Early adopters of artificial intelligence (AI) and machine learning (ML) are able to sift through massive amounts of data and use it to enable various capabilities. These range from making decisions about how to triage a claim using algorithms to improving a customer’s overall claims experience using more data and sources automatically pulled in from AI and ML methodologies.
But where does the rest of the industry stand with these new capabilities? We released a study around how the top 100 U.S. carriers are benefiting from AI and ML and the challenges and opportunities for an AI-driven future. We found that 75% believe proper implementation of AI can provide carriers with a competitive advantage through better decision-making.
While only 62% say the carrier they work for is already applying, piloting or planning AI and ML initiatives, these early adopters are already seeing significant AI and ML benefits. In terms of improving the experience for existing customers, insurers are experiencing advantages with faster claims settlements (88%), improved fraud detection (87%) and better risk scoring (85%). On the prospecting side, AI and ML are enabling early adopting insurers more customized and targeted opportunities for cross- and upselling (88%).
Of the survey respondents representing insurers that are early adopters, most come from the 20 largest U.S. carriers, but adoption across the remaining top 100 U.S. carriers is also rapidly increasing.
While carriers are generally positive about their use of AI and ML, implementation does come with its own set of challenges surrounding staffing, data and compliance.
The challenges around AI and ML adoption
Insurance carriers are largely positive about the value of their AI and ML initiatives, but the study identified the challenges they will need to overcome. Staffing challenges are a major concern. According to the study, nearly half of the respondents (49%) said that AI and ML implementation has already affected their staffing plans today. Insurers need people who can understand the inputs and outputs of the applications, and who can explain them to the business. They need knowledge managers who can speak in both technical and non-technical languages and link the dialogue between parties.
Another major concern is the ability to access high-quality, trustworthy data. The three main issues with data that survey respondents mentioned include their ability to manage the volume and security of the data; linking and normalizing data across different data sources; and ensuring access to the data. Adopters clearly see the value of third-party data, as a majority of the adopters (82%) say their organizations have or will buy external data for their AI and ML initiatives.
The third concern we found is around compliance and regulatory challenges with insurers’ use of AI and ML. Adopters worry that regulators and legal bodies may not understand AI and ML applications and could possibly block or limit them. Nearly three-quarters (74%) of adopters also have concerns about data privacy, security and ownership issues, anticipating increased regulatory scrutiny as more data sources are accessed and modeled.
Although the COVID-19 pandemic has slowed things, 95% of personal lines insurers are moving forward with their overall technology plans and investments, with only 5% retrenching, according to Strategy Meets Action (SMA). Meanwhile, 75% of commercial lines insurers are moving forward with their overall technology plans and investments, with only 25% retrenching or pausing.
See also: Step 1 to Your After-COVID Future
Despite these challenges, the early adopters of AI and ML are already seeing benefits. Faster claims settlement, more targeted cross-selling and upselling, improvement in fraud detection and better risk scoring are just a few advantages that insurers are leveraging. As insurance carriers look to implement emerging technology, they should find a technology partner that has a deep understanding of the data, analytics and insurance industry to help them maximize their AI and ML initiatives. In particular, they should look to find a partner with a demonstrated expertise in building models that leverage advanced analytics and that have extensive experience in managing, normalizing and analyzing increasing volumes of data. By this time next year, only those insurance carriers that are fully embracing and implementing AI and ML capabilities now will have that competitive advantage.
For additional insights and data from our study, you can turn to our white paper, The State of Artificial Intelligence and Machine Learning in the Insurance Industry.