Role of AI in the insurance sector
Role of AI in the insurance sector
It is not a secret that Artificial Intelligence (AI) is revolutionising every facet of our lives. Its ability to learn, make decisions, and self-correct is making it the fastest growing technology of our era.

Industries and organisations that have rapidly adopted AI, with top organisations having seen manyfold growth and significant impact on their businesses ––and have created competitive advantage for themselves as a result.

The insurance sector can be divided in three broad categories: life and annuity (L&A); property and causality (P&C); and finally, health and wellness. With all these types of insurance categories, organisations observe some common processes like sales and marketing, underwriting, claims, services etc.

AI is being deployed to take up such tasks. In general, AI systems work by ingesting large amounts of labelled training data, analysing the data for correlations and patterns, and using these patterns to make predictions about future states. In this way, a chatbot that is fed examples of text chats can learn to produce lifelike exchanges with people, or an image recognition tool can learn to identify and describe objects in images by reviewing millions of examples. 

Let’s see how AI is transforming most of the insurance subprocesses and providing customers a different experience altogether. 

AI application in new business onboarding: The customer applications came from various sources and format like PDF files, customised forms from partner networks, data dump in the insurance portal database. AI can help an insurance company here in 

  1. Automated relevant content extraction using Natural Language processing and Image processing.
  2. Robotic Process Automation agents gathering relevant information and inserting into database for further processing.

AI applications in Underwriting: Underwriting is one of the most critical processes of all insurance processes. AI can help underwriters in the following ways.

  1. Claims Risk Assessment: Availability of risk assessment factors are growing day by day. With evolution of wearables, preventive healthcare screening results, tracking personal habits like eating, health club memberships etc, AI can create more sophisticated and robust models to ascertain claim risk.
  2. Predict and simulate outcomes of an incident: Take an example of Covid-19. What if an insurance company already had an AI model that could simulate a pandemic and ascertain the overall risk to the population which is already covered or is going to get covered under an insurance policy. This can help the insurance company to determine better and more competitive premiums for their members.
  3. Generate a quote: Quote generation is subject to underwriter’s individual biases and competitor pricing. Machine learning algorithms can help eradicate these biases and derive the best policy quote for an individual or homogeneous group of individuals.
  4. Digital Insurance products: As agents adopt digital tools such as interactive advisory services, companies can use live simulations and digital AI health assessments to provide personalised product recommendations that help customers understand their unique life situations and achieve their financial goals.

AI application in policyholder services: 

  1. Straight thru processing of policyholder change requests like name change, address change, addition, and deletion of members etc.
  2. Customer Experience: AI-driven chatbots have already gained traction in the sphere of customer service. In the insurance industry, these chatbots can enhance scalability and take the load off human resources for more critical matters. At the same time, the chatbots cross-sell or upsell products depending on the customer profile and history. In short, AI can help improve overall customer experience.

AI applications in claims processing: The next process which is highly involved in insurance companies is claims processing. Following are the ways wherein AI can be useful in Insurance claims processing.

  • Claims creation: Automated claims creation by use of image processing and NLP.
    1. Creation of healthcare claim from healthcare records using NLP
    2. Creation of Auto claims with photographs of damaged vehicles using image processing
    3. Improved loss estimation for low claims leakage
  • Conversational AI driven chatbots for first notice of loss (FNOL) reporting.
  • Deep learning driven fraud detection model to detect aberrant and fraudulent claims
  • Claims investigation: AI driven integration services to assist Special Investigation Units to ascertain fraud, waste, and abuse (FWA) claims.
  • Automated claims processing using AI agents (Straight-thru processing).

Major insurance organisations are using AI to get smart quotes and accelerated claims settlement with adoption of Deep learning and image recognition services. Safeco’s right track app captures telematics of the drivers and other sensor related data to generate quotes for its new and existing subscribers. 

Now the bigger question is how we can get started with AI adoption and make your processes smarter. With a determined vision and commitment of AI adoption, an organisation can start by partnering with AI services companies which can help in setting up center of excellence for AI use cases experimentation, incubation of use case, and finally creating a matured AI operations (AIOps) to deliver scalable and self-learning AI solutions for the clients.

Our organisation has already delivered numerous use cases in transforming insurance processes including but not limited to photo-based estimation, smart conversational AI agents, information extraction, and anomaly and fraud detection etc which provide 40 to 60% reduction in manual efforts. 

Source: Times of India

Share this article:

Share on linkedin
Share on twitter
Share on facebook