AI and analytics promise to revolutionise operations within the insurance industry, but significant barriers to their successful implementation remain. While the potential benefits are substantial, the path to integration is fraught with challenges that require careful navigation and strategic thinking.
The Threefold Challenge
Insurers often comment on their struggle to deploy analytics. Indeed, if there is a primary inhibitor delaying the adoption of AI and analytics assistance, it's the ability to join and embed this assistance at scale into systems that need to use it. The technical architecture of many insurance organisations has evolved over decades, creating complex legacy systems that resist seamless integration with modern analytical tools. Even with modern systems, a business case can falter due to the cost of both integrating and managing such analytics interventions.
The second challenge often is how to ensure a seamless adoption of analytics, in this case for claims handlers. Introducing a new capability can demand re-training, process re-engineering, and the ability to absorb and learn from its adoption. Even the most sophisticated analytical tool becomes worthless if the people who should be using it either don't understand its value or find it too cumbersome to incorporate into their daily workflows.
The third consideration is one of trust and defensibility. For insurers to trust any form of outside influence, be it a fraud or total loss model, they need to understand the rationale behind any decision or recommendation it yields. In addition, those decisions need to be audited so they can be recalled for both internal and external regulation. In an industry where every decision can have significant financial and legal implications, the "black box" nature of many AI systems creates understandable hesitation among stakeholders.
Where To Now?
Any solution to enable analytics to be injected into the operation successfully needs to have an answer to the challenges illustrated above.
Firstly, it needs to be able to deploy and manage these analytics interventions at a price point which is in line with the business case, and with a very high degree of business control. It also needs to be scalable. The solution must demonstrate a clear return on investment whilst remaining flexible enough to grow with the organisation's needs.
Secondly, it needs to blend seamlessly into the operation, such that claims handlers benefit immediately and can adopt it easily. User experience becomes paramount when the success of the entire initiative depends on widespread adoption by frontline staff.
Thirdly, as a supporting function, it needs to offer transparency and audit for it to be used and trusted. Every recommendation, every automated decision, and every insight must be traceable and explainable, ensuring that both internal stakeholders and external regulators can understand and validate the system's operation.
The degree to which claims handlers are then empowered and redirected to their most valuable activities can increase their motivation and the extent to which they value their work. Furthermore, such a mechanism has the potential to afford a significant increase in customer sentiment, as well as a reduction in Loss Adjustment Expenses (LAE) and Indemnity.
Fear of the Machine
There is a further consideration: the fear that the machine will replace jobs. This fear can arise from a natural human trait to fall for the ‘False Dilemma’, where crises in particular drive us to seek a 'right, wrong' black or white answer, and to ignore the possibility of a nuanced, more effective one. [1994 Webster & Kruglanski 42-point measurement for NFC (Need for Closure)]
Already, the adoption of AI within insurance is demonstrating the propensity of where good customer relationships remain the domain of the human claims handler. This is unlikely to change. What will change are the tools, with which claims handlers will be able to shed some of the more clerical work and focus instead on driving superlative customer relationships.
The combination of technology and human interaction in claims management is likely to further differentiate insurers in the market. Rather than replacing human expertise, intelligent systems can amplify it, allowing professionals to focus on what they do best while technology handles routine tasks and provides valuable insights to inform decision-making.
The future of insurance lies not in choosing between human expertise and AI, but in creating a relationship where each amplifies the strengths of the other. When implemented thoughtfully, intelligent support systems don't diminish the human element in customer service. Instead, they enhance it, creating opportunities for deeper, more meaningful customer relationships whilst improving operational efficiency and accuracy.
The organisations that successfully navigate this transformation will be those that approach it with a clear understanding of both the technical and human challenges involved. They’ll create solutions that serve not just business objectives, but also the needs and concerns of the people who will use them every day.
For more information about how to embed insights into underwriting and claims processes with predictive analytics specifically made for P&C insurance, head to the Guidewire website: https://www.guidewire.com/products/analytics/predict