Generative AI and Insurers: Move like a startup

Co-authored by? Jürgen Eckel

Generative AI is an emerging subset of artificial intelligence that refers to machine learning models that generate new, original data that mimics the style, patterns, and structure of existing data. In other words, generative AI models produce new data based on patterns and rules learned from existing data, rather than simply classify, or predict new data based on existing labels or features.

Generative AI has many obvious applications in fields such as art, entertainment, design, and advertising, but we are also seeing it bring tremendous value and innovation to the insurance and financial services industries.

Gen AI technologies such as ChatGPT are increasingly used in the insurance industry to improve customer service, reduce costs, and enhance operational efficiency. These technologies have the potential to revolutionize the way insurers interact with their customers, enabling more efficient and personalized customer service for those insurers who rapidly build the digital products needed to bring these use cases to market.

One of the primary use cases for Gen AI in insurance is customer service. With ChatGPT, customers can interact with virtual assistants that use Natural Language Processing (NLP) to provide real-time assistance and answer questions, increasing the productivity of human agents. This can result in faster response times, reduced costs, and improved customer satisfaction. While chatbot solutions have been around for years, the giant step forward provided by the latest Gen AI solutions revolutionizes the quality and breadth of servicing options that can be built.

Gen AI can automate routine tasks such as claims processing, underwriting, and risk assessment. By using Gen AI to analyze data and identify patterns, insurers can improve the accuracy and speed of these processes, reducing errors and improving efficiency. Gen AI is already being used by some insurers to significantly improve underwriting efficiency. Actionable predictions using internally and externally available data, reducing manual data entry and scrubbing tasks all drive efficiency.

Another use case for Gen AI in insurance is fraud detection. Fraud is a significant problem in the insurance industry, with billions of dollars lost each year to fraudulent claims. By using these tools to analyze data and identify patterns, insurers can detect fraudulent activity more quickly and accurately, reducing losses and improving operational efficiency. Research published by the University of Virginia showed 99% accuracy in detecting fraud in a data set of almost 80 million credit card transactions.

Personalized insurance products and services provide another opportunity to leverage Gen AI. By analyzing customer data and behavior, insurers can rapidly develop personalized products and services that meet the unique needs and preferences of individual customers. This can help insurers to differentiate themselves from competitors and increase customer loyalty.

Finally, Gen AI can improve risk management. By analyzing data from multiple sources, including social media, weather data, and news feeds, insurers can better understand and manage risk. This can help insurers to develop more accurate risk models, improve underwriting decisions, and reduce losses.

?Gen AI is without a doubt the next big wave of technology to sweep across all industries, including insurance. Many insurers will be left behind in this revolution, though, due to their inability to quickly identify relevant use cases and bring them to market. Insurers are held back by their own out-of-date technology practices and cumbersome procedures for identifying product candidates and selecting a few to test in the market. An approach that emphasises assembling a pool of potential ideas and vetting them rapidly through a shark-tank-like process—before quickly building and bringing to market a product that customers can give feedback on—is needed to keep up with opportunities presented by Gen AI. For organisations that don't have this capability already, there is still hope in the form of Product-Led Transformation.?

Product-led transformation emphasizes identifying new digital product opportunities through a rapid and transparent vetting process and the quick building of an MVP while simultaneously streamlining the ways of working for the team to accelerate their productivity. Once the flywheel of product-led is started, organisations maintain the momentum and continuously bring to market new and innovative digital products, including Gen AI enabled products.

Whether it's through improving customer service, automating routine tasks, detecting fraud, personalizing products and services, or improving risk management, insurers can use Gen AI to drive efficiency, reduce costs, and improve customer satisfaction. As these technologies continue to evolve, insurers that fully embrace them will be well-positioned to succeed in the years to come.