The insurance industry has undergone significant changes over the years. The integration of advanced technologies such as artificial intelligence (AI) has paved the way for further evolution, offering improved efficiency, reduced costs and enhanced customer experience. Various AI applications are currently in use in the insurance industry, ranging from underwriting to claims processing.
AI can help insurers evaluate risk more accurately by analyzing large amounts of data such as historical claims data, credit scores and social media activity—thereby enabling insurers to offer personalized coverage to customers and price policies more accurately. It can also aid in detecting and preventing fraud by analyzing data patterns and identifying suspicious activity, which can help insurers save money by reducing the number of fraudulent claims they pay out.
For example, we developed intelligent claim validation software leveraging AI and intelligent robotic process automation (RPA) for one of the largest independent insurance companies in the U.S. that helps analyze and validate claims to generate accurate loss reports. We discovered that claims accuracy improved by up to 99.99%, operational efficiency increased by 60% and customer experience improved by 95%.
A McKinsey article states that “as AI becomes more deeply integrated in the industry, carriers must position themselves to respond to the changing business landscape.” Insurers need to understand what factors are contributing to this change and how AI can reshape the claims-handling, distribution, underwriting and pricing process.
For an insurer, claims processing is one of the most important insurance operations. A study by EY found that 87% of customers say claims processing effectiveness influences their decision to renew the insurance with the same insurer. AI and machine learning (ML) algorithms can facilitate and speed up the claims-handling process without human intervention. ML can help to determine aspects of claims such as image recognition, data unification, data analysis and predict potential costs. ML algorithms typically analyze images and sensors along with the insurer’s historical data, helping to speed up claim settlement.
However, according to a Deloitte study, while 32% of software and internet technologies have begun investing in AI, only 1.33% of insurance companies have invested in AI.
I believe insurance companies should prioritize implementing AI into their existing processes to improve operational efficiency and customer experience. However, there are various challenges that organizations could encounter during this implementation such as a lack of high-quality data for accurate predictive analysis, a lack of AI expertise, the cost to implement and change management across the organization. To overcome these challenges, insurance companies should develop a clear understanding of AI, identify use cases, develop a data strategy and build an AI team with a clear roadmap.
Once a strategy is devised, insurers should invest in infrastructure. Consider looking for partners that can help put together an execution plan and work with them closely to execute it. Insurers should pay special attention while identifying use cases and selecting the partners, as those are the most crucial during the process. Use-case identification should be based on areas where AI can add the most value for the organization, and the partner should be identified based on its AI implementation and industry experience.
Overall, AI solutions can help insurance companies transform their business and help insurers stay relevant as more customers demand an evolving and personalized digital ecosystem of insurance products, services and support channels.