How AI and ML Are Transforming the Insurance Industry

January 9, 2023

The insurance industry is both characterized by and driven by data. The industry continually generates and processes large sets of data in delivering effective insurance services, including providing policy pricing, customizing insights, meeting company expectations, and analyzing market trends.

With the world continuously evolving and witnessing extraordinary events such as the global COVID pandemic that inflicted over $55 billion in losses on the insurance industry, the industry’s emphasis on the vitality of existing technology solutions has surged. Plus, the executives and chairpersons of almost three-fourths of insurance-industry verticals are now pushing for innovations. Nearly half of them plan to upscale their expenditures on the integration of artificial intelligence (AI) and machine learning (ML) to enhance predictive analytics and neural networking and deploy robotic process automation. McKinsey has estimated that AI investments could potentially catapult the annual value of the insurance industry to $1.1 trillion!

Champion Advertisement
Continue Reading…

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.

Advances in Cognitive Technologies

Standards for umbrella AI technologies such as deep learning and convolutional neural networks (CNN) will soon enable the processing and structuring of complex datasets. Plus, the processing of newly filed insurance applications will constantly generate additional datasets. These advances will aid insurance companies that are working on new categories of products and customer-engagement strategies.

Incorporating AI Technology into the Insurance Business

The pace at which industries across the world have embraced technological advancements such as automation and deep learning reflect their importance. For the insurance sector to catch up with the adoption of AI technologies, it is imperative that the industry initiate action to facilitate and make space for AI.

Structuring and Implementing a Conclusive Data Strategy

Since data sits at the core of the insurance industry’s functioning, a conclusive strategy for managing data is indispensable. We can deploy AI to cluster internal and external data into identifiable coefficients while maintaining a balance between the two. AI would also be very useful in risk evaluation, improving the customer experience, and simplifying the underwriting process. The only really challenging aspect of its deployment would be keeping this process cost efficient.

Constructing a Suitable Technology Infrastructure

To harness the potential of AI technology, it is equally necessary to cultivate staffing who have the necessary technology expertise. We need highly skilled candidates to take the roles of data engineers, data scientists, and cloud-computing specialists. By pairing such skillsets with AI technologies—including UX design with AI—we can render exceptional results in contrast to using conventional approaches. Therefore, targeted investments by insurance companies are necessary in this arena to update their AI technology stack and enforce a futuristic outlook.

Conducting Extensive Research on AI Technologies

The emergence of AI and its umbrella technologies have created a paradigm shift in the insurance industry. For the insurance industry to determine what functionalities could be either incorporated with AI or automated will require extensive analysis. Technologies such as deep learning could be integrated into the behavioral analysis of policyholders, while convolutional neural networks could process very large policy datasets, then categorize them to render conclusive results. These AI-based technologies could also automate claims processing, facilitate seamless transactions, and aid in record keeping. Therefore, before investing, it is advisable that the industry conduct extensive research on improving the available AI technology stack to ensure that it aligns with companies’ requirements.

Outlining and Undertaking an Effective Business Strategy

The development of a comprehensive business strategy for the adoption of AI technologies by an insurance company would not only cultivate its market capitalization but also result in better branding, product design, and customer engagement. The insurance industry should leverage AI technologies in its business strategy to generate market insights and carefully outline the risks and opportunities to consider. The insurance industry could then use the resulting data in much more efficient ways that would further shape strategic plans for the industry.

As the age of information progresses, both evolutionary and innovative changes in several industries will continue to have considerable impacts on the insurance sector. For example, the expansion of healthcare coverage and a focus on affordability, enhanced preventative measures against natural disasters, an emphasis on safe and sustainable mobility, and the human ambition of attaining a higher standard of living will all shape the insurance industry. Such trends will inevitably lead to the development of new products, fundamental changes in profit pools, and a revamped outlook for the industry. 

CEO and Co-founder of Appventurez

Noida, Uttar Pradesh, India

Ajay KumarAjay has more than 15 years of experience in entrepreneurship, project management, and team leadership. His technical expertise is in software development and database management. Ajay currently directs Appventurez’s day-to-day functioning and administration.  Read More

Other Articles on Artificial Intelligence Design

New on UXmatters