AI-enabling the insurance value chain

Introduction: Bigger Opportunities

Machine learning and Artificial intelligence provide immense opportunities for the insurers who adopt these technologies effectively. These technologies essentially have the capability to transform various areas such as claims, customer service, marketing, underwriting, Straight-through processing (STP) and Fraud detection.

The adoption of AI-enabled services has grown exponentially across industries in recent years.  These services are reorganizing and uplifting organizations across all industries - even insurance. Increased data consumption, changing digital capabilities, and increased consumer expectations, all have accelerated AI development and usage. Consequently, artificial intelligence is continuously creating versatile opportunities for business growth.

Some of the key areas insurance companies can improve using AI are:

  • Pricing:  Precise and advanced automated setting of pricing, which is based on risk computation
  • Loss control: Reduce operational costs, reduce errors through accurate decisioning, and prevent claims leakage through automation provides better loss control
  • Claims: AI enables claims automation, which streamlines the settlement process and detection of potential fraud. It also enables Straight-through processing (STP)
  • Underwriting: Automate underwriting to ensure rapid and risk managed customer acquisition and onboarding

End to end automation is the ultimate goal that insurance companies want to achieve. It involves the automation of each process for a streamlined and quick service, providing an improved customer experience.

Peek into the present transformative trends

AI and machine learning provide powerful platforms to build streamlined services, which are more advanced than the traditional approaches. This will be favorable for both the customers and the company to offer smooth onboarding and quick claim resolution.

Data collection and management is crucial for insurance companies. They can no longer be dependent on traditional data collection and analysis processes. Companies need to make use of real-time data that is both operational and perceptive. Some of the latest innovations are:

  • IoT based sensors for monitoring resources of a residential building.
  • Telemetrics for monitoring driver behavior and managing vehicles.
  • Bio-wearable devices for monitoring wellness and safety to provide health insurance for individuals and company workers.

Underwriting Triage

Underwriting is an intensive process, requiring large datasets from multiple sources, in multiple formats. Only when the data gets structured and analyzed, underwriters can predict risk probability and impact, and provide a cost-effective quote to the client.

This decision-making can be automated using AI/ML, which aids underwriters to efficiently analyze and standardize the process for better decision-making across the company. Risk pattern analyzing and risk predictions are quite difficult through manual processes; and with AI and machine learning, these risk patterns can be used for earlier predictions.

Claims and Engagement

If the claims decision takes too much time, companies risk losing customers due service degradation; if claims are processed quickly, there is the risk of overpaying, or becoming victim to fraudulent claims.  Insurance companies can win this tug of war using AI.

AI can be deployed for automation of the claims triage. These strategy adoptions can aid companies to direct “no-touch” claims, designate “low touch” and “high touch” claims for further investigation and manage them efficiently.

AI and machine learning can be adopted throughout the claims process for automated analysis of claims based on their size and complexity, filter them among the chances for litigation, triage, highlight possible fraud cases, and cases for subrogation. This precise and robust claim management model will provide higher accuracy and lower the surprises in reserve forecasting.


Insurance companies have steadily ventured towards AI.  With a large group of traditional insurers' already here, many are still experimenting with modern ways to integrate it into their risk and claims management processes. Many InsurTech companies are making use of AI to provide solutions to insurers for streamlining operations, and engaging in developing better underwriting models and claims processing that will eventually personalize and enhance customer service , while also reducing costs for insurers.

AI will definitely provide more opportunities for traditional insurers in order to modernize the process; however what they need to be mindful of, is that the implementation of AI is not straightforward. Insurance providers may come up with many challenges while integrating AI - like infrastructure, privacy, and cost. Hence selecting the right partner - a partner that is enterprise focused, fully understands data security and privacy issues, has a proven track record of productionizing models through robust deployment techniques  - becomes crucial.

Ketaki Joshi

Ketaki Joshi

Driving outreach and messaging to the Insurance community about using AI driven products to gain growth and efficiency. Exploring and developing opportunities for collaboration.