The role of AI in commercial insurance

AI in the insurance industry is growing rapidly, and experts believe the market value will reach $6.92 billion by 2028, growing at a CAGR of 24.05%. The new tech capabilities like Artificial Intelligence, Machine Learning, Deep Learning, Robotic Process Automation, Computer Vision, and Natural Language Processing can potentially restructure the legacy approach to managing the insurance lifecycle, from customer procurement to claim processing. The change is driven by the growing need to offer hyper-personalized insurance services to end-users. The need for speed and accuracy are probably other factors influencing the growth of AI insurance.

Factors driving AI Insurance

Omnichannel CX becomes a reality in insurance

The insurance sector is at the lower end of the scale when it comes to digitalization. Many challenges prevent a digital future with a truly omnichannel customer experience offering. However, the pandemic has proved that the digital front is the only way forward as more and more customers turn to online shopfronts to conduct their business. There is no alternative if insurance providers want to stay competitive with their customer experience.

Another important factor, according to Munich RE, is that the true potential of digitization only becomes apparent if it is consistently implemented along the value chain. To realize that insurers have to navigate complex legacy system landscapes with unlinked datasets in sales, the management of insurance applications and claims processing, major anti-selection risks from overly generalized automated solutions, and serious regulatory obstacles.

Digitization has enormous potential when it comes to creating efficient processes, reducing costs, and innovating new product offerings.

Data and analytics

Data has been utilized in underwriting. But, recently, insurers have been striving for more sophisticated data analytics to improve the customer experience by better customer segmentation and targeted offers, enhancing risk assessment in underwriting, reducing the cost of claims, and identifying new sources of sustainable growth.

Unfortunately, certain issues are plaguing the industry, such as data analytics solutions not being embedded into business processes, the value of data analytics solutions not being defined or not measured structurally, and a lack of company-wide vision and strategy. In order to profit from AI in insurance, they need solid data analytics capability first. And with AI comes even more powerful data analytics potential, creating additional pressures to adopt data analytics solutions quickly.

The underwriter as a decision scientist

Experts believe that the future of underwriting will be transformatively driven by talent and technology. Eventually, the underwriter will evolve from a mere risk assessor to a decision scientist. The need for improved decision-making and loss ratios can be met by multiple statistically-based models and codified, heuristic underwriting rules to support sophisticated analytics and rules-based decisions. This will drive higher levels of productivity. Predictive modeling and Machine Learning capabilities are needed for a more granular level in analytical and transactional models, enabling the process of underwriting to move into sales, retention, and CX, as well as early detection and analysis of anomalies and nuances to improve the precision of models and rules.

Challenges and remedies offered by AI insurance

Presently, the insurance sector is grappling with many challenges, much of which was triggered by the pandemic. Some of these issues include the following:

AI in the insurance industry comes as a blessing helping businesses to address such bottlenecks and improve the profitability of their offerings. XtractEdge Commercial Insurance enables commercial insurers to improve Underwriter productivity and response time by offering a complete view of the right information across the New Business and Underwriting lifecycle.

With Document AI solutions like XtractEdge, insurers can witness: