The Rise of Gen AI in
Insurance

Technological breakthroughs are akin to tsunamis. They take everyone by surprise, pack a punch and alter everything in their path. You can either ride the gigantic wave successfully or it will swallow you. The insurance industry is experiencing its tsunami moment, with reports predicting a $1.1 trillion addition in value for the global insurance industry by 2030¹.

How can the Insurance industry players seize the opportunity to ride the wave successfully?
Let’s explore.

Gen AI as a Game-Changer

1. Vast datasets can be processed for precise risk assessment leading to personalized policy creation.
2. Quick validation and fraud detection via Gen AI can significantly enhance claims processing.
3. Gen AI-powered chatbots will handle up to 95% of all customer interactions by 2025² leading to hyper-personalized customer interactions 24/7.

These represent just the tip of the iceberg.

The bigger portion of the opportunity iceberg is predominantly spread across four major areas that insurance players can explore for use cases. These are search, summary generation, content creation and code.

However, high-quality training data is key to achieving AI success. Poor data results in sub-optimal training that can have disastrous consequences. Think of claims data riddled with errors or inconsistencies. An AI engine trained on such data might incorrectly process claims resulting in delayed payments, wrongful claim denials, or even fraudulent claims slipping through unnoticed. We all know how frustrating such experiences can be!

How can one be absolutely sure of the training data? Sadly, we don’t have the luxury of trusting all of the training data due to its very nature of having emerged from a not-so-perfect world. An alternative is to have Gen AI complement humans instead of replacing them. The “human in the loop” approach, where humans collaborate with AI, can provide accurate results and, more importantly, reassure end customers that AI conclusions are human-vetted.

Potential pitfalls include data privacy risks, bias in AI models, and over-reliance on automation. Ensuring data privacy and compliance with regulations is crucial, as AI systems handle sensitive customer information. Consider plausible scenarios that might unravel.

In contrast, in a medical context, it was observed that AI, when used alone, outperformed human doctors, providing the most accurate results. This highlights the need for continuous testing and analysis when integrating AI into various industries, including insurance. Balancing accuracy with efficiency will be a challenge for some time to come.

While a lot of Gen AI tools and techniques abound, here are the approaches that could provide the greatest dividends for the successful implementation of Gen AI in commercial insurance.

You don’t always need to chase the latest AI models. Previous AI versions may be more suitable for certain use cases and can be more cost-effective, making them a viable option for insurers.

Gen AI constructed responses need to be presented in a manner that is discernible and acceptable to humans. Developing interfaces that allow non-technical users to understand and verify AI outputs is crucial.

Experimentation is inevitable. However, insurers should engage in small-scale, rapid experiments to determine the feasibility and potential impact of Gen AI on specific use cases. Rapid elimination of ‘what does not work’ can help your teams to bring the focus back on ‘what can be potentially pursued’. Experimentation is preferred over a ‘wait-and-watch’ approach.

The democratization of AI has enabled a rare opportunity for employees in the insurance industry to move to more value-adding activities³. This should also put the focus back on low-hanging fruits that can be the focus of early experimentation within insurance enterprises.

Budgetary Challenges

Allocating budgets can be challenging as AI experimentation may not provide a clear cost-benefit analysis. No Pain-No Gain – This adage underlines the need for experimentation without overly worrying about the cost. Allocate a portion of the budget for experimentation and risk-taking to facilitate AI adoption.

The rapid development of AI has also led to a mushrooming of vendors, making it essential for insurers to be cautious. It might be prudent to build on existing partnerships and conduct pilot projects with trusted vendors.

Also, it pays to remember that while AI technology evolves quickly, organizational culture changes slowly. It is recommended that enterprises work towards

Disclaimer Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the respective institutions or funding agencies

1. https://www.mckinsey.com/industries/financial-services/our-insights/insurer-of-the-future-are-asian-insurers-keeping-up-with-ai-advances
2. https://www.forbes.com/advisor/in/business/ai-statistics/
3. https://www.bcg.com/publications/2023/why-insurance-leaders-need-to-leverage-gen-ai