Meeting the Regulatory and Reporting Demands for Big Data

Volker Mohr is director of solution management for SAP AG's Insurance Business Unit.

New and developing regulations are bringing fresh challenges to the insurance industry. The new rules are impacting management of risk, capital, and data; accounting; and information technology. One of the most closely watched regulatory projects is Solvency II, a European Union initiative with implications for U.S.-based insurers.

Solvency II is designed to ensure the financial soundness of insurance companies with the goal of protecting policyholders and promoting fair and stable markets. Solvency II uses an economic risk-based approach for assessing capital adequacy.

Solvency II affects U.S.-based insurance companies that are subsidiaries of a parent company located in the EU, as well as U.S. companies that do business in Europe. Beyond its effect on individual companies, Solvency II is also expected to influence any upcoming U.S. insurance regulation. Further, across the industry globally, the fundamental changes and additional disclosures required by Solvency II will create competitive pressures to implement more sophisticated risk and capital management systems.

Similar to Basel II, which defined capital requirements for banks, Solvency II has three pillars. Pillar one specifies quantitative requirements for solvency capital. Pillar two sets out qualitative requirements, including an Own Risk and Solvency Assessment (ORSA). The third pillar establishes regulatory reporting and public disclosure mandates.

Right now, there is uncertainty regarding the final implementation date and several complex policy issues are still unresolved. Recent discussions among EU officials and regulators, however, indicate that they are likely to agree to a phased implementation approach that will begin with qualitative measures.

Raising the bar for data management

Despite the lack of a clear timeline and unresolved issues, companies have begun working on Solvency II. The initiative raises the bar for data management. The quality of complex risk calculations is dependent on the input data, necessitating a strong focus on processes and procedures to ensure that the data is appropriate, complete, and accurate.

Data quality is just one of several technology challenges, however. Calculations require detailed data from a variety of different source systems. Reporting requirements are also becoming more granular and frequent. Overall, the ability to efficiently and accurately process and analyze big data will become more critical over time with a general trend toward real-time reporting that can only be achieved with new technology such as in-memory data management

If data quality and processes are lacking, organizations run the risk of putting too much capital aside, negatively impacting their profitability, or facing consequences for having inadequate reserves.

One challenge to meeting Solvency II mandates is that current insurance IT landscapes are fragmented and unnecessarily costly. Many are characterized by inconsistencies in data definitions, processes and technology. There is plenty of opportunity for errors and inefficiencies, including data quality issues stemming from external data providers, multiple systems in diverse technology platforms, complexity of data transfer process, and process failures, among others.

In developing their systems, insurers and reinsurers should be guided by data quality requirements from the European Insurance and Occupational Pensions Authority (EIOPA), one of the regulatory bodies involved with Solvency II. Among other tenets, EIOPA mandates that companies embed a system of data quality management across the entity. They must also define and monitor processes for identification, collection, transmission, processing, and retention of data.

Other requirements focus on transparency and monitoring. For example, companies must perform periodic data quality assessments and implement a process for identifying and resolving data deficiencies. They must also document instances where data quality may be compromised, including implications and mitigating actions. The audit trail must be clear and transparent.

The uncertainty about both the implementation date and ongoing changes to the legislation means that any quality and process requirements will be even more costly and complex than they would be otherwise.

Architecture to address Solvency II

A simplified architecture to address Solvency II and other analytical topics will generally include the following layers:

• Source systems that create the transactional data needed for further valuation, risk calculation, and regulatory reporting purposes. This is where the foundation for data quality, and therefore quality of solvency calculations, begins.
• Source data layer that, following an extract, transform and load (ETL) process, contains all data needed for Solvency II calculations and reporting in the upstream process.
• Central calculation layer where all calculation tasks and valuations are orchestrated in order to create key information such as SCR/MCR (Solvency Capital Requirement/Minimum Capital Requirement).
• Result data layer that stores key figures, accounting and solvency information needed to create the economic balance sheet, external and internal reports.
• Consolidation layer, where consolidations for the group reporting are performed.
• Reporting layer that provides capabilities to create regulatory reports and visualize data from the source and result data layers for internal and external analysis.

Solvency II compliance is fully dependent on each of these layers and the data flow being designed properly. Meeting these demands is a complex undertaking that touches every corner of an insurer's IT architecture. It is particularly difficult when timelines and requirements are moving targets.

While implementation is a major effort, some insurance companies are also seeing an upside in that Solvency II creates an opportunity to rethink data management in general and to implement a comprehensive framework for companies' data that can be used beyond the regulatory necessity.

Solvency II does not only have an impact on analytical, risk, and financial application. The advantages of a streamlined core insurance environment that supports configurable components such as policy management, claims management, reinsurance management, billing, and financial asset management become even more apparent in light of overlapping regulatory initiatives. A framework that supports Solvency II requirements can be the starting point for creating standardized data models, processes, technology and interfaces also on the core insurance side that can be used to quickly and efficiently meet other current and future regulations.

The same is true for the other layers in the overall architecture. The lower the number and the higher the standardization of systems, technologies, data definition and processes, the easier it will be to adapt to ongoing regulatory changes and any new regulations or standards in the future.

By looking at Solvency II in a broader context, companies can take the opportunity to build on the granular data they collect and leverage insights from real-time reporting. They can improve their data management, collect and analyze Big Data in myriad ways, and take advantage of new technology, such as in-memory computing. This will enable them to not only avoid redundant effort, but also gain a strategic advantage over their competitors.