3 Questions to Prioritize Insurtech Transformation
Insurers are embracing the technological transformation, ramping up investments as new tools become available. There’s just one issue: with so many new AI and machine learning options, which investments should insurers prioritize?
The AI Boom Is Here
Over the next three years, 65% of claims executives will be investing more than $10 million in AI. That’s according to a report from Accenture, who also says incorporating AI and automation into the underwriting process could help insurers avoid $160 billion in efficiency losses over the next five years.
Technology is advancing quickly, which means insurers that don’t keep up are at risk of falling behind. This could happen in multiple ways. For instance, insurers that:
- Take a wait-and-see approach may find their competitors have raced ahead of them by the time they act.
- Launch technology slowly may lose out on significant opportunities and market share.
- Focus on the wrong technologies may invest considerable resources without realizing a return on investment.
The Next Move Matters
There’s no question that now is the time to invest in insurtech transformation. However, deciding which AI and machine learning technologies should be the priority is more complicated. The following three questions help steer these important decisions.
1. Which Problems Can Be Solved?
The purpose of the insurtech transformation is to leverage technologies that help insurers overcome existing challenges. Which problems do insurers need to fix? Two stand out:
- Underwriting profitability. The property and casualty sector, in particular, is suffering from underwriting losses. Technology that boosts profits should, therefore, be a priority.
- Policyholder retention. As technology improves, customer expectations increase, leading to an arms race as companies try to outdo each other to win over customers. Insurers that leverage technology to create better experiences have the potential to attract and retain more policyholders and gain a larger market share.
The takeaway for insurers: There’s a lot you can do with new AI tools. Before embracing any new tech project, ask yourself why you would want to. How will it help your company achieve its goals?
2. Which Technologies Can be Launched Quickly?
Time is a luxury that insurers can’t afford. If you spend years launching a new system, or attempting to build a system in-house, you may find the technology is obsolete by the time it’s ready for use. Worse yet, you may find that your more agile competitors have already captured crucial market share.
Therefore, when comparing different options, speed to launch is a critical factor. This often means that solutions you can run alongside your existing systems have an edge over options that require you to start from scratch. The days of waiting three years for a complete system overhaul are over. Likewise, it’s helpful if you can avoid having your project delayed by other time-intensive processes, like building a new team or filing new rates.
The takeaway for insurers: As the saying goes, a bird in the hand is worth two in the bush. Real improvements now are worth more than potential improvements two to four years from now. When assessing whether the launch time is practical, consider both how long it will take to implement the technology and how long it will take to train your team to use it.
3. What Are the Potential Risks and Rewards?
Insurers must carefully weigh the potential risks and rewards of new advancements. How much time can be saved? How much additional profit can be created? How will the policyholder experience change? What could go wrong? How quickly can you adjust and fine-tune?
Acting too quickly, especially with tech that involves policyholder-facing touchpoints, may create even bigger challenges. Just imagine what would happen to an insurer that launches an AI-powered chatbot that provides incorrect information about coverage and rates. It could be disastrous.
The takeaway for insurers: Adopting new technology means walking a fine line between being overly cautious and throwing caution to the wind. Look for opportunities with large potential rewards, small potential risks and positive policyholder impacts. Think through worst case scenarios and how you will respond if outcomes are different than expected.
A Solution That’s Worth the Hype
There’s a lot of hype around AI now. Although some tools are little more than novelties, others are absolutely worth the hype – for example, systems that leverage machine learning to revolutionize underwriting processes.
Most insurers already integrate third-party data points and apply underwriting rules within their underwriting processes. The Soteris solution is different.
Soteris uses machine learning to analyze the loss behavior of your own book of business and then builds a unique algorithm to predict each policy’s likelihood of claim on a continuous, numeric scale. The point-of-sale platform enables you to pull in an individual policy risk score within one second – at either or both rate calls. You can pick and choose which policies to quote based on the risk thresholds you establish, ensuring that you only quote business when your rate makes sense. The result is a highly accurate point-of-sale risk selection process that helps insurers quickly write more volume and drive down loss ratios.
How does the Soteris solution stack up against the prioritization questions discussed in this article?
- It addresses two important challenges: profitability and retaining the best policyholders. With Soteris, insurers can turn on segments they had previously eliminated and write a ton of business more profitably. One client recently turned on a segment they had previously shut off due to high loss ratio. Using the Soteris scoring system, they determined that two-thirds of the customers in that segment were good risks. They had previously been throwing out two-thirds of the volume from that segment for no reason.
- The Soteris solution can be launched quickly. The point-of-sale risk scoring solution can typically be launched within four months, alongside your existing technology, without any changes to your existing rate filings. More importantly, insurers can quickly leverage technology that took Soteris years to develop – testing, failing, learning, and iterating, to finally come up with the right way to attack this problem. Insurers can attempt to build the algorithm in-house, but why spend years reinventing the wheel when time is of the essence? Soteris has cracked the code so you don’t have to.
- The potential reward is high and the potential risk is low.
