analytics has always been at the core of property and casualtyinsurance, with carriers calculating prices and reserves based onrisk characteristics.

But the insurance industry—which pioneered the use ofanalytics—now finds itself lagging behind many otherindustries.

This is a shame because advanced analytics can address numerousopportunities across the insurance value chain, especially incommercial underwriting.

CUSTOMER-DRIVEN CROSS-SELLING TO COMMERCIALCLIENTS

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

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

But commercial insurers today are typically still relying onrule- and grid-based approaches to determine when to offercustomers complementary products—rather than providing theunderwriter with similar, customer-driven insight options toreview.

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

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

IDENTIFYING OUTLIERS

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

It is not unusual, for example, for a midsize to largecommercial risk to include hundreds of locations and propertytypes. With advanced analytics, these properties can be assessednot only to understand the average risk profile for the portfolioof properties, but also to identify specific outliers forremediation.

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

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

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

By providing underwriters with identification of theseproperties or risks to be mitigated, analytics can improve theoverall quality of the risk for the insurer, either withmitigations or through appropriate pricing.  

FLEET SHEETS

We are also seeing the application of advanced analytics forvehicle fleets, which represent another complex commercialunderwriting decision.

While it is impossible to review a long list of vehicles usingmanual 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 riskprofile and the typical pricing for that specific risk.

Like property portfolios, information on vehicle fleets is oftencollected manually and stored on paper—so considerable effort maybe needed to structure such information to yield data usable foranalytics.

Carriers can also draw upon third-party databases forinformation on replacement costs and other data that can befactored into the analytics mix.

DATA ESSENTIALS

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

1. Data quality: Information on properties, vehicle fleets andsimilar, large capital investments must be centralized, cleansedand conformed.
2. Analytics capabilities: The use of analytics needs to beextended beyond the boundaries of actuaries with additionalinvestments in tools, training, processes and people to turn dataanalysis into actionable business insights.
3. Presentation capabilities: Once data is captured, assembled andanalyzed, it must be presented in the right format, in the rightplace and at the right time, to help the underwriter make the rightdecisions in terms of risk and pricing.

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

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