Designing an underwriter-first future
Middle-market underwriting has the potential to deliver strong returns for brokers, carriers and agents but it can often be encapsulated by archaic processes and inherent inefficiencies, brought about by years of established processes that have somehow failed to evolve.
With the advent of adaptable and affordable automation technologies, particularly those powered by artificial intelligence (AI) and machine learning (ML), these carriers can find essential efficiencies that drive cost savings, create premium customer experiences and deliver better risk profiles to the carrier.
James McKenney, chief strategy officer and products business head of Intellect SEEC, explains to Intelligent Insurer why his keynote at May’s Commercial Lines Innovation USA Virtual Event is a must-see for all interested parties.
“The AI and ML solutions are applied across many lines of business and integrate submission data seamlessly.”
What are the biggest challenges for middle-market carriers?
The commercial market middle-market space is highly fragmented and has a wide range of performance. The industry has been forced to improve underwriting results, due to a variety of reasons including a “lower for longer” investment yield environment. As a result, carriers have looked to make investments to improve their loss and expense ratios, particularly in the underwriting space.
The middle market is a full-touch underwriting experience, with a lot of inefficiency or non-value-added tasks throughout every step of the underwriting funnel. One of the less competent parts of the procedure is in the submission process and inefficient information exchange between broker and carrier.
It’s an expensive game of telephone tag with all those interaction points in the timeframe, and it’s largely inefficient. It’s almost archaic—sending documents over email is highly inefficient for all parties involved, let alone the end customer. In addition, this leaves carriers manually handling large amounts of information, creating much waste in the process and slow turn-around times. With little investment in technology in this part of the market in the past, the underwriting process for many carriers is ripe for investment to create efficiencies.
Carriers are also looking to invest in improving loss ratios. There may be nuances by carrier but for the majority, the main processes are pretty much the same. You try to understand what your exposure is, and whether or not it matches your appetite for risk. Then you look to price to match the exposure.
Risk selection is very important in the middle market. In small commercial insurance, pricing is about homogenous risks—for example, one landscaper business is much like another. In large commercial underwriting, past performance is highly predictive of the future.
In the middle market, it is neither of the two. You have to understand your exposure deeply, as these types of risks are more heterogeneous and their past is not as indicative of the future as with larger accounts. Those who understand their exposures better than their competitors tend to out-perform, otherwise they can be faced with adverse risk selection.
Carriers are investing significantly in understanding underlying exposure in this space to drive preferred loss ratio outcomes.
Those who can drive efficiency, remove the waste and improve the customer and broker experience while balancing that with understanding their exposure better will ultimately win in this highly fragmented market.
Where are carriers focused on creating efficiencies?
There are many opportunities to create efficiencies throughout the underwriting cycle. Many carriers are focused on removing non-value-add steps at each stage of the funnel. There is significant focus across the industry in driving more efficient submission process. As mentioned, there is a highly inefficient exchange of information between brokers and carriers, and many are focused on solving this.
Some solutions are emerging that look to drive connectivity between broker and carrier in a more efficient way than in the past, by creating interfaces or leveraging AI and ML. The take-up on interfaces has been slow in the industry, but leveraging AI and ML solutions has gained significant traction.
The AI and ML solutions are applied across many lines of business and integrate submission data seamlessly. In many cases the data is enriched and directly absorbed into the policy administration or underwriting systems. These solutions have significantly improved cycle time and almost eliminated all manual efforts to key in submission information.
Where do you see investments in sophistication in the middle market today?
Carriers are focused on improving underwriting performance to offset declines in yields. There is continued focus on building advance predictive pricing models. In addition carriers are starting to expand the use of third party data to help better understand risks and exposures.
Many carriers are focused on working smarter rather than harder—by prioritising the right risks to work on earlier in the process.
There has clearly been an explosion of available third party data. The challenge is how to make access easy and understand the context. It would be easy to overwhelm an underwriter with too much information if not contextualised.
At Intellect, we work with carriers to customise data and information in context to other risks in a certain class of business. This enables carriers to differentiate very similar risks, which at times would not be differentiated with existing predictive models.
There is significant investment going on in the industry to prioritise which risks to push through the process to create improved return on investment at every step in the underwriting process.
Many carriers are leveraging various third party data elements to get more predictive on preferred risk as well as identifying risk more likely to bind. Applying this early in the funnel, sometime even prior to receiving a submission to create target accounts, results in improved efficiency and higher bind rates.
With that said, could AI replace an underwriter? The art of underwriting is alive and well in my mind, but those who optimise the interrelation of AI, data, models, and underwriters will certainly win in the long run.
How do we deal with a certain nervousness among brokers and carriers about introducing yet more technology?
There are varying degrees of modernising. Some are behind the curve, but the amount of capital being directed at modernisation is continuously increasing. Investment in insurtechs is multiplying. There are vendors coming out left and right, and it can be hard to weed out who will actually bring value. Do we build or buy? How do we keep up with advancements in AI and ML use cases? There is a ton of complexity in building your modernisation roadmap.
In terms of AI, many carriers are inundated with potential use cases. The leaders seem to focus on AI investments that could create a competitive advantage or a proprietary solution. With the example of AI use case with submission intake previously mentioned, this is typically a “buy” decision as it doesn’t necessarily create a proprietary solution on its face, except when implemented in a proprietary way.
The key is to get started on your roadmap and prioritise those that can make the biggest impact, while focusing on some quick wins.
What do you hope viewers will take from your session at Commercial Lines 2021?
That it’s possible to find a balance between investments in driving efficiency and advancing sophistication. You can be efficient yet trail the industry leader’s loss ratios, and those who invest in one but not the other won’t succeed.
Carriers that are not investing in this space will certainly fall behind and it will be difficult to catch up.
James McKenney will be speaking at Intelligent Insurer’s Commercial Lines Innovation USA Virtual Event (May 18 to 20). The event is free to attend for insurers and brokers/agents, but you must register in advance. Sign up to access the content live and on demand here.
Intellect SEEC, Artificial intelligence, Machine learning, Insurance, Reinsurance, Commercial lines, James McKenney, North America
Designing an underwriter-first future