A Glimpse of AI-driven Innovations Tailored for Modern Insurers

Insurance CIO Outlook | Wednesday, August 14, 2019

Artificial IntelligenceAs AI is more deeply incorporated into the insurance sector, carriers are reacting to the evolving market landscape with insights into how AI will reshape the industry.

FREMONT, CA: By quickly generating controlled, digitally enhanced automated environments for maximum productivity, artificial intelligence adds value to multiple industries and specifically to insurance companies, which have plenty of opportunities to gain from investing in AI-enabled technology that can not only automate the planning of executive-level duties but can also enhance the quality of service by supporting agents make good choices and make final judgments. Within the fields of their prime concern, the insurance businesses certainly profit from the AI implementation. With this knowledge, they can begin building abilities and talent, embracing evolving technologies, and creating the culture and outlook required to be active players in the future insurance industry. There have been several use cases for AI that can be leveraged to solve the many problems faced by insurers. Here are just a few instances of AI application.

Image Analytics

Formulating insurance policies within minutes, advanced picture analytics enables fast photo analysis to determine parameters such as age, BMI, habits, etc. that are essential in the life insurance view. These parameters can assist in determining whether or not a medical underwriting solution is necessary. If underwriting is not needed, insurers can provide an immediate quote and formulate policy within minutes. Zero Touch Claims in Property and Casualty Insurance, advanced picture analytics can be used to analyze car pictures in accidents, to determine parameters, and to evaluate the cost of replacement.

Machine Learning in Underwriting

Underwriter shaves the tedious and error-prone task of handling various pages of unstructured papers and extracting data for company choices from them. AI, machine learning, and deep learning can assist extract data from these papers, align it with a common vocabulary, and facilitate access to data through a search engine or virtual assistants. Underwriting is therefore decreased to an automated method lasting approximately a few seconds.

Process Automation

The most common cause of concern for insurers is incoming information obtained from brokers. It comes in a multitude of formats, without standardization, requiring many individuals to transform the data into a standard format. The submission can only be processed if the information is mapped correctly. Here, AI shows high potential, allowing insurers to decrease process inefficiencies. Machines can learn patterns and assign new submissions automatically. AI can also enhance the quality of information by identifying and addressing incoming data gaps.

Connected Claims Processing

Insurance claims can be mainly automated with sophisticated algorithms, allowing insurers to reach drastic effectiveness and precision levels, decreasing processing times from days to hours or minutes. Technologies for data capture, including IoT sensors, substitute manual techniques. It is also possible to automatically trigger claim triage and request for repair services. Assessment of a claim's validity is also a much simpler job for insurers.


Lengthy papers and complicated policies often make insurance policies confusing and daunted for clients. They have questions, expect almost instant answers to their issues, and therefore, 24x7 assistance is compulsory. Chatbots, created from AI's natural language processing capacities, serve as virtual agents that can answer most demands and inquiries from customer service. If the applications are not in their domain, these chatbots can also pass specific applications to human agents.

Insurance companies and insurers can react to the evolving landscape, with the value additions fostered by AI. Also, smart algorithms have substantially decreased fraudulent allegations through their capacity to self-learn, identify patterns, and flag. It's just the start, and they're certainly more to come in the future.

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