What does it take to model risk?

One of my colleagues, Burchard Hillmann-Koster, recently published a white paper that details what insurance companies in Europe must do to comply with the new Solvency II directive.

By setting minimal capital requirements, the EU wants to provide consumers with an adequate level of protection. These capital requirements could total hundreds of millions of dollars or more, so the ability to accurately measure assets and calculate risks can have a significant impact. Solvency II allows insurers to develop and certify their own internal model to calculate the solvency capital requirements—but the effectiveness of these internal models cannot be guaranteed without easy access to high quality, historical and predictive data.

In some ways, the principles behind Solvency II are similar to the Basel II regulations in the banking industry.  In fact, any industry where portfolio and risk management come into play must deal with the same issues and challenges on a day-to-day basis.  So even if you are not in the insurance industry, you may find value in the discoveries and best practices identified in this paper, Five Steps Toward Solvency II and Beyond.

Here are a few samplings from this informative read: 

One: start with high-quality data

Many organizations are not satisfied with their data quality, citing incorrect information, missing or misfield data, duplicated records and inconsistent standards that lead to significant costs, delays and an incomplete understanding of the truth. Actuaries and compliance groups responsible for doing the necessary calculations will need accurate data, which can be delivered though data audits, data cleansing and validation, data matching and consolidation, and data integration.

Two: geocode with confidence

When it comes to assessing risk, location is everything. Geocoding turns addresses into geographical coordinates that can be measured, compared, accumulated and analyzed using location-based analysis. Tools should offer the ability to cleanse, parse, standardize and validate addresses before determining location, which adds confidence to the process. Be wary of “false positives” that can increase the risk for poor decisions.

Three: make the necessary connections through predictive analytics

Combine geocoding with the ability to spatially enrich the data, perform analysis, calculations and predictive analytics. If you can verify that an insured is not in a high-risk area such as a flood zone or hurricane path, your aggregate risk will be lower – as will your solvency capital requirements. 

Four: find ways to integrate multiple functions

Maintaining one platform reduces cost of ownership and can speed up system integration. A single interface also simplifies training and education, and makes it easier for your company to gain the skills and capabilities needed to achieve a competitive advantage.

Five:  add value beyond Solvency II

While there is a place for point-level solutions, organizations may be better served by building and enhancing their overall capabilities in data integration, data quality, geocoding and spatial analysis. These core capabilities can help you reduce solvency capital requirements—but they also add value across your entire operation.

Obviously, the white paper goes into much more depth on these topics.  If risk management is one of your concerns, take a moment to learn more and download Five Steps Toward Solvency II and Beyond.