire or divest. The traditional approach of valuing books of business on an embedded value basis is now influenced by its impact on diversified capital.

Value is added when that data is structured and made available to users in a format that is readily understandable – “translated” so that business users, executives, and the board can understand it. To remain competitive, insurers must also be able to react quickly to change, which means they may need instant access to accurate and relevant information.

What-if analysis

The interaction of investment portfolio, product portfolio, legal entity structure, and the potential effect of diversification benefits are heightening interest in conducting what-if analysis. The scope of any what-if analysis might include:

  • Economic capital/solvency capital requirement projections under multiple scenarios
  • Hedging strategy analysis
  • Changes in product portfolios
  • Strategic asset allocation
  • Acquisitions and sales
  • Changes in entity structures
  • Interim balance sheet valuation

The analysis and performance measures previously discussed require a high volume of analytical data. And, while the data is critical, many insurers will also have to review their actuarial models and develop new ones (e.g., for International Financial Reporting Standard figures). They will also need to look for new tools and techniques, such as economic capital calculators and proxy modeling, particularly for large complex liability portfolios, where full stochastic modeling is required for internal model solvency capital calculations.

The importance of management actions

Generating correct and meaningful information, reports, and dashboards is undoubtedly an important part of the decision-making process, but so too is the willingness of management to take action on the basis of the information provided. In some situations, management actions can be pre-built into certain scenarios, so that in the event of the scenario materializing, a series of pre-planned actions are triggered. In other circumstances, actions will have to be much more reactive.

The quest for better informed decision-making

Complying with regulation such as Solvency II is a major cost to insurers and requires a vast amount of data. That data, however, has tremendous value if it is enhanced and used properly. Deriving benefits from Solvency II programs is a topic on the agenda of most boards. One of those benefits is undoubtedly better decision-making. To support this, insurers need high quality data that is stored in a structured repository to generate the reports, dashboards, and KPIs the business needs. Data is one of the most valuable assets an insurer has – they should make sure they use it to the fullest.


1 Transforming data primarily involves the construction of online analytical processing (OLAP) cubes by IT specialists, which enables databases to be accessed to provide specific views (or sub-sets) of multiple data elements that meet a user’s requirements. This process effectively translates data into information, which can be presented in the format of reports, dashboards, or other forms of graphical output.

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