A prime example of extending the use of analytics is in cross-selling.

Internet retailers such as Amazon have developed advanced customer-analysis capabilities capable of presenting users who have made one purchase with more items to consider based on what other, similar customers have purchased.

But commercial insurers today are typically still relying on rule- and grid-based approaches to determine when to offer customers complementary products—rather than providing the underwriter with similar, customer-driven insight options to review.

This type of analysis is especially useful with today's more complex risks that cross traditional boundaries.

For example, would the traditional industry cross-sell model catch the full range of coverage needs for a midrange manufacturer that also sells over the Internet, has periodic warehouse sales and forwards freight overseas? A customer-driven model would.

IDENTIFYING OUTLIERS

Advanced analytics can also play a role in more efficiently focusing underwriter time on the most critical business issues.

It is not unusual, for example, for a midsize to large commercial risk to include hundreds of locations and property types. With advanced analytics, these properties can be assessed not only to understand the average risk profile for the portfolio of properties, but also to identify specific outliers for remediation.

These outliers could be found based on simple rule analysis—such as proximity to a flood zone or a chemical plant—or through more advanced risk analysis such as the degree of risk variance from the average profile of the risk.

A well-constructed digital file can then provide the underwriter with advanced map views and risk information of the properties—with the specific risks pinpointed for consideration.

When this same information is combined with property information from other risks in the company's portfolio, analysis can also be performed to address unusual risk concentrations, such as too many properties near a quake zone. 

By providing underwriters with identification of these properties or risks to be mitigated, analytics can improve the overall quality of the risk for the insurer, either with mitigations or through appropriate pricing.  

FLEET SHEETS

We are also seeing the application of advanced analytics for vehicle fleets, which represent another complex commercial underwriting decision.

While it is impossible to review a long list of vehicles using manual processes, analytics can identify the vehicles (or drivers) that may require special attention—a petroleum truck, for instance, among a fleet of passenger cars—along with that vehicle's risk profile and the typical pricing for that specific risk.

Like property portfolios, information on vehicle fleets is often collected manually and stored on paper—so considerable effort may be needed to structure such information to yield data usable for analytics.

Carriers can also draw upon third-party databases for information on replacement costs and other data that can be factored into the analytics mix.

DATA ESSENTIALS

There are three key elements to successful use of analytics in these circumstances:

1. Data quality: Information on properties, vehicle fleets and similar, large capital investments must be centralized, cleansed and conformed.
2. Analytics capabilities: The use of analytics needs to be extended beyond the boundaries of actuaries with additional investments in tools, training, processes and people to turn data analysis into actionable business insights.
3. Presentation capabilities: Once data is captured, assembled and analyzed, it must be presented in the right format, in the right place and at the right time, to help the underwriter make the right decisions in terms of risk and pricing.

With these elements in place, insurers can put advanced analytics to work in commercial underwriting. Although analytics have gained a strong foothold in personal and small commercial lines, the benefits of using analytics—including improvements in pricing, risk selection, operational efficiency and pricing—can be just as significant for large, commercial lines.

To gain these benefits, however, insurers need to take an end-to-end approach, working through the issues involved from data capture through final presentation of the underwriting case file.