With digital records of one’s savings & risk responses, software could be trained to recommend relevant insurance schemes, schedule auto-renewals, simplify claims and do much more.
Artificial Intelligence (AI) for the insurance industry presents great potential in intelligently recommending customers, what risks to avoid or mitigate, and what investment opportunities could yield good returns on the investment in insurance, along with providing a risk cover. With “machine learning” capabilities, software systems can continuously learn from the user’s data – lifestyle, risks and risk responses, and present relevant opportunities and enable taking actions to be more convenient.
With AI systems and robo-advisors, insurance agents can be more impactful and relevant in customer meetings and calls, they could also take help of technology to be more efficient and flawless processing claims.
Example:Lemonade launched in 2016, with a “Chatbot” based user interface has transformed the way how customers find, purchase, renew and cancel insurance policies. Customers login to the app, answer a few questions and see recommended policies for them. Customer can proceed to purchase through the app and electronically sign documents.
Insurance sector is a data hungry industry that relies on continuous real time data about an asset and the customer, to be able to assess optimal premium amount & claim eligibility, creating a personalized policy based on the customer’s behavior and the asset health. For example, a risky driver pays more premium than a conservative one. For cars that are covered under an insurance policy, the insurance provider could have sensors attached to the car, to have real time data streamed, that indicates the usage and health of the asset. Similarly wearables could be streaming health data for life & health insurance. IoT (Internet of Things) based solutions can help insurance companies gather and study large amounts of real time data, to make assessments on the claim eligibility.
Example:Wearables, biometric sensors and smart watches could monitor and stream heart rate, blood pressure, glucose levels and other health parameters to the insurance company, so customers can be suggested ways to mitigate health risks, and can be incentivised to adopt a healthier lifestyle. This can be intrusive, and needs to comply with data privacy laws and regulations of the state.
“Machine learning” can enable insurance companies to process complex data and turn it into “relevant data” to perform appropriate actions and suggest useful insights to the customer. Machine learning, can go a step beyond analysing the past, to predicting the future – tell what risks are likely to arise and what preventive measures can be taken by the customer.
Example:Telematics car insurance policies – mandate a box (fitting of sensors) that continuously monitors customers driving habits, and enables the insurance provider to dynamically calculate optimal premium amount / claim amount, suggest risk response strategies (avoid, mitigate, transfer, accept etc), incentives for proactively taking measures, to limit accidents.