At Amazon’s prototype grocery store, Amazon Go , customers can walk in, pick up what they want and walk out, without ever waiting in a checkout line or pulling out a wallet. Amazon will automatically charge their account and send them a receipt. Run out of paper towels? No problem. Amazon Prime customers can place an order from their phone and get same-day delivery.
copyright by venturebeat.com
Unlike Amazon, and many other product and service providers, insurance companies have fewer opportunities to interact with customers, and those opportunities are less rewarding. Insurance is traditionally a “selling” process not a “shopping” process, and claims are the necessary outcome of an unfortunate event. For insurers looking to turn the tables on a less-than-ideal dynamic, artificial intelligence can transform both the customer experience and the claims process.
Chatbots have the answers
What problems can AI solve for insurers? Many are investing in chatbots, powered by natural language processing, to improve customer experience. Chatbots are fast and efficient, and customers can interact with them in the way they are most comfortable communicating — via whatever mobile device they have in their pocket. And unlike humans, they can help more than one customer at a time and are available around the clock. Chatbots are not strictly a utility play. When they are designed to have personalities that align with the brand powering them, they move from simply transactional to transformative customer experience.
In a recent Accenture study of the insurance industry, 68 percent of respondents said their companies use some sort of AI-powered virtual assistant in at least one segment of their business. Geico’s virtual assistant, Kate, for example, answers basic policy and billing questions within an app. Digital insurer Lemonade takes things a step further. Their chatbot, Maya, sells inexpensive homeowners’ and renters’ insurance, and their claims bot, AI Jim, makes Amazon Prime’s same-day delivery look slow — it recently settled a simple claim in three seconds.
As chatbots become more commonplace, they are making their way into behind-the-scenes claims processes as well. Tableau’s prototype chat software, Eviza, has a voice interface, so users can drill into its signature data visualizations simply by asking questions out loud. Clara Analytics offers askClara, a chatbot they bill as a “24/7 personal assistant to the claims handler”. Like customer-facing chatbots, it can answer routine questions about a given set of claims.
Machine learning makes sense of data
Insurance companies are sitting on a trove of the one thing AI requires to be successful — data. And AI technologies like machine learning have the ability to make that data actionable. Machine learning can look at data in a number of different ways. It can rank information, putting what it thinks you are looking for at the top of a list; classify information like images; make recommendations; and associate something with a numerical value. It can also group similar things together and detect anomalies.
For example, by reviewing data from closed claims, machine learning algorithms can identify both straightforward claims for automatic processing and complex claims that are more likely to require human intervention. By identifying commonalities in closed claims that resulted in litigation, it could predict which new claims might take a similar path and recommend preventative measures. Anomaly detection plays a big role in identifying fraud of all types. It could, for instance, be used to flag abnormal pharmacy prescribing patterns and alert an adjuster that some kind of clinical review might be necessary. The possibilities are limitless.
read more – copyright by venturebeat.com
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
At Amazon’s prototype grocery store, Amazon Go , customers can walk in, pick up what they want and walk out, without ever waiting in a checkout line or pulling out a wallet. Amazon will automatically charge their account and send them a receipt. Run out of paper towels? No problem. Amazon Prime customers can place an order from their phone and get same-day delivery.
copyright by venturebeat.com
Chatbots have the answers
What problems can AI solve for insurers? Many are investing in chatbots, powered by natural language processing, to improve customer experience. Chatbots are fast and efficient, and customers can interact with them in the way they are most comfortable communicating — via whatever mobile device they have in their pocket. And unlike humans, they can help more than one customer at a time and are available around the clock. Chatbots are not strictly a utility play. When they are designed to have personalities that align with the brand powering them, they move from simply transactional to transformative customer experience.
In a recent Accenture study of the insurance industry, 68 percent of respondents said their companies use some sort of AI-powered virtual assistant in at least one segment of their business. Geico’s virtual assistant, Kate, for example, answers basic policy and billing questions within an app. Digital insurer Lemonade takes things a step further. Their chatbot, Maya, sells inexpensive homeowners’ and renters’ insurance, and their claims bot, AI Jim, makes Amazon Prime’s same-day delivery look slow — it recently settled a simple claim in three seconds.
As chatbots become more commonplace, they are making their way into behind-the-scenes claims processes as well. Tableau’s prototype chat software, Eviza, has a voice interface, so users can drill into its signature data visualizations simply by asking questions out loud. Clara Analytics offers askClara, a chatbot they bill as a “24/7 personal assistant to the claims handler”. Like customer-facing chatbots, it can answer routine questions about a given set of claims.
Machine learning makes sense of data
Insurance companies are sitting on a trove of the one thing AI requires to be successful — data. And AI technologies like machine learning have the ability to make that data actionable. Machine learning can look at data in a number of different ways. It can rank information, putting what it thinks you are looking for at the top of a list; classify information like images; make recommendations; and associate something with a numerical value. It can also group similar things together and detect anomalies.
For example, by reviewing data from closed claims, machine learning algorithms can identify both straightforward claims for automatic processing and complex claims that are more likely to require human intervention. By identifying commonalities in closed claims that resulted in litigation, it could predict which new claims might take a similar path and recommend preventative measures. Anomaly detection plays a big role in identifying fraud of all types. It could, for instance, be used to flag abnormal pharmacy prescribing patterns and alert an adjuster that some kind of clinical review might be necessary. The possibilities are limitless.
read more – copyright by venturebeat.com
Thank you for reading this post, don't forget to subscribe to our AI NAVIGATOR!
Share this: