Artificial Intelligence (AI) has been the buzzword of the insurance industry in 2024 and it is looking set to maintain its presence as we move into 2025. It is not uncommon for emerging technology to have this kind of amplified hype around it, which slowly fizzles out – but it seems that AI may exceed the staying power of some earlier tech trends such as Blockchain. The key differentiating factor is AI’s promise to not just deliver a specific capability but to provide more general intelligence behind a wide number of insurance processes.
In this blog, we explain what we mean by AI and explore its impact on both customer experience and back-office operations. Ultimately, we will determine whether the reality of AI adoption aligns with the current hype.
What is AI in the context of insurance?
AI is an umbrella term for a collection of technologies that allow computers to perform tasks that typically require human intelligence. There are different types of AI, such as:
- Generative AI: One of the most widely used subdivisions of AI, generative AI creates text or images based on prompts. An obvious example of this type of AI is ChatGPT.
- Natural Language Processing (NLP): This technology allows machines to understand and process human language. One example may be AI chatbots.
- Artificial Narrow Intelligence: These AI models are designed to complete one particular task and as such are most commonly used for automation.
As we explore examples of AI applications in the insurance industry, it will become apparent where these different AI categories come into play.
How can AI be leveraged as a customer-facing technology?
In this section, we explore the transformative potential of AI as a customer-facing technology and evaluate the impact its adoption can have on customer experience.
Improving customer service
One key benefit of adopting AI-powered solutions is the potential to improve customer interactions with increased availability and efficiency. By deploying conversational AI with Claim Technology, insurers can take their self-service capabilities to the next level. We’ve seen one leading third-party administrator (TPA), who augmented a rules-based approach with conversational AI, increase self-serve rates from 50% to over 90%.
Instantaneous document validation and feedback
Document submission and review is one stage in the claims process where customers often feel they’re experiencing unnecessary delays. Solutions like Paperbox.ai can automatically detect missing information in emails and documents, significantly reducing the amount of manual review time needed, expediting the process for customers and offering a 50% decrease in the amount of time claims handlers dedicate to administrative tasks. By adopting AI solutions like Paperbox, insurers are able to deliver faster, reliable claims experiences that align with the increasing expectations of policyholders.
How transformative is the potential of these use cases?
The most obvious benefit of these AI use cases is the ability to provide continuous availability and streamlined processes. However, these are increasingly being seen as baseline requirements, rather than anything particularly transformative. We believe that the true potential for AI as a customer-facing application lies in its ability to understand and resolve complex customer queries, which otherwise would have had to be managed by a human. When leveraged to its full potential, this capability goes far beyond scripted responses and keyword recognition to give customers the same nuanced understanding they’d receive in speaking to a claims handler. By implementing AI in this way, insurers can prioritise their resources more effectively, meet increasing customer expectations and scale their customer support operations more efficiently.
How can AI be leveraged to improve the back office?
AI is also being used to improve internal legacy processes. The use cases we explore in this section have significant potential for their application in simple claims, but are slightly more limited in their ability to meaningfully transform complex claims.
Fraud detection
One area where AI shines for its time-saving capabilities is in fraud detection. AI solutions can review and analyse documentation, identify discrepancies and highlight instances where fraud is suspected. In straightforward claims processing, insurers may decide to fully automate this process and approve/deny claims based solely on AI’s perception of fraud. However, what is more often the case, is that AI’s role in fraud detection is limited to flagging discrepancies to claims handlers, who can then review the documentation and make a decision.
Image recognition
AI image recognition is becoming increasingly prevalent in several key verticals, for example, home insurance and motor insurance.
Image recognition in motor insurance
Currently, AI image recognition can be used to detect the damage from an accident, like a car crash, evaluate whether the damage can be repaired and estimate the cost of doing so. The limitation for this use case, however, is AI’s current ability to identify pre-existing damage that is not consistent with the accident in question.
Image recognition in home insurance
In contents insurance, AI, coupled with LIDAR technology can be used to identify the structural elements of the home, for example, doors, floors, windows, ceilings and objects. It then automatically captures measurements to create 3D models of rooms or buildings, which are used to estimate the replacement works based on standard cost schedules.
Where AI is less transformative, is in identifying individual objects and assessing their value to recommend a replacement item or adequate compensation.
How extensive is the potential of these use cases?
The key point to take away from this section is that human oversight still remains crucial for successful AI adoption. If insurers take the approach of implementing AI in tried and tested use cases, like flagging potentially fraudulent claims or automatically processing contents insurance claims, then AI has the potential to be transformative for simple claims processes.
Where it is less transformative, is in complex claims processes where the nuanced judgement of experienced professionals is needed. In these instances, AI is still beneficial for accelerating processes and reducing the manual load on claims handlers, however, it takes on more of a supporting role, than a true transformation driver.
Challenges and considerations for adopting AI
For insurers aiming to leverage AI’s transformative potential, there are several key challenges and considerations they must address to experience any transformative benefits.
- Data privacy, security, and regulatory compliance – Insurance is a heavily regulated industry, where handling highly confidential personal data is an everyday occurrence. Therefore, insurers adopting AI must adhere to strict data privacy standards and work with trusted providers like Claim Technology, who specialise in insurtech.
- Integration with legacy systems – One of the most significant hurdles is incorporating AI into outdated infrastructure. Claim Technology facilitates seamless integration with all of the providers within our insurtech marketplace, through plug-and-play APIs. This enables insurers to adopt AI without overhauling their existing systems.
- Continuous model refinement – AI implementation is not a ‘fire and forget’ effort. Models are only models, and they need constant refinement. AI should be seen as a continuous improvement effort, much like the modelling, executing and measuring of its technology predecessor, business process management.
- Upskilling the Workforce – The most likely entry point for AI into the insurance industry is in an assistant capacity. Therefore, employees must be prepared and equipped to work alongside AI. Equally, insurers must provide adequate training programs to help staff understand and adapt to AI-enabled processes.
A balanced, realistic approach to AI transformation is needed in the insurance industry
While AI holds the promise of transformation within the insurance industry, the journey from potential to realisation requires a clear, strategic approach. Insurers need to identify the specific use cases —for example, automating straightforward contents claims and enhancing customer interactions—while acknowledging the current limits of AI in complex scenarios. The successful adoption of AI hinges on insurers being able to maintain a balanced partnership between advanced technology and human expertise.
