Risk Insider: Ernie Feirer

Predictive Modeling and Small Commercial Risk

By: | March 2, 2018 • 3 min read
Ernie Feirer, CPCU, is Vice President and General Manager, Commercial Insurance, at LexisNexis Risk Solutions, where he is responsible for developing a suite of solutions for the commercial insurance market. He can be reached at [email protected]

The small commercial insurance sector has been relatively slow to adopt predictive modeling despite its proven successes in other segments. Often it is due to a lack of resources. Other times it’s because an insurer doesn’t know how to build an effective model. Or there may be concerns about engaging the organization in the predictive modeling process.


The good news is that there are simple best practices businesses can use to benefit from predictive modeling and reduce risk vulnerability.

Leveraging from the product development life cycle

Creating and using an effective predictive model can be likened to following a four-stage product development lifecycle process: ideation, design and development, implementation, and monitoring. Following this process can help integrate predictive modeling into a workflow to better predict risk and improve business outcomes.

Step 1: Ideation

The starting place is identifying a problem that needs solving and determining whether or not a predictive model can help. The critical first steps are garnering strong executive sponsorship for the effort and defining a committed cross-functional team that can help bring the idea to reality.

Step 2: Design and development

In the small commercial segment, there’s a growing movement to use predictive modeling for risk assessment and pricing through building insurance scores that order risks in terms of loss propensity. Designing and developing such a model is a very iterative process, which begins with data exploration, followed by training and validating the model, and finally ensuring it meets regulatory compliance.

During data exploration, the team members evaluate data sources. There are many third-party data sources to consider. These include commercial credit from the big credit bureaus, or business owner consumer credit for micro businesses. Public records on the business or business owner are also good sources for assessing risk. Additionally, many carriers choose to integrate prior loss or geospatial data into their models.

An insurance score approach can streamline underwriting and improve pricing based upon the risk associated with the account. With proper segmentation, an insurance score assists underwriting automation to potentially decline, refer, or accept business without the intervention of an underwriter.

Step 3: Implementation

Once the model has been designed and proven, it’s time to implement it within the workflow. This stage requires careful planning. Implementation impacts many parts of the organization and requires thoughtful decision-making. Questions to ask include: Will the score only be used for discretionary pricing or will it be incorporated more broadly? Which, if any, underwriting rules and procedures will change?

To ensure the model is successful you need to work with IT to implement the final model, modify the application workflow to use the model’s score, define customer dispute resolution processes if applicable, and deploy stakeholder training.

Step 4: Monitoring

A model is only as good as the results it produces. To make sure your model is working the way you want it to, it’s critical that you track ongoing performance, make any necessary tweaks, and monitor its efficacy.


For example, scores should be tracked both when they are used and when they are overridden. When they are overridden, who overrode the score and why? Allowing for and documenting score overrides provides valuable insight into score limitations and how the score and its implementation should be improved in the future.

You should periodically monitor the efficacy of the model to determine if it’s achieving the desired results. If not, a deep dive into the underlying causes is required. You might need to periodically recalibrate or rebuild your models to ensure their performance. You may also want to incorporate the score into your underwriting dashboard and business intelligence reports.

Putting it all Together

Aligning a predictive modeling integration with the product development lifecycle process is a methodology any carrier can follow.  It enables commercial insurers to realize the full benefits of predictive modeling for small commercial risk assessment and pricing. Keys to success include executive sponsorship, a competent and engaged cross-functional project team, and a four stage life-cycle process of ideation, design and development, implementation, and monitoring to steer the process.

Mathew Stordy, Director of Commercial Insurance for LexisNexis Risk Solutions, also contributed to this article.

More from Risk & Insurance

More from Risk & Insurance

2018 Risk All Stars

Stop Mitigating Risk. Start Conquering It Like These 2018 Risk All Stars

The concept of risk mastery and ownership, as displayed by the 2018 Risk All Stars, includes not simply seeking to control outcomes but taking full responsibility for them.
By: | September 14, 2018 • 3 min read

People talk a lot about how risk managers can get a seat at the table. The discussion implies that the risk manager is an outsider, striving to get the ear or the attention of an insider, the CEO or CFO.


But there are risk managers who go about things in a different way. And the 2018 Risk All Stars are prime examples of that.

These risk managers put in gear their passion, creativity and perseverance to become masters of a situation, pushing aside any notion that they are anything other than key players.

Goodyear’s Craig Melnick had only been with the global tire maker a few months when Hurricane Harvey dumped a record amount of rainfall on Houston.

Brilliant communication between Melnick and his new teammates gave him timely and valuable updates on the condition of manufacturing locations. Melnick remained in Akron, mastering the situation by moving inventory out of the storm’s path and making sure remediation crews were lined up ahead of time to give Goodyear its best leg up once the storm passed and the flood waters receded.

Goodyear’s resiliency in the face of the storm gave it credibility when it went to the insurance markets later that year for renewals. And here is where we hear a key phrase, produced by Kevin Garvey, one of Goodyear’s brokers at Aon.

“The markets always appreciate a risk manager who demonstrates ownership,” Garvey said, in what may be something of an understatement.

These risk managers put in gear their passion, creativity and perseverance to become masters of a situation, pushing aside any notion that they are anything other than key players.

Dianne Howard, a 2018 Risk All Star and the director of benefits and risk management for the Palm Beach County School District, achieved ownership of $50 million in property storm exposures for the district.

With FEMA saying it wouldn’t pay again for district storm losses it had already paid for, Howard went to the London markets and was successful in getting coverage. She also hammered out a deal in London that would partially reimburse the district if it suffered a mass shooting and needed to demolish a building, like what happened at Sandy Hook in Connecticut.

2018 Risk All Star Jim Cunningham was well-versed enough to know what traditional risk management theories would say when hospitality workers were suffering too many kitchen cuts. “Put a cut-prevention plan in place,” is the traditional wisdom.

But Cunningham, the vice president of risk management for the gaming company Pinnacle Entertainment, wasn’t satisfied with what looked to him like a Band-Aid approach.


Instead, he used predictive analytics, depending on his own team to assemble company-specific data, to determine which safety measures should be used company wide. The result? Claims frequency at the company dropped 60 percent in the first year of his program.

Alumine Bellone, a 2018 Risk All Star and the vice president of risk management for Ardent Health Services, faced an overwhelming task: Create a uniform risk management program when her hospital group grew from 14 hospitals in three states to 31 hospitals in seven.

Bellone owned the situation by visiting each facility right before the acquisition and again right after, to make sure each caregiving population was ready to integrate into a standardized risk management system.

After consolidating insurance policies, Bellone achieved $893,000 in synergies.

In each of these cases, and in more on the following pages, we see examples of risk managers who weren’t just knocking on the door; they were owning the room. &


Risk All Stars stand out from their peers by overcoming challenges through exceptional problem solving, creativity, clarity of vision and passion.

See the complete list of 2018 Risk All Stars.

Dan Reynolds is editor-in-chief of Risk & Insurance. He can be reached at [email protected]