Along with healthcare and finance – which intersect the insurance sector in a major way – insurance will start seeing some major changes as artficial intelligence (AI) gains ground as a decision-making tool. Marr predicts that the AI and big data-inspired transformation of insurance has “already begun, with companies like Progressive offering discounts of you agree to put a little monitoring device in your car that lets them know whether or not you’re a safe driver.” Next on the horizon is wearable devices that health insurers could conceivably offer to policyholders in exchange for lower rates. “If your heart rate, blood pressure, activity levels and other things indicate you lead a healthful life, you get a discount.” Soon, autonomous cars may be a frontier for insurance AI as well. There are also applications in agriculture, in which, following a hailstorm, for instance. “Sensor data and satellite imaging, combined with weather data, can instantly assess whether or not you have a claim, eliminating the need for certain claims agents.”
So, what, exactly is AI, and how should insurers approach this new type of decision-making platform? In a recent white paper, Srinivasan Somasundaram, Aritro Bhattacharya and Divyaprakash Modi, all with Cognizant,
They recommend insurers first “use AI to assist human workers rather than displacing them, particularly in underwriting and advisory services.” In underwriting, “AI systems can be used to perform research, aggregate, refine and present required information to underwriters, allowing them to focus on core underwriting activities.
The Cognizant authors identified some ways AI may present itself in the insurance market, in order of sophistication:
• Chat bots
• Personal financial trackers and advisors to target prospects and customers
• Virtual sales assistants that manage basic routine work (e-mails, meetings, lead search, etc.)
• Automated algorithms for needs analysis that can be deployed across all customer-facing channels, thus ushering in robo-advisors
• A dynamic underwriting model based on machine learning to provide enhanced context relevancy for decision-making
• Claims transformation through intelligent prediction and adjudication
• Contact center modernization through voice recognition and interactions mining
Somasundaram, Bhattacharya and Modi recommend the following steps to moving to AI-based operations:
Assess readiness: AI solutions require every interaction/transaction to be recorded for machine learning. The authors recommend “socializing the concept and experience of AI solutions and soliciting response though surveys, interviews and discussions. At the same time, AI decision-makers must spend quality time with their executive teams, peers and functional business leaders to reflect upon the potential implications of new AI-based products or applications on operating models, products and operational workflow.”
Start small: “Given the cultural and risk challenges facing the sector, insurers should start by developing a proof of concept model that can safely be tested and adapted in a risk-free environment,” the authors advise. “Since AI machines excel at routine tasks and their algorithms often learn over time, insurers should focus their early efforts on the processes or assessments that are widely understood and add a modicum of value.”
Manage change: “Because AI capabilities can potentially displace humans -- or require talent upskilling -- insurers need an effective and thoughtful HR strategy. Full communication and retraining of affected staff, as well as a focus on building new skill sets and training, will go a long way toward minimizing resistance and encouraging acceptance. This means insurers must focus on effective change management to ensure that impacted employees understand the tools are deployed to help them do a better job, increase their productivity and value, and enhance customer satisfaction, which in turn will raise employee satisfaction and retention.”
