Using Analytical Data for Business Decision-Making in Insurance

There is, among many insurers, a feeling of regulatory “burnout” and disillusionment. For the last few years, insurers around the globe have been heavily focused on implementing regulatory initiatives, in particular Solvency II (and equivalent regimes), while also responding to the implications of International Financial Reporting Standard 4 (IFRS 4) and regulatory stress testing.

Hundreds of millions of pounds and euros have been spent on Solvency II projects that are now nearing completion, but insurers need to do more work to realize the potential business benefits of these investments.

This continued regulatory burden, combined with a low interest rate/low inflation environment, is making generating value increasingly challenging for insurance companies. Margins are under pressure and firms have to work much harder to remain competitive and deliver returns to shareholders and/or policyholders. Firms must look for new opportunities to support growth, including designing products that are aligned with this new world and adopting alternative investment strategies to generate higher returns and manage costs.

Our discussions with a number of insurance CROs, CFOs, and CEOs over the last six months or so indicate that they have the immediate regulatory situation under some degree of control. Therefore, their focus is turning to running their businesses more effectively and making informed risk-based decisions.

Business decision-making fundamentally revolves around three high-level measures: profitability, capital, and growth. These three factors need to account for the entire risk profile of the business.

There are two aspects to assessing the interaction of these high-level measures: understanding the historical (e.g., year-to-date) performance of the firm tracked against their strategic business plan, and modeling the interaction of these measures over future time horizons under differing stressed scenarios.

Figure 1 illustrates the type of information to which we believe C-suite executives need to have access. Both historical and forward-looking perspectives are critical for effective risk-based decision-making.

Figure 1. Information needed by the C-suite

Source: Moody's Analytics

Equally important is having the available information much more quickly – ideally in real time – and in a format that is readily understandable. This in essence translates to a series of interactive executive dashboards with drill-down and “what-if” capabilities that display the requisite analytical information.

On the face of things, creating these interactive dashboards seems relatively straightforward. In reality, however, insurers must anticipate multiple challenges, both in terms of data and organization.

Although insurers have a good understanding of the type of historical information they need, the requirements tend to be siloed within functional areas (e.g., finance or risk). Creating a common vision across the entire business is not difficult at the highest level, but it becomes more challenging when trying to define the exact requirements across numerous stakeholders. Even when the requirements are well understood, the right solution is needed to deliver the business benefits in a cost-effective manner.

Invariably, it will be ownership and implementation of this vision that is the most difficult part. Adopting a top-down pragmatic approach helps firms focus on what is most important and avoid a “boil the ocean” scenario in which considerable time and effort are spent trying to resolve all the granular issues without much visible business benefit.

While much of the historic analytical information (financial, risk, capital, or investment information) required for decision-making may already exist within an organization, it is usually fragmented across multiple systems and a plethora of spreadsheets. This information has to be collated – often using manual processes and even more spreadsheets – a procedure that has to be repeated each time information is required, whether for regulatory or business purposes such as board meetings. This makes producing the necessary information and metrics a difficult and time-consuming job.

Consequently, the first challenge is extracting, transforming, aggregating, and storing all of the information required in a logical and structured manner and making it easily available to the enterprise. Many insurers have existing operational datamarts or warehouses, but these are typically based on legacy systems and are not necessarily suitable for storing the type of analytical data needed at the required levels of granularity.

A second problem relates to consistency across different data sources. If data sources for a particular use (e.g., assets, profitability, etc.) are inconsistent, there may be questions about the underlying data quality, which can undermine senior management’s confidence in the provided Management Information.

Historical information is the most readily available to any organization. While it is important for monitoring the progress against business metrics and targets (or for regulatory purposes), historical information is limited in terms of its strategic planning and decision-making capabilities.

Forward-looking projections by scenario and their corresponding “what-if” analyses are an important part of the C-suite toolkit. Regulators, under the guise of processes such as ORSA, are also increasingly using them. Figure 2 illustrates an insurer’s solvency ratio projected over a five-year time horizon based on a baseline scenario (most likely) and four alternative scenarios. However, forward-looking projections also present a considerable challenge.

First, projecting an insurer’s balance sheet is not always as straightforward as it sounds, particularly for complex organizations or those with complex assets and/or liabilities (e.g., path-dependent liabilities, as is common for life insurers).

Second, a key part of risk-based decision-making is the ability to measure return on capital. This means that firms need to be able to make projections across multiple regimes, such as solvency regimes for capital and accounting regimes (e.g., IFRS) for profitability.

Figure 2. ORSA results

Source: Moody's Analytics

A final challenge is the length of time it takes to generate the relevant metrics, particularly for risk-based decision-making. Running what-if analyses can be a time-consuming process, especially if actuarial models are involved. Having to wait days or weeks for this information does not support a dynamic decision-making process. The lack of accurate and timely information often means that decisions are driven by gut feeling rather than sound analysis.

We believe that an Insurance Analytics Platform can be used to solve many of these challenges and provide the foundation for the management of risk-based performance metrics to support better decision-making.

Figure 3 illustrates the conceptual architecture of what an IAP might look like for an insurer.

Figure 3. IAP conceptual architecture

Source: Moody's Analytics

The central core to the IAP is a dedicated insurance analytical data repository that stores the relevant asset, actuarial, finance, and risk data (analytical data) and model run results, enabling the generation of a range of executive reports and interactive dashboards.

The repository acts as the common “clearing house” for the analytical data across the enterprise. The underlying data model can be designed to support the storage of both historical and forward-looking analytical data for the purpose of providing drill-down analysis of risk-based performance metrics. The “clearing house” concept ensures that there is consistency of data for the analytics, while also ensuring complete transparency/audit trails from the source data.

The “raw” data then has to be extracted, transformed, and loaded from multiple data sources (1 and 6 in Figure 3) before quality and validation checks can be undertaken in the repository. Most insurers have an existing extract, transform, and load tool (2) for this purpose. Importantly, the repository varies from a typical insurance database in a number of ways primarily in terms of the level of granularity and ability to store complex results such a cash flows

Generating the physical reports and dashboards requires a reporting engine (4) capable of querying the repository, organizing the data logically, and rendering the output in the selected format. This is typically facilitated by what are termed On Line Analytical Programming cubes, which are multi-dimensional views of data held in the repository. They enable high levels of granularity and provide drill-through paths.

Outputs can be generated in a variety of formats, typically reports, spreadsheets, and dashboards. From the perspective of decision-makers, interactive dashboards are particularly valuable. Such dashboards should focus on the analytical information/metrics that are used to manage the business and make decisions.

• Provide drill-down analyses from the high-level business metrics, offering different levels of aggregation, drill-through, and granularity.
• Generate tailored views of the analytical management information dependent on the stakeholder (CEO, CFO, CRO, etc.) or functional area needs.
• Provide a “what-if” interface to enable comparison of the different (pre-run) scenarios against each other or the base scenario (e.g., compare the impact of an extra 5% new business growth on profitability and solvency).
• Present both point-in-time and forward-looking analytical management information.

Insurance companies are complex entities, necessitating a way to easily consolidate all the data from various sources. Thus, a key component of the IAP is a consolidation engine. In essence, a consolidation engine provides a mechanism for mapping the underlying analytical data onto the organizational structure of the business. The engine consolidates the granular results to present an enterprise view. This aligns the data model to the business and supports effective drill-down analysis.

As we have already alluded to, one of the most difficult challenges is projecting forward key metrics and analytical information for strategic purposes. Most firms have some forward-looking capability, especially to meet the needs of ORSA under Solvency II. The main problem is that most insurers have not invested in the end-to-end infrastructure to support the efficient production of multi-year results across a range of scenarios.

Given what we have seen in the banking sector with multi-year stress testing, we expect this will be an area that insurance companies will increasingly look at in the coming years, which would naturally integrate with the IAP. Even where there is still a heavy reliance on existing capabilities with use of spreadsheets and manual processes, these capabilities can be integrated into the IAP and also used to help support better data management.

We believe that it should be possible to run a pre-defined set of scenarios and store the results in an analytical repository. This means that within defined parameters the CRO/CFO/CEOs would have “real-time” access to a range of results via interactive dashboards. If the information required were to be beyond the scope of the pre-defined scenarios, the models would have to be re-run.

More generally, we believe that insurers will use proxy techniques to enable scenarios to be run more quickly without relying on individual business units to produce results. The benefits gained through speed, accessibility, and centralization can easily offset a reduction in accuracy, provided the results are “good enough.” Creating a forward-looking projection is a complex process and a detailed analysis is beyond the scope of this article.

There is little doubt that insurers exist in an increasingly competitive and attritive environment. The ability to quickly make informed business decisions based on accurate historic and forward-looking information is crucial, but that information is difficult to collate as it is spread across a plethora of systems.

To meet the information challenge, firms need to have a clear vision of the enterprise metrics required to support their business, and adopt a top-down approach to ensure appropriate focus on the delivery of business benefits.

An IAP can help firms implement their vision. The end capability should be a flexible dashboard that focuses on key business metrics, can be tailored to address the needs of different stakeholders within the organization, and provides drill-down analysis. The analytical data repository can leverage the important source data via a robust data model designed to support the dashboard’s capabilities.

Projecting balance sheets, capital, and profits by scenario in a timely manner to support the forward-looking metrics requires significant investment. However, the IAP can produce outputs from the manual processes that are currently in place at most organizations. As these processes become more streamlined, the platform must be flexible enough to cope with the changes.