Earlier this year, I was fortunate enough to spend time at the historic old library within Lloyd’s of London. Lloyd’s was one of the first to apply analytics to insurance by disseminating key information needed for underwriting dating as far back as 1689. Since then, insurers all over the world have been using various forms of analytics to better assess risk, model loss experience, establish pricing, improve retention, identify fraud, target sales and marketing activities, along with hundreds, if not thousands, of individual use cases.
Given the origin of insurance is predicated on the ability to accurately calculate risk using analytics, one might think the whole industry is at the forefront of employing artificial intelligence. While early adopters have created strategic advantages through AI, many others are slower to realize the benefits. Regardless of size or line of business, the benefits of AI to insurers is real – from improving risk segmentation, process automation, retention, mitigating fraud, pricing cyber risks to improving customer service and reducing claims costs. AI promises to transform the industry and deliver competitive advantages to those that recognize and harness its capabilities.
For most insurers, the biggest issue with tapping digital technology is the prioritization of how they will use it, and then aligning on how it will be integrating within systems and operations. They must decide which business problems they need to address first, which require more data to do so, and how to operationalize those models to optimize their core production system. Here are the six ways we anticipate insurers will use AI to advance their business operations in 2019 (and beyond):
- Create Smart Systems. Developing predictive models that are not integrated greatly diminish their organizational value. The natural place for insights to be delivered is within an insurer’s core system and thus, predictive analytics and artificial intelligence will become fundamental components of an insurers core system, creating a smarter insurance organization. To advance business operations, we anticipate insurers to pivot from focusing solely on model development to employing strategies and products to operationalize existing models and enterprise model management.
- Straight Through Processing. Insurers are striving to meet and exceed consumer expectations through faster, easier and more accurate claims settlements as well as improving time to manually quote, review and bind new business. Insurers are increasingly turning to AI in order to get there. Advanced analytics will drive process efficiencies throughout claims and underwriting, progressing to true straight through processing for high-volume, low complex transactions. AI chatbots will deliver customer updates, reducing the number of inquiry calls and the related expense.
- Loss Cost Management. Controlling loss costs is an imperative for all insurers. AI is playing a key role in this effort by targeting key business problems and embedding the analytic results into the core workflow, at the time decisions are made. A common example is using machine learning techniques to accurately identify large losses earlier in the claims process. By routing these claims to highly skilled resources who then follow a prescribed workflow (driven by analytics), adjusters are better able to manage outcomes. AI is also targeting subrogation, fraud and litigation management to better identify opportunities for improvement.
- Increase Profitability. Insurers are using potent AI and analytic algorithms to sift through big data to more accurately calculate risk, improve profitability and drive retention. Increased granularity in insurers’ risk-segmentation practices, which includes innovative use of data such as telematics, public records, areal imagery, geospatial, etc., enable a better understanding of risk. For policyholders, this helps them obtain more appropriate coverage and pricing.
- New Policies and Plans. AI-generated algorithms and insights also provide insurers with the ability to rapidly develop custom policies that reflect each customer’s history, habits, activity level, lifestyle and budget. For example, an individual seeking an auto insurance policy can be quoted a premium based on their driving activity, as gleaned from the data sourced from the growing abundance of connected devices and sensors to help carriers determine the risk level of an individual driver.
- Mitigate 21st Century Risks. Insurers are increasingly mindful of the damage a data breach can cause, not only in potential financial exposure, but also in reputational damage. To combat this, insurers are turning to data listening technologies capable of gathering petabytes of data, then applying machine learning and natural language processing to find signals from structured and unstructured data. The result is increased insights that drive underwriting, pricing and risk management strategies to new types of risk.
What’s increasingly clear is that AI is essential for insurers to grow a profitable business, by gaining and retaining profitable policyholders. And while barriers continue to keep insurers from applying AI more effectively to improve underwriting, risk management, claims and general customer service, insurers are fast recognizing that if they don’t get actively involved in digital, they will suffer competitively.
