Within the next 5 to 10 years, Hastings Mutual Insurance Company and other mid-size small commercial insurance carriers will be wiped out by online insurance companies. The process for a small business owner (e.g., local baker) to buy commercial insurance is surprisingly painful. While individuals can buy personal auto insurance directly online with a few clicks, business owners need to spend hours on the phone with an agent, or physically go into an agent’s office, sometimes waiting days to receive a quote. The process is cumbersome because insurance companies need to ask a lot of questions about a business to assess its risk (e.g., age of office building, number of employees, types of vehicles) . However, digital players are changing the way insurance coverage is delivered to businesses by leveraging machine learning to allow customers to purchase commercial insurance with the same convenience as auto insurance. As millennials replace “mom and pop” and become small business owners, companies like Hastings need to adapt to meet expectations of convenience and speed, or risk becoming obsolete.
[Source: McKinsey Small Commercial Insurance Buyer Survey, 2015)
Hastings has over 600 independent agents across the mid-west region . To buy insurance from Hastings, Business owners must deal with an agent and answer tens of questions. Digital competitors like biBERK, Berkshire Hathaway’s online insurance seller launched in 2016, allows business owners to buy insurance directly online, answering only a handful of questions, and receiving a quote in under 5 minutes . biBerk uses machine learning to analyze historical claims data to identify the application questions that are most predictive of loss. It then further reduces the number of questions by drawing data from third party sources . For example, instead of asking a customer for the year an office building was constructed, this information can be automatically drawn from a deeds registration database available to the public. Larger insurance companies are making multi-million dollar investments in technology to keep up with the changing customer demands for online direct-to-customer channels. Mid-size players like Hastings just simply do not have the financial horsepower to respond .
Having said that, Hastings has made smaller investments in digitization. It has tried to improve the speed of delivering a quote to customers by implementing an online agent portal. The online portal allows agents to submit an application, interact with Hastings underwriters, submit claims, and access other resources . Hastings relies on its independent agents to generate a business pipeline and are therefore the central part of the selling process. Independent agents work for commission, so if Hasting’s system generates quotes and services customers faster than its competitors, agents are more likely to select Hastings. While it does not enable Hastings to respond to online direct-to-customer disruptors, it does make Hastings more competitive among its peers.
In the medium term, Hastings can also take advantage of machine learning to reduce the number of questions business owners are required to answer. If agents spend 5 minutes to generate a quote from Hastings, and 5 hours from its competitors, he or she will choose Hastings every time . This effort is a sub-million-dollar investment and even cheaper if Hastings has in-house data scientists and IT talent. If it has the resources, Hastings could go even further to leverage third party data sources to auto-fill questions, thus further reducing the number of questions customers (and agents) need to answer .
The only risk of using machine learning for mid-size insurers like Hastings is limited historical claims data. Many of these companies sell less than 1 billion dollars in premiums each year . It is difficult to understand which application questions are most predictive of losses if few claims have been placed before. My recommendation to Hastings and other mid-size small commercial insurers is to pool their historical claims data together so that they can improve the accuracy of machine learning analytics outcomes. They will also enjoy greater negotiation power with third party data providers to lower the costs. While this would not ensure mid-size players withstand digital disruptors like biBerk, it would prolong their existence.
Digitization is rapidly disrupting the insurance industry, leaving the mid and small size players most vulnerable. While these players can slow the disruption by pooling together data and leveraging technological innovation, several questions remain:
- To pool together data to improve machine learning analytics outcomes, insurers would need to share claims data with their competitors. Do the benefits outweigh the risks?
- Using machine learning to reduce the number of application questions and leveraging third party data sources come with risks (e.g., inaccurate third-party data). It is likely digital players like biBerk will therefore need to sustain higher number of losses. Is this a viable business model?
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 What’s the Business Insurance Application Process Like?
 Hastings Mutual Insurance Company
 Berkshire Hathaway Goes Small with biBERK Online Commercial Site, Insurance Journal
 Berkshire Unit Prepares to Sell Insurance Direct to Business via Internet, Insurance Journal
 Small Commercial Insurance: A Bright Spot In the U.S. Property- Casualty Market, McKinsey
 Small-business insurance in transition: Agents difficult to displace, but direct sellers challenge status quo, Deloitte
 Digital Disruption in the US Small-Business Insurance Market, Boston Consulting Group