Transforming the Insurance Industry Through AI and ML
Since the insurance industry has always been a highly data-intensive industry, AI and ML technologies are now uniquely positioned to have a substantial influence on the industry. In this article, I’ll describe some ways in which AI and ML can help transform the insurance industry.
Comprehensive Customer-Assistance Services
From assisting customers in opting for the perfect insurance policy that meets their needs to processing and resolving customer complaints, AI can provide seamless solutions for monotonous logistical operations. By implementing deep learning and neural networks, AI can study customers’ profiles and review their needs, then recommend the most suitable policies available. Such changes not only save time by cutting down on the need for consulting but are also cost effective. Renowned insurance companies have already installed chatbots on their Web sites to address customer-specific queries and resolve generic issues.
Efficient Claims Processing and Transaction Management
AI can facilitate the swift processing of insurance-policy claims and automate benefits transactions. It can also regulate a policy’s specifics to further streamline the handling of claims without any human intervention. This, in turn, saves a lot of time in clearing claims filings and enables a company to focus on improving the quality of their service.
Detection and Prevention of Fraud
Fraudulent claims have long posed an enormous challenge to the insurance industry. In fact, instances of fraud are so common that the industry loses about $40 billion every year to insurance fraud in the United States alone! AI can help mitigate fraudulent claims by evaluating previous claims reports and quickly learning to identify fraud. Thus, companies can take swift, effective action against fraud. Predictive analytics can also play a significant role in fraud prevention.
Policy Pricing and Optimization
AI could potentially replace the insurance industry’s conventional approach to policy pricing, in which insurers have deduced certain specifications, then pooled customers based on those specifications. With the aid of predictive analytics in underwriting, AI could personalize policy plans to address specific customer requirements. AI could also develop insights into customers’ preferences, pricing, and behavioral indicators, then list other relevant, accommodative factors that are based on market circumstances and associated risks. Insurers could then utilize all of these approaches to further customize policy compensation.