Back in 2010, I co-authored an article (Best’s Review magazine, Technology Insights column, “Risky Business”, Nov. 2010) advocating that Risk and Performance Management business processes should be brought into the planning, reporting, and analytics processes. Since then, there have been several key developments:
- Big Data has exploded
- Predictive modeling capabilities have advanced
- New powerful database technologies including embedded modeling capabilities have evolved
- Regulatory changes and additional requirements have continued
- Integrated risk and finance management platforms have emerged
The View in 2014
With these developments, risk and finance integration has advanced. Below you will find an update on the key areas and related recommendations cited in the original article:
2010 View | 2014 View |
Addressing increased complexity in financial derivatives, sophisticated “value at risk” modeling, and economic capital allocation as well as updated and increased regulatory guidance. | IFRS4 (Int’l. Financial Reporting Standards) Phase II and European Solvency II evolution continues. U.S. NAIC Solvency Modernization Initiative (SMI) task force continues to review international developments in five key solvency areas: capital requirements, international accounting, insurance valuation, reinsurance, and group regulatory issues. |
Re-using risk and financial calculation models into an integrated risk and performance model | This has not been fully actualized by insurers, but is progressing resulting from insurers’ awareness and software application vendors integrated functionality development. |
Bringing risk measurement and performance management together with enterprise planning, reporting and business intelligence processes. | Most organizations are still focused on past and current performance. Few are leveraging sophisticated predictive models for future planning. |
Improving collection, integration and management of data for risk and financial performance | In memory databases are becoming pervasive; their processing power enables calculations in real-time and simplifies extract, transform, and load (ETL) processes. Business rules are still needed to support data normalization across multiple sources, but virtual ETL eliminates data movement and corruption issues. Most organizations have clung to traditional DW and data management approaches; some are piloting new virtual DWs; few have widely adopted new practices. |
Incorporating risk into budgeting, planning and reporting and into measurements and reporting systems. | Most organizations are incorporating at least operational risk metrics into finance data models and reporting. Increased use of predictive models and modeling language such as R, are being adopted as well. |
Unifying accounting rules, statistical models, and risk measurement and management techniques | Unification has begun but has room for improvement. |
Clearly, risk and finance business process convergence has evolved but so have regulatory changes. New requirements are continuing to challenge insurers to keep up. Fortunately, new capabilities and integrated solutions are emerging to help address these needs which insurers should review. In upgrading in-house solutions or reviewing applications, insurers should look to incorporate these capabilities:
- An integrated platform and architecture for risk and finance
- An integrated risk and finance data model to support data integration, calculations and business rules
- Flexible rules engine for both ongoing accounting rules and risk rules changes
- Predictive modeling environment that is integrated or interoperable
A sound integrated risk and finance platform with the flexibility to expand will enable insurers to meet regulatory and risk requirements with minimized implementation risk, lower cost, and minimal disruption.
The next few years will continue to yield new technologies and solutions providing a competitive advantage for financial institutions and insurers to exploit the combined sensibilities of risk and finance.
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Pat Saporito
Sr. Director, BI - SAP Global Center of Excellence for Analytics
Twitter: @PatSaporito
