How AI delivers value to insurance
Innovative technologies are opening up new opportunities that are enabling insurers to become more productive, efficient and competitive.
Insurance is an industry in transition, as new payment models, government policies and the need to keep costs down work to change the industry as we know it.
Yet, despite mounting pressures from regulators and increasing customer demands, for the most part the insurance industry still clings to manual, labor-intensive processes that chip away at profitability and customer loyalty. The good news is that this is changing. Innovative technologies are opening up new opportunities that are enabling insurers to become more productive, efficient and competitive.
One key technology that is boosting productivity and providing a key competitive differentiator is Artificial Intelligence (AI). As a data-intensive industry that is inundated with paperwork, insurance is ripe for AI technology. This technology can deliver value to insurers by automating manual tasks, providing faster and better customer service, predicting outcomes, and helping companies make smarter business decisions– in processes ranging from underwriting and risk assessment to customer service and claims processing.
Data, data and more data
Since achieving the best outcomes from AI requires having lots of data, it’s a perfect fit for data-intensive insurance, which thrives on data, statistics and actuarial science. The industry has access to current and historical data on demographics, health and personal information on millions of customers as well as their claims.
So how exactly does AI work/? It starts with identifying a business problem to solve; and gathering all the relevant data that the data scientists uses to train an algorithm to use that data to identify patterns and make predictions.
Data size is particularly important when it comes to predictive analytics, which is based on the scientific process and statistical mathematics. In developing a predictive analytics app, a data scientist comes up with a hypothesis to answer a specific question. The data scientist then tests it using a comprehensive set of data points — or variables. If the hypothesis is not correct, he/she would remove these variables and test other ones instead. If an AI system doesn’t have enough data it might not include the variable that would accurately support the hypothesis. For example, if you are trying to determine where you may have customer churn and you only include demographic information, you might miss other important factors, such as how long they had to wait for customer service.
AI adds value throughout process
AI can provide key value in all areas of insurance — from underwriting to claims. Underwriters base their risk assessment based on what they know about the customer, often based on self-reporting as well as relevant industry statistics. But imagine how much more accurate it could be if insurers could have greater insight into the actual behaviors of their customers.
For example, with a customer’s permission, automotive insurers can use sensor-based devices to determine how good a driver someone really is, using variables, such as speed, or how much someone drives in a year. Similarly, health insurers could track how much a person exercises and engages in other healthy activities when estimating their risk, which could ultimately be passed on as lower premiums for those customers. And even without a customer’s participation, AI programs can review a lot of public data, to determine a customer’s actual behavior and develop more accurate customer profiles.
According to a study cited in Forbes, 80 percent of insurance executives believe that AI will be a game changer in how they collect data from customers, and the majority of respondents said that AI’s ability to gain greater knowledge about their customers was key. AI can also be a key component of the risk management stage, by applying predictive analytics to determine the level of risk and likelihood that specific scenarios may occur.
Another form of AI, chatbots, use natural language programming to create conversational user interfaces and can be instrumental when it comes to customer support and service. It is not practical or perhaps even feasible for insurance companies today to run a 24/7 call center and be able anticipate the spikes that might come from unexpected natural disasters, among other situations. Chatbots help them effectively address that need.
Chatbots can perform a number of functions previously performed by humans, such as answering general questions, providing information on services, or even selling products. By addressing some of the straightforward calls, chatbots can provide fast and efficient service – reducing those long customer wait times — and improving the customer experience. By addressing most of the standard questions, chatbots can direct those that require additional help to human operators.
Another key area where AI is beneficial is in claims. Claims processing is a typically manual and repetitive process that can handled more efficiently and cost-effectively using robotic process automation or RPA, which relies on software robots to complete straightforward, and repetitive manual tasks. Claims fraud is a big concern within the industry, which causes $40 billion of losses in the U.S. annually. AI can be extremely helpful in detecting patterns that might indicate fraud and alerting insurers to them.
Any way you look at it, insurance is an industry that is ripe for automation. AI provides an ideal solution to handle inefficient and manual processes, and even more importantly, it provides added value through insight and prediction. As an industry undergoing enormous change, AI is fast becoming the game-changer for forward-thinking insurance firms, helping to deliver a better customer experience, reduced costs and business growth, and the best is yet to come.
As COO and co-founder of near-shoring software engineering services firm Wovenware, Carlos Meléndez works with customers to help them drive their digital transformation initiatives and leverage the latest artificial intelligence and deep learning applications. To reach this contributor, send email to cmelendez@wovenware.com.
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