With increased access to sophisticated data analyticstrategies, there is growing opportunity to jump into thecommercial auto insurance business with soundrisk analysis and decision-making.

Commercial auto has long been a troubled line of business forproperty and casualty insurers.

Since it suffers from historically poor performance, manyinsurers refrain from writing certain businesses in the lineentirely, which eliminates a potential revenue stream and plaguesthose trying to create a profitable commercial auto portfolio.

Even though insurers would like to have access to driver and telematics data, theyrecognize many businesses and related service providers areunwilling to turn over telematics information. And even if they couldaccess such information, the data may be incomplete because of highdriver turnover.

It also can be hard to sell top management on the purchase orinvestment of data sources.

Commercial auto insurance obstacles

Additionally, as insurance companies venture into usage-based commercial auto insurance for thefirst time, they are trying to make inroads in understanding theexisting data structures. They are trying to scale up acrossvarious distinctions of commercial auto business.

They are looking for quick success to showcase the skill andbuild a case for future investment. This contributes to thereluctance to write commercial auto insurance.

With ever changing access to external data and sophisticateddata analytic strategies, however, there is growing opportunity tojump into the business with sound risk analysis anddecision-making.

What are the alternatives?

In situations where the behavioral data of the drivers in notavailable, it is possible to use vehicles as proxy for drivers toderive a manual premium value. This approach involves applicationof ISO document rules on the basis of vehicle class code andterritory information to create on level premium.

This solution is feasible, and works, as it has been cited inmany rate-making papers.

For example, geography is considered one of the primary drivers of claims experience.Consequently, it is one of the more established and widely usedrating variables.

Companies typically define territories as a collection of smallgeographic units (e.g., postal/zip codes, counties, census blocks)and have premium rate relativities for each territory.

The territorial boundaries and associated rate relativities canvary significantly from insurer to insurer. Therefore, thevehicle's geography can be used as driver's proxy. This informationcan be used to do loss ratio modeling and have more informedunderwriting and pricing techniques instead of focusing on averagecosts and expected number of claims that will be received during agiven time (frequency severity modeling).

ISO data methodology

As you look to adjust premiums to current rate level(extension of exposures is a requirement), the ISO document methodconsiders the following factors mentioned in the ISO manuals basedon class code characteristics of a vehicle:

Rating territory

Insurers or rating agencies often divide states intogeographical subdivisions called rating territories.

Fleet vs. non-fleet

Under a commercial auto policy, vehicles are subject to eitherfleet or non-fleet rates.

Fleet rates are typically lower than non-fleet rates. A fleetnormally consists of five or more self-propelled autos (such astrucks or tractors). A trailer is not self-propelled, so trailersare not included in the vehicle count.

Size class

Under the ISO classification system, trucks are categorized intosize classes based on their gross vehicle weight (GVW). The GVW isdetermined by the manufacturer. It is the weight of the truck whenloaded to its capacity with people and cargo.

Radius class

Another factor that affects commercial auto premiums is theradius of use. This is the distance a vehicle normally travels eachday from the place where it is stored. The distance is calculatedusing a straight line from the origin to the destination. Acommercial vehicle is assigned to one of the following classes:

• Local Up to 50 miles
• Intermediate 51 to 200 miles
• Long Distance More than 200 miles

Physical damage

The premium charged for physical damage coverage on a commercialvehicle is based on the vehicle's age and its current cost.

Private passenger types

The rating of private passenger vehicles is much simpler thanthat of trucks. The liability premium for a car is typically basedon a flat charge. This charge varies depending on the ratingterritory.

All the variables above along with additional variables willgives us a combination of factors which can be used to calculatethe on level premium of the commercial fleet. Typically, fora commercial vehicle this can be represented by a simplemathematical equation

On Level Premium= Factor 1* Factor2 * Factor 3

Where, Factor 1, Factor 2, Factor 3 are combination of varioushidden ISO factors received from Verisk.

Benefits of using ISO data methodology

Certainly, it is advantage to have actual driver data, but thefact remains it is possible to complete an on level premiumcalculation of commercial auto insurance. In addition, thiscalculation has other benefits including:

1. Massive cost saving; gone is the need to buy telematics data and driver behavior data;
2. Repeatable process, which is easier to implement acrossdifferent tools like R and SAS, and its usage is not confined toBig commercial insurance players;
3. Data knowledge gains;
4. Due to this on level premium, one can get extra variables forexample additional information on nature of vehicles for a policylike age of vehicles, etc.

Limitations of this approach

There are also limitations to be aware of as you look atemploying the ISO data methodology. These include:

1. This is not applicable to an entire line of business.
2. May not the most reliable as many policies are defined bydriver characteristics and not by vehicle characteristics.
3. Incomplete vehicle data. Vehicles also keep changing for apolicy, so the calculation needs to be fixed for a time period.This has a great impact for non-current year policies, itrepresents data at a point in time (mid-term/ end term ofpolicy).
4. Absence of certain variables during methodology.

The use of data analytics is both necessary and inevitable forimplementing successful lines of commercial auto insurance.

Even with stated limitations, the ISO data methodologydemonstrates how insurers can set premiums in the absence ofcritical data such as drivers and telematics data while showing acost advantage as it avoids buying behavioral data.

Dheeraj Pandey is assistant vice president at EXL Analytics,a provider of data analytics solutions to financial organizationsincluding P&C Insurance firms. To reach this contributor, sendemail to [email protected].

See also:

8 ways telematics will shape insuranceagencies

A look at the evolution of insurancetelematics

5 reasons to embrace telematics for the connectedcar