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GenAI in P&C insurance

Insurers harness the power of GenAI to drive advanced operations

Insurance

The property and casualty (P&C) insurance industry is well positioned in terms of pricing, underwriting, claims, and capital standing but with the caveat of two recurring challenges. One is climate change with exposure to more severe weather that produces catastrophic losses. The other is a race to modernize technology with the promise of lower costs, further improvements in improved claims efficiency, and more advanced customer capabilities. 

Generative artificial intelligence (GenAI) can be the next chapter in how the insurance operating model can be modernized and drive further innovation in P&C insurance. The industry is now considering and experimenting with GenAI use across the insurance value chain, from front-end product development and distribution, to core operations in underwriting, claims, and policyholder services, as well as back-end reinsurance..

Potential use cases that could move the meter include:

Summarization

  • Claims – Quickly capture summary of claims handling status and outstanding actions to speed up handling in a multistate claims team environment
  • Police reports – Combine natural language processing (NLP) and GenAI to recognize facts from police reports across thousands of formats from city and state police departments
  • Underwriting – Bring together relevance facts across various new business application documents to aid better underwriting decisions 

Anti-fraud and duplication

  • Duplicated payments – Use trained GenAI to identify a large volume of supporting documents such as medical bills to prevent duplicated claim payment for the same treatments or expenses incurred 
  • Fraud – Use GenAI to identify forged or inconsistent documents to reduce the probability of fraud in claims 

Policyholder services and customer

  • Customer inquiry – Generate relevant written responses when policyholder inquire about products or application status
  • Customer complaint – Provide timely and warm responses to initial customer feedback or complaints and recommend next steps 

Onboarding new recruits

  • New agents – Use AI to run sales simulations with real-time feedback to increase probability of success before seeing the first client
  • New claim adjusters – Fast on-boarding of new adjuster running multiple auto accident and liability scenarios to provide handling experience accelerators.

1

Summarization: 

  • Claims – Quickly capture summary of claims handling status and outstanding actions to speed up handling in a multistate claims team environment
  • Police reports – Combine natural language processing (NLP) and GenAI to recognize facts from police reports across thousands of formats from city and state police departments
  • Underwriting – Bring together relevance facts across various new business application documents to aid better underwriting decisions 

2

Anti-fraud and duplication:  

  • Duplicated payments – Use trained GenAI to identify a large volume of supporting documents such as medical bills to prevent duplicated claim payment for the same treatments or expenses incurred 
  • Fraud – Use GenAI to identify forged or inconsistent documents to reduce the probability of fraud in claims 

3

Policyholder services and customer: 

  • Customer inquiry – Generate relevant written responses when policyholder inquire about products or application status
  • Customer complaint – Provide timely and warm responses to initial customer feedback or complaints and recommend next steps 

4

Onboarding new recruits: 

  • New agents – Use AI to run sales simulations with real-time feedback to increase probability of success before seeing the first client
  • New claim adjusters – Fast on-boarding of new adjuster running multiple auto accident and liability scenarios to provide handling experience accelerators.

Overall, the adoption of GenAI offers the potential of driving down costs and optimizing processes across the P&C value chain. 

However, GenAI applied to workflow processes requires not only a strong data foundation, but also the engagement of domain experts and the design of a user experience in facilitating seamless interaction between the experts and the AI systems. Each area of the insurance value chain also has its own specific technology, data, and business challenges. 

For any P&C carrier, adopting GenAI is not a sprint completed in a year. It is a multiyear journey that requires a proper foundation in data quality and a cloud strategy to harness its power effectively. 

Overall, the adoption of GenAI offers the potential of driving down costs and optimizing processes across the P&C value chain.

1

However, GenAI applied to workflow processes requires not only a strong data foundation, but also the engagement of domain experts and the design of a user experience in facilitating seamless interaction between the experts and the AI systems. Each area of the insurance value chain also has its own specific technology, data, and business challenges. 

2

For any P&C carrier, adopting GenAI is not a sprint completed in a year. It is a multiyear journey that requires a proper foundation in data quality and a cloud strategy to harness its power effectively.  

“GenAI can be a game changer for insurers. The key isn’t to just bolt on or add new with this emerging technology. The potential of GenAI comes shining through when it’s integrated with a company’s core processes and delivers as promised: cost savings and breakthrough efficiencies.” 

Anthony Lee

Principal, Advisory, KPMG LLP

Foundational data requirement for GenAI

Data standards and requirements for LLMs
Foundational data—encompassing both structured and unstructured formats—such as text, images, audio, and more—serves as the essential raw material for training, tuning, and grounding large language models (LLMs). This data must undergo rigorous processes of collection, curation, labeling, enrichment, verification, and cataloging to meet high-quality standards. Such data is crucial for GenAI as it imbues language models with the fundamental knowledge and patterns needed to generate contextually relevant content that is domain-specific, unbiased, and adept at navigating diverse scenarios. Conversely, to perform inferences, AI models, including LLMs, require access to data across various enterprise systems, necessitating seamless integration and robust data governance to ensure the integrity and utility of the AI outputs.

Critical to the success with integrating GenAI is possessing robust foundational data capabilities characterized by high-quality data, robust controls, and an ethical governance framework to ensure its responsible use.  

Data has long been the life blood of the insurance business. Over many decades, P&C insurers have identified new ways of using data, including predictive analytics, risk management, and fraud detection. Strengths in managing and governing data, specifically in capabilities such as data quality, data engineering, data cataloging, and master data management, are critical to the success of GenAI—and the advent of this emerging technology is causing many insurers to renew their focus on foundational data.  

Use cases for P&C are emerging with lessons learned and gains in efficiencies and capabilities. Begin by exploring three areas of P&C insurance where GenAI is most applicable: underwriting, claims, and policyholder service. 

GenAI use cases photo or interactive component

Improve the speed and quality of underwriting

P&C insurers can achieve performance breakthroughs when underwriting new policies and reviewing policy renewals. GenAI serves as a force multiplier and allows insurers to tap into a broader pool of data for analysis, which in turn enhances the quality of data leveraged for underwriting. Thanks to GenAI, tasks that once required laborious manual effort and considerable time can be accomplished with relative ease. GenAI also generates outputs and responses at AI language model speed—seconds or even milliseconds. The result is greater precision predicting risk, more accurate pricing, and less time spent on underwriting.

With GenAI supporting the underwriting department, reams of data from the highly to tangentially relevant are quickly analyzed. Data from public sources and third-party providers are aggregated. This data may include customer information, risk information, claims, insurance applications, forms and contracts, and even Securities and Exchange Commission filings. The expansion of data that is rapidly analyzed empowers P&C insurers to differentiate pricing, limit underwriting mistakes, and increase efficiency.

With the help of a GenAI tool, even front-line underwriting personnel can now perform sophisticated trend analysis that previously required the expertise of programmers and data scientists. Isolating trends can be difficult for humans to even identify, yet it is simple and easy when using a GenAI tool. The same technology can be used to upskill underwriting staff. GenAI reduces the time required to read and understand contracts or underwriting guidelines by summarizing the information and making it easier for staff to share and clients to understand. 

Lower costs and boost efficiencies with claims

GenAI could make a big impact on the claims process, especially since it is an area of insurance that has featured runaway cost runups. Claims faces rising costs from supply chain, employee salaries, litigation, higher settlements, and increased severity of claims. The key is to strike a balance between the human touch that claims has historically been known for with technology that can automate claims processes.  

That is where GenAI fits in, empowering claims professionals to better serve the insured and equipping claims adjusters to better assess damage and repairs, as well as estimate claim outcomes. Also, P&C insurers should integrate new technologies and digital tools into the claims process while maintaining or improving operational efficiency. Other technologies like traditional AI and blockchain are helping to modernize and streamline claims processing.  

As the P&C insurance industry continues to adapt to managing large catastrophic events like hurricanes and earthquakes, individual carriers that utilize GenAI will be better positioned to profit. Policyholders are accustomed to swift, personalized claims interactions. A GenAI tool backed by a large language model can respond in real time and deliver on policyholder expectations. 

Create a differentiated customer experience

Many organizations are in test mode with emerging technology like GenAI. Testing and initial use cases may be the way forward for some industries. P&C insurance is different. After underwriting, policyholders may have little contact with their agents. But when there is a claim and the policyholder is often shaken and filled with uncertainty, customer service in that moment takes on a whole new meaning.    

Integrating GenAI into the customer lifecycle begins with focusing on operating models and ensuring these models put the customer first. The goal is to use a mix of channels, technology, and human interaction opportunities with GenAI supporting how P&C insurers solve customer issues. Whether it is a call center that handles claims or a mobile phone app, GenAI with a trained language model can support agents on the phone or assist developers who maintain the mobile app.  

To enlist GenAI in enhancing the customer experience, insurers should start small, prioritize trust, and consider the emerging technology as a virtual employee with responsibilities and expectations. During implementation, customer service should constantly measure customer acceptance and satisfaction. GenAI generates novel, human-realistic content that aligns with the role of insurance in people’s lives, so insurance companies should focus on empathy, personalization, and maintaining a human touch. 

KPMG trusted ai

Important considerations for governing GenAI

There are important considerations to keep in mind in order to effectively govern GenAI. The KPMG Trusted AI framework promotes responsible and ethical use of GenAI; aligns with regulations; and emphasizes transparency, fairness, and security. In addition, strong data governance policies should be implemented to help ensure high-quality, secure, and private data. It is also important to monitor and address biases by training models on diverse datasets and regularly assessing for unintended biases. To further this step, it is recommended to continuously measure AI performance in terms of accuracy, efficiency, and customer satisfaction against key performance indicators. Finally, it is essential to maintain GenAI as a tool to enhance human efficiency and decision-making rather than replacing humans entirely.

Stay future-ready with responsible GenAI

P&C insurers face new risks from unexpected sources like climate change, pandemics, and autonomous vehicles. GenAI also presents new risks that insurers will need to bridge, including security issues, language model bias, and a lack of transparency. GenAI demands controls and responsible use policies. For P&C insurers, the goal is staying future-ready, serving customers, adapting to new insurance needs, and remaining competitive. GenAI is already maturing with companies now turning to value creation. Successful early adopters start small and build a foundation while a team of experts provides guidance. The use cases for GenAI can be overwhelming. The expert team prioritizes what needs to be done. GenAI holds the potential to transform areas of the insurance business and upend the entire value chain. At the same time, trust is paramount and should be incorporated with governance. 

10 pillars

Trusted AI framework

At KPMG, we’ve built a Trusted AI Framework based on 10 pillars to help ensure the safe, ethical, and fair deployment of AI in our own operations and made it available to our clients. Together, we can help make sure your insurer’s AI deployment is not just technologically advanced but also rooted in good ethical and governance principles.

Request the KPMG Trusted AI Framework

KPMG can help your organization engage GenAI, whether it’s a rapid GenAI assessment, foundational data readiness, or as consultants on piloting GenAI or leveraging it for value creation.

Dive into our thinking:

GenAI in P&C Insurance

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