The Transformative Impact of AI and Gen AI on the Insurance Industry

Share on Social Media

The insurance industry is undergoing a profound transformation as it embraces cutting-edge technologies such as artificial intelligence (AI), deep learning, and connected devices. This transformation relies on the following key technological trends:  

  1. Explosion of Connected Device Data: The proliferation of connected devices generates a vast pool of user data, serving as a cornerstone for AI applications.
  2. Open-Source and Data Ecosystems: Open-source & data ecosystems foster collaboration, enabling seamless data sharing and utilization across the industries.
  3. Advances in Cognitive Technologies: Cognitive technologies, including AI-powered models, redefine how insurers approach distribution, underwriting, pricing, and claims processing. 

Generative AI, or Gen AI, holds the potential to revolutionize the insurance industry by merging human creativity and ways of interpreting data with artificial intelligence. Unlike traditional technologies, Generative AI goes beyond optimizing existing data. Not only can it infinitely combine data sets, but it also learns from it which is different than other types of automation. With this capability, Gen AI will provide novel and creative outputs for any user. 



Nowadays, AI is being utilized in insurance for various workstreams: 

  • Underwriting and Risk Assessment: AI tools help companies create personalized risk profiles and utilize predictive analytics for determining the level of risk associated with insuring a particular individual, property or entity. 
  • Improved Claims Processing: With Gen AI, manual process interventions can be minimized since this technology can apply more specificity to business rules and therefore, it also delivers more reliable results. 
  • Customer Service and Engagement: Nowadays, companies with AI-powered chatbots provide human-like interaction and conversations which results in improved accuracy and faster decisions. 
  • Ensuring Data Confidentiality: Gen AI allows users to implement privacy-preserving techniques like federated learning, a machine learning approach that allows a model to be trained across multiple devices or servers, instead of training the model on a single centralized server, helping to maintain data privacy.    
  • Predictive Analytics for Market Trends: With the pre-trained Large Language Model (LLM) and the ability to combine historic company, market, and environmental data, Gen AI engines are also able to provide insights for strategic decision-making and dynamic pricing models. 


checking info on the laptop and mobile


The adoption of automated underwriting in the value chain by numerous insurers, driven by the advantages of time and cost savings, has been notable. Recent research among insurance companies in various countries showcases this early adoption. For instance, in the UK 8 in 10 brokers are already using AI on a daily basis, and in USA, 58% of life insurance companies either use or have plans to incorporate Artificial Intelligence into their operations..  

Automation dominates underwriting, as AI-powered models leverage extensive internal and external data, employing techniques such as third-party data augmentation, which enhances a dataset by incorporating additional information or features from external sources. 

Gen AI supports insurers in risk evaluation, processing vast amounts of data, including medical diagnoses, and mitigating potential biases. In doing so, it creates personalized risk profiles and employs predictive analytics

Technically, Gen AI has the capability to analyze current customer data and generate synthetic datasets derived from existing information. This is particularly valuable in situations where specific data types are insufficient for modeling, ultimately enhancing the effectiveness of predictive models. 

Moreover, the synthetic datasets produced by generative AI can replicate the characteristics of the original data while ensuring the absence of any personally identifiable information. This function as a crucial mechanism for safeguarding customer privacy. 



Nevertheless, Gen AI holds the promise of significantly enhancing claims processing through different perspectives. One key aspect is the automation of routine and repetitive tasks inherent in claims processing, such as manual handling of claim reimbursements, data entry, document verification, and basic decision-making processes. By automating these tasks and analyzing data, Gen AI can streamline the overall claims processing workflow, leading to faster payouts and heightened customer satisfaction. 


But, what about the unstructured data? In that case, the application of Natural Language Processing (NLP) is another crucial element. Gen AI can leverage advanced NLP capabilities to understand and process unstructured data, such as text found in claim forms or supporting documents. This advanced comprehension facilitates quicker and more accurate decision-making, particularly in handling complex claims that involve nuanced language and details. 

By identifying patterns, trends, and anomalies that might escape human scrutiny, Gen AI can assist in detecting fraudulent claims and ensure that legitimate claims are processed more efficiently. 

Furthermore, predictive analytics enabled by AI can analyze historical data to anticipate potential future claims. Insurers can proactively implement measures to prevent certain risks, ultimately reducing the likelihood of claims and improving overall risk management. This data-driven approach enhances the overall accuracy and effectiveness of the claim evaluation process

header with hands holding mobiles


As AI analyzes vast amounts of data, companies can provide tailored coverage for their policy holders, improving customer experience and achieving better risk management. 

Currently, there are various examples of tailored insurance policies utilizing AI, particularly within auto insurance companies. The prevalence of telematic devices in vehicles for real-time data collection has notably increased in recent years. These devices analyze behavioral data, encompassing factors such as speed, acceleration, braking, and location. 

Auto insurers then use AI to analyze this data and offer Usage-Based Insurance (UBI). Safe drivers may receive discounts or personalized coverage based on their individual driving habits, improving accuracy in risk assessment. This adaptability allows for real-time adjustments in policy terms and pricing based on changing risk factors, contributing to more responsive and flexible coverage. 

According to several reports, many customers are indeed shifting to UBI-based auto policies, which also offer lower prices. In fact, the UBI market is expected to grow to $80.7 billion by 2028.



McKinsey's estimations suggests that, considering various functions and use cases, investments in artificial intelligence (AI) have the potential to generate a substantial annual value of up to $1.1 trillion for the insurance sector. This represents a growing trend that is reshaping the insurance industry day by day and that accelerates as time goes on.

In a recent interview, Caroline Dunn, Zurich UK’s Chief Underwriting Officer and one of the most relevant professionals in this area, highlighted a significant shift over the past year. As the AI in insurance industry enters 2024, Dunn emphasizes that "the pace of AI development in the future is expected to continue, if not accelerate further." 


In any case, the applications of Gen AI across various functions promise a future of streamlined processes, improved decision-making, and increased efficiency for the insurance sector. 

However, one of the significant challenges for the industry in transitioning to Gen AI is compliance with regulations. Last year, the European Commission proposed an AI Act that aims to establish a unified legal framework for Gen AI development in line with EU values, presenting challenges for regulated industries such as insurance. The AI Act focuses on high-risk AI systems, providing harmonized standards and guidance to ensure compliance. 

In conclusion, while AI in insurance is already integrated into the insurance value chain, the emergence of generative AI indicates untapped potential. Regulations like the AI Act seek to provide a framework aligning with standard values, and their impact on the insurance sector awaits trialogue decisions, necessitating guidance for effective implementation. 

group of Asian people in a meeting

Discover how to leverage AI in your company with SPS

With SPS, insurance companies can harness the potential of Generative AI (Gen AI) to redefine traditional processes and drive operational excellence. From underwriting and risk assessment to claims processing and customer service, Gen AI empowers insurers to make data-driven decisions swiftly and accurately.


Placeholder image

What’s next? The Evolution and Impact of AI

Not all Artificial Intelligence is the same, nor does it have the same impact. Artificial Intelligence (AI) is an umbrella term that includes a broad range of technologies and techniques and is roughly defined as software that can be trained on examples

Placeholder image

Use-cases for generative AI

As explored in the first post in this series, generative artificial intelligence (AI) is a ground-breaking technology that uses large language models (LLM) to create...

Placeholder image

Use Generative AI to talk with your data

If data is the new oil, then the key to unlocking its value lies in refining it. Without effective management and analysis, the insights in your data will go unrealized.