With AI generating profound improvements to loan underwriting, is it only a matter of time before similar capabilities are introduced to P&C underwriting?
A recent study on Fintech lending practices may have interesting implications on the future use of AI within P&C insurance. Consider the following case of Lending Club and its use of alternative data and AI scoring algorithms to assess credit worthiness. Lending Club can provide credit access to previously underserved segments with similar or better spreads compared to traditionally underwritten loans. Further, loan performance is similar or better. As short as six years ago, their models relied roughly 80% on traditional FICO scores and loan application data; today that reliance is less than 30%. With AI generating profound improvements to loan underwriting, is it only a matter of time before similar capabilities are introduced to P&C underwriting?
To answer that question, let’s begin by understanding customer attitudes towards the two underlying concepts that drive AI: data and algorithms. AI products require substantial amount of data input. An algorithm is then applied to the data to find patterns or make connections that may not be readily evident given the sheer volume of data analyzed. Herein lies the some interesting, and revealing, insights.
First, we look at attitudes towards data and data sharing. Consumers are focused on two key concerns: security and value. We have several good data points from across J.D. Power on security. A full 80% of financial institutions customers are confident that their data is secure. However, when we look at vehicles and willingness to share driving information, 87% are somewhat or very concerned about security. This is interesting given that for Gen X to Gen Z, 63% and 83% respectively definitely or probably would share their driving data. Of course, they are not willing to share for free—they expect an exchange of value in the form of discounts or additional services. Consumers recognize that vehicle information, driving behavior and owner information are relevant items an insurance company may need to provide a discount, but they clearly are not comfortable (yet) with data security. Further, they aren’t interested in sharing all their data—they would prefer to share specific data that can be traced to the discounts or services received. What these potential conflicts likely indicate is that consumers need time to get comfortable with a technology and the value it delivers before widespread adoption is possible.
Further, data-sharing adoption is already high. 85% of consumers already use at least one of six devices, programs or services that features elements of AI1. Navigation apps lead the way at 84% use with smart home devices (thermostats, lighting, etc.) rounding out the bottom at 20% use. There are clear differences in adoption by age group and education level, with younger, more educated consumers showing stronger use and adoption across the board. It is notable that many products which show the highest adoption rates have been on the market the longest indicating that familiarity plays a role. The key takeaway is that AI applications which provide clear value are likely to be adopted.
Secondly, we explore attitudes towards algorithms (complex analytics). Here we find some revealing views that may appear contradictory given the Lending Club results observed above. A recent Pew Research Center survey2 highlights the general skepticism that consumers feel towards algorithms. 58% feel that computer programs will always reflect some level of human bias. They also express broad concerns about the fairness and acceptability of using computers for decision-making in situations with important, real-world consequences. One such situation is personal finance scoring using many types of consumer data. 68% feel that this is unacceptable citing privacy violations, accurate representation and potential discrimination as key reasons. However, 54% feel that the algorithm would be effective in deciding when to extend credit, but only 32% think it would be fair. Some of the reasons cited for these feelings range from inherent bias, use without permission and inhibiting the ability to freely engage in activities for fear of which ones may contribute to the specified outcome. The contradiction is of course that results of AI algorithms may produce results that do not readily align with current attitudes.
We take away a few key lessons. First, consumers are not inherently against sharing data and subjecting it to complex algorithms to generate better outcomes. They are however, skeptical and want to be confident in the whole process. Second, they want a voice in what data is shared and how it is used to ensure transparency and explainability of outcomes. While it may be possible to simply demonstrate the results, adoption rates may be increased by delivering broad awareness of the inputs, outputs and how they are derived. Third, value needs to be evident from the start. ‘Give me your data and we’ll see what we can do’ isn’t likely to generate trust. Rather a concerted effort to articulate the value offered, highlight the factors that data sharing will impact and then explain algorithm results in the context of ‘me’ v. ‘others’ will drive acceptance. Finally, consumers seek better outcomes. Those companies that provide them in the way consumers want are likely to win the battle of attraction and retention in the low growth, highly competitive personal lines insurance market.
We believe the future potential of advanced technologies to deliver better outcomes is high in the personal lines insurance market. By listening to the Voice of the Customer, carriers can maximize adoption rates to better recover their financial investments.
Throughout 2019, our Insurance Emerging Tech study will dive deeper into these concepts to uncover consumer attitudes, perceptions and behaviors in the specific context of auto and home insurance. We seek to bring a greater understanding of the Voice of the Customer into the design and development of advanced technologies so that better outcomes can be delivered for everyone.
Sources
1Most Americans Already Using Artificial Intelligence Products, Gallup, March 6, 2018. RJ Reinhart.
2Pew Research Center, November, 2018. “Public Attitudes Towards Computer Algorithms”
