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How AI will disrupt Insurance

Insurance is about to have a glow up in the next decade

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Carriers and insurtechs are in for a ride as the insurance industry is going through a seismic technological shift with the dominance of AI and Machine learning - given the wealth of data insurer’s possess. Insurers will uncover a distinct advantage if they incorporate Artificial Intelligence and Machine Learning within their internal processes early, in order to efficiently run their businesses. In addition to this, they must also externally adapt to customer behavior, by drawing more accurate risk profiles better and enabling solid retention; while providing an A+ customer experience.

 

The Technological Shifts in AI


Large Language Models

Large Language Models (LLM’s) are now able to process an astounding amount of data via AI accelerators. They are a type of AI model that is designed to understand, generate and manipulate human language. Massive amounts of text data can be processed using machine learning and prompting techniques to reach a desired outcome and perform language related tasks. Think text and language translation, content generation and other types of analysis.



Singularity is inevitable

As we prompt and engineer LLM algorithm’s to become more sophisticated, we will reach a point of singularity where machine intelligence will surpass human intelligence. Think lightning fast processing and production – with pinpoint accuracy.



AI for any use case


Although Artificial Intelligence (AI) is a buzzword on everyone’s radar after the massively successful debut of ChatGPT – AI has been around for a long time and has extremely widespread use cases depending on its application and complexity. AI is an umbrella term that can be translated to several branches. We’ll dive deep into each of these in a separate article.

AI technologies can be applied to any process of the insurance value chain, depending on the needs and priorities of the specific enterprise:

Artificial Intelligence in the Insurance Value Chain


Text Analytics and Natural Language Processing


A large amount of customer feedback and support calls are handled via phone call transcripts, social media posting, emails and other open text formats. Natural Language Processing enables insurers to quantitatively analyze this text for marketing and solutioning analytics for customer queries.


Customer support and claims reports resolve can be fast tracked via these text recognition algorithms, generating patterns of solution optimizing.


Pattern Recognition and Anomaly Detection


Customer behavior and demographic data sets can be analyzed to predict product selection and spearhead more specialized product development. Insurers can be more reactive to customer demand in real time and create hyper-personalized audience segments. Win-win for insurers and customers alike.

  • Parametrically analyzing quote and claims data from external data sources for accurate pricing and claims validation. For example weather reports, customer health data etc.

  • Automating customer query flows based on past inquiries.

The possibilities are endless.


Recommendation Engine


When I say a recommendation engine, what I mean is predictive data analysis combined with machine learning (adaptive to changing needs, and other data factors):

  • Generating product recommendations for purchase that are marketed to customers based on their income, preferences and other purchase behavior information.

  • Using historical data to bucket customer’s into risk categories and generating templates for faster claim resolve based on the most common reports.


Speech Recognition


Virtual voice assistants could replace human employees and be effective at verifying customer identity and the information shared to prevent fraud. Another aspect of this is understanding customer emotion or sentiment


Image and Video Analysis

Auto, Property and Casual and Business Owner Protection insurance can optimize for image and video analysis for all claims submissions – allowing the insurer to accurately scope damage and assess compensation / claims losses. This reduces the need for physical assessment of the damage and reduces time for the customer’s claim payout.


 

The use cases of Artificial Intelligence in Insurance


In conclusion, insurers stand to gain significantly in terms of cutting costs, headcount and a more personalized insurance lifecycle, while mitigating their claims losses.


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