By embracing GenAI insurers have the potential to transform the entire value chain: reducing costs while driving growth and innovation. But to do it, insurers must address the paradox of “Predictive Uncertainty”, because, for all its potential benefits, GenAI brings a high level of uncertainty e.g. regulatory challenges and ethical concerns around data usage.
Does this contradict the core insurance purpose of evaluating and managing risk? Not necessarily. The reality is that GenAI’s potential to dramatically enhance internal processes, as well as customer experiences, means that it’s too powerful for insurers to ignore.
So how can insurers implement it in a way that transforms disruptive challenges into a competitive advantage?
Insurers need to think about these five key points to ensure success:
Value is already being harnessed by leading insurance companies and others in financial services. Although cost savings are a significant driver, the impact of GenAI goes well beyond, creating opportunities for revenue growth.
GenAI enables insurers to offer more personalized products and services giving them powerful market differentiation, while improved underwriting and fraud detection reduces risk exposure. Similarly, early adoption of cutting-edge technology can attract tech-savvy customers as it shows a commitment to innovation.
Cross-industry capabilities that insurers can leverage include dialogue generation for virtual assistants, augmenting customer service, automated code generation, data analysis, document summarizations, and marketing and sales content generation, to name but a few.
AXA and Allianz are two insurance firms that have already made some headway using GenAI to create innovative employee and customer experience solutions. The AXA Secure GPT is their own version of ChatGPT, an internal service built on Microsoft’s Azure OpenAI Service to generate, summarize, translate and correct texts, images and codes.
GenAI is already being used in insurance to help agents and customers access relevant information and answer queries. Chatbots reduce the time and effort required to research documented knowledge, which improves efficiency and productivity. The advantages not only include cost savings, but also the ability to present complex information in simple and clear terms, resulting in enhanced customer satisfaction and trust.
Insurers can begin to create a cohesive implementation strategy by targeting areas of the insurance value chain where a competitive advantage can be realized, based on use cases that have a proven return on investment. At BearingPoint, we are already working with a number of insurers on GenAI focused-areas to ultimately improve their combined operating ratios.
For all the exciting applications a trustworthy GenAI can offer insurers, uncertainties remain around governance, IP, data protection and quality of information – making a robust strategy essential. Insurers must identify key areas where GenAI can drive value, then prioritize efforts and resources into these specifically and measure what impact they have. Our recent insight on navigating the GenAI journey explores this further.
Human and technology interface is crucial to success. End-to-end automation is a significant opportunity for insurers to improve turnaround time and customer experience.
Insurance in particular can require an elevated level of empathy when handling claims that, if absent, could actually be detrimental to the customer experience.
This is where the co-pilot model comes into play – where a human remains in the loop to oversee the GenAI and refine when required. Even with human involvement, the whole process has been radically simplified, saving time, money and resources.
An example in General Insurance of this could be the end-to-end automation of the claims process. GenAI enabled chatbots can respond to First Notice of Loss and assist the customer-facing triage process to improve the overall customer experience and turnaround time. With a human co-pilot, a person would review the process at key stages to ensure quality of experience, while still significantly reducing costs.
Generative AI can be embedded to customize investment portfolios based on individual customer needs, risk appetite, and financial goals. In the context of life insurance or pensions, we can use GenAI to enable financial advisors to better leverage research, market data and in turn simulate various investment scenarios to optimize returns.
It’s this balance of identifying what areas of the insurance value chain will benefit from automation, with the need for a co-piloted approach (at least in the short term), which will allow insurers to unlock the true value of GenAI.
Employees and stakeholders must get comfortable with the tools. In the long-term, navigating the technology’s uncertainties will come down to implementing the proper safeguards upfront. Monitoring and internal governance should be established and remain consistent, while communication with third-party vendors when utilizing their AI models should always be open.
Regulatory responsibilities, including the EU AI Act and its legislation, pose significant concerns. However, the insurance sector is already well-versed in effectively managing risks and regulatory requirements through robust governance models. Prioritize stakeholder awareness of not only its potential but also the risks, and ensure employee training on GenAI use, so that all foster a shared understanding, recognize the opportunity and are aligned on the future direction.
Despite the risks and uncertainties, GenAI’s adoption by the insurance sector is not a question of if, but how it should be embedded.