- 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 of Cost Management. Controlling loss costs is 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.
