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

Risk Focus: Cyber

Expanding Cyber BI

Cyber business interruption insurance is a thriving market, but growth carries the threat of a mega-loss. 
By: | March 5, 2018 • 7 min read

Lingering hopes that large-scale cyber attack might be a once-in-a-lifetime event were dashed last year. The four-day WannaCry ransomware strike in May across 150 countries targeted more than 300,000 computers running Microsoft Windows. A month later, NotPetya hit multinationals ranging from Danish shipping firm Maersk to pharmaceutical giant Merck.


Maersk’s chairman, Jim Hagemann Snabe, revealed at this year’s Davos summit that NotPetya shut down most of the group’s network. While it was replacing 45,000 PCs and 4,000 servers, freight transactions had to be completed manually. The combined cost of business interruption and rebuilding the system was up to $300 million.

Merck’s CFO Robert Davis told investors that its NotPetya bill included $135 million in lost sales plus $175 million in additional costs. Fellow victims FedEx and French construction group Saint Gobain reported similar financial hits from lost business and clean-up costs.

The fast-expanding world of cryptocurrencies is also increasingly targeted. Echoes of the 2014 hack that triggered the collapse of Bitcoin exchange Mt. Gox emerged this January when Japanese cryptocurrency exchange Coincheck pledged to repay customers $500 million stolen by hackers in a cyber heist.

The size and scope of last summer’s attacks accelerated discussions on both sides of the Atlantic, between risk managers and brokers seeking more comprehensive cyber business interruption insurance products.

It also recently persuaded Pool Re, the UK’s terrorism reinsurance pool set up 25 years ago after bomb attacks in London’s financial quarter, to announce that from April its cover will extend to include material damage and direct BI resulting from acts of terrorism using a cyber trigger.

“The threat from a cyber attack is evident, and businesses have become increasingly concerned about the extensive repercussions these types of attacks could have on them,” said Pool Re’s chief, Julian Enoizi. “This was a clear gap in our coverage which left businesses potentially exposed.”

Shifting Focus

Development of cyber BI insurance to date reveals something of a transatlantic divide, said Hans Allnutt, head of cyber and data risk at international law firm DAC Beachcroft. The first U.S. mainstream cyber insurance products were a response to California’s data security and breach notification legislation in 2003.

Jimaan Sané, technology underwriter, Beazley

Of more recent vintage, Europe’s first cyber policies’ wordings initially reflected U.S. wordings, with the focus on data breaches. “So underwriters had to innovate and push hard on other areas of cyber cover, particularly BI and cyber crimes such as ransomware demands and distributed denial of service attacks,” said Allnut.

“Europe now has regulation coming up this May in the form of the General Data Protection Regulation across the EU, so the focus has essentially come full circle.”

Cyber insurance policies also provide a degree of cover for BI resulting from one of three main triggers, said Jimaan Sané, technology underwriter for specialist insurer Beazley. “First is the malicious-type trigger, where the system goes down or an outage results directly from a hack.

“Second is any incident involving negligence — the so-called ‘fat finger’ — where human or operational error causes a loss or there has been failure to upgrade or maintain the system. Third is any broader unplanned outage that hits either the company or anyone on which it relies, such as a service provider.”

The importance of cyber BI covering negligent acts in addition to phishing and social engineering attacks was underlined by last May’s IT meltdown suffered by airline BA.

This was triggered by a technician who switched off and then reconnected the power supply to BA’s data center, physically damaging servers and distribution panels.

Compensating delayed passengers cost the company around $80 million, although the bill fell short of the $461 million operational error loss suffered by Knight Capital in 2012, which pushed it close to bankruptcy and decimated its share price.

Mistaken Assumption

Awareness of potentially huge BI losses resulting from cyber attack was heightened by well-publicized hacks suffered by retailers such as Target and Home Depot in late 2013 and 2014, said Matt Kletzli, SVP and head of management liability at Victor O. Schinnerer & Company.


However, the incidents didn’t initially alarm smaller, less high-profile businesses, which assumed they wouldn’t be similarly targeted.

“But perpetrators employing bots and ransomware set out to expose any firms with weaknesses in their system,” he added.

“Suddenly, smaller firms found that even when they weren’t themselves targeted, many of those around them had fallen victim to attacks. Awareness started to lift, as the focus moved from large, headline-grabbing attacks to more everyday incidents.”

Publications such as the Director’s Handbook of Cyber-Risk Oversight, issued by the National Association of Corporate Directors and the Internet Security Alliance fixed the issue firmly on boardroom agendas.

“What’s possibly of greater concern is the sheer number of different businesses that can be affected by a single cyber attack and the cost of getting them up and running again quickly.” — Jimaan Sané, technology underwriter, Beazley

Reformed ex-hackers were recruited to offer board members their insights into the most vulnerable points across the company’s systems — in much the same way as forger-turned-security-expert Frank Abagnale Jr., subject of the Spielberg biopic “Catch Me If You Can.”

There also has been an increasing focus on systemic risk related to cyber attacks. Allnutt cites “Business Blackout,” a July 2015 study by Lloyd’s of London and the Cambridge University’s Centre for Risk Studies.

This detailed analysis of what could result from a major cyber attack on America’s power grid predicted a cost to the U.S. economy of hundreds of billions and claims to the insurance industry totalling upwards of $21.4 billion.

Lloyd’s described the scenario as both “technologically possible” and “improbable.” Three years on, however, it appears less fanciful.

In January, the head of the UK’s National Cyber Security Centre, Ciaran Martin, said the UK had been fortunate in so far averting a ‘category one’ attack. A C1 would shut down the financial services sector on which the country relies heavily and other vital infrastructure. It was a case of “when, not if” such an assault would be launched, he warned.

AI: Friend or Foe?

Despite daunting potential financial losses, pioneers of cyber BI insurance such as Beazley, Zurich, AIG and Chubb now see new competitors in the market. Capacity is growing steadily, said Allnutt.

“Not only is cyber insurance a new product, it also offers a new source of premium revenue so there is considerable appetite for taking it on,” he added. “However, whilst most insurers are comfortable with the liability aspects of cyber risk; not all insurers are covering loss of income.”

Matt Kletzli, SVP and head of management liability, Victor O. Schinnerer & Company

Kletzli added that available products include several well-written, broad cyber coverages that take into account all types of potential cyber attack and don’t attempt to limit cover by applying a narrow definition of BI loss.

“It’s a rapidly-evolving coverage — and needs to be — in order to keep up with changing circumstances,” he said.

The good news, according to a Fitch report, is that the cyber loss ratio has been reduced to 45 percent as more companies buy cover and the market continues to expand, bringing down the size of the average loss.

“The bad news is that at cyber events, talk is regularly turning to ‘what will be the Hurricane Katrina-type event’ for the cyber market?” said Kletzli.

“What’s worse is that with hurricane losses, underwriters know which regions are most at risk, whereas cyber is a global risk and insurers potentially face huge aggregation.”


Nor is the advent of robotics and artificial intelligence (AI) necessarily cause for optimism. As Allnutt noted, while AI can potentially be used to decode malware, by the same token sophisticated criminals can employ it to develop new malware and escalate the ‘computer versus computer’ battle.

“The trend towards greater automation of business means that we can expect more incidents involving loss of income,” said Sané. “What’s possibly of greater concern is the sheer number of different businesses that can be affected by a single cyber attack and the cost of getting them up and running again quickly.

“We’re likely to see a growing number of attacks where the aim is to cause disruption, rather than demand a ransom.

“The paradox of cyber BI is that the more sophisticated your organization and the more it embraces automation, the bigger the potential impact when an outage does occur. Those old-fashioned businesses still reliant on traditional processes generally aren’t affected as much and incur smaller losses.” &

Graham Buck is editor of gtnews.com. He can be reached at riskletters.com.