Marc Giombetti, product architect at Two Impulse, a firm adept in utilising artificial intelligence, natural language processing and machine learning tools to boost efficiency in the insurance and financial services sector, delves into detailed market trends and the outlook for the highly competitive industry, in the 6th and final instalment of Delano’s series. Having started in 2016, Two Impulse has grown into a team of over 50 professionals spread across Europe.
Kangkan Halder: In what ways do you anticipate artificial intelligence, particularly in the realm of generative AI, revolutionising risk assessment and policy customisation in the insurance and reinsurance industry?
Marc Giombetti: AI and GenAI in particular are more than just technological advancements for the insurance industry. In my eyes, they are a fundamental paradigm shift in the way insurance products are designed, sold and serviced.
GenAI has a direct impact on personalised policy creation. By analysing customer data, preferences and historical behaviour, AI algorithms can craft tailored policies to individual needs, thus enhancing customer satisfaction and loyalty. On top of that, it automates time-consuming administrative tasks, allowing underwriters to focus on more complex tasks.
Particularly in reinsurance, with complex and individualised contract wordings, GenAI helps to issue better contracts and avoid common pitfalls around legal wordings.
GenAI has applications all along the (re)-insurance value chain, spanning from marketing, sales and underwriting to reserving and claims handling. Optimising claims processing with AI for instance leads to swift, accurate and fast responses and settlements.
For insurance companies to harness these benefits, a strategic approach is key. This involves investing in the right technology and training and developing strategies to effectively leverage this technology. This is critical to ensure success in the digital and AI transformation journey.
How can AI and GenAI enhance the customer experience and service efficiency in the insurance sector?
GenAI is transforming the insurance sector, enhancing both customer experience and service efficiency. As technology advances, we’re seeing more sophisticated applications of virtual agents. The latest revisions of chatbots enable insurance companies to provide real-time customer service and rapid claims processing. What’s more, they allow for the creation of insurance products tailored to individual customer needs.
The developments in predictive customer service are of particular interest to me. By leveraging predictive analytics, it’s possible to anticipate customer needs, offering assistance or advice before the customer even asks. This improves the customer experience and optimises the overall service perception.
However, there are also challenges to address: A significant number of policyholders still prefer human interaction over AI, and there’s a level of distrust towards AI-driven services. Moreover, regulators at all levels and across all jurisdictions are watching with keen interest how AI is being used. Understandably, their main focus is on preventing inadvertently biased or discriminatory AI outcomes.
As we move forward, the industry must balance the integration of AI with these considerations, ensuring that technology enhances services while maintaining trust and regulatory compliance. This balanced approach will be key to fully realising the benefits of AI in the (re)-insurance sector.
Shift towards dynamic pricing is likely to have far-reaching implications for customer perceptions and market competition.
What challenges and opportunities do you foresee for the (re)-insurance industry in Luxembourg in adopting generative AI for claims processing and fraud detection?
In the Luxembourgish (re)-insurance market, the adoption of GenAI presents distinct challenges and opportunities.
The diverse linguistic landscape in Luxembourg, with prevalent languages like Luxembourgish, German, French, English, Italian and Spanish introduces a certain complexity. However, GenAI offers a unique solution. Large language models, a cornerstone of GenAI, facilitate seamless customer service across these languages, making multilingual communication more accessible than ever. In essence, it is not necessary to develop distinct logic for each language--but the model can do the translation into the target languages.
For fraud detection, the rise of advanced visual models plays a critical role. These models enhance the ability to spot irregularities in images, such as those from car accidents, improving fraud detection accuracy. Insurers venturing into GenAI must first tackle core data challenges, ensuring they have robust foundations to leverage these emerging technologies effectively.
The ongoing challenge of effective data management continues to be paramount. Additionally, the adoption of cloud-native services still presents a notable hurdle for many local companies. However, I expect some changes to come, largely influenced by the implementation of the Digital Operational Resilience Act (Dora).
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How do you envision AI impacting the development and pricing of (re)-insurance products in the next two to three years?
Over the coming 2-3 years, AI’s influence on the development and pricing of insurance products will be transformative. As an example, the integration of AI-driven dynamic pricing models represents a significant leap forward. These models, by utilising multiple data sources, enable insurers to offer premiums that reflect current risk factors more accurately. For instance, car insurance premiums can be adjusted based on actual driving behaviour, or life insurance costs can be aligned with health indicators.
However, this shift towards dynamic pricing is likely to have far-reaching implications for customer perceptions and market competition. Customers accustomed to static pricing models may need time to adapt to the fluidity of AI-driven pricing. There’s a potential for increased market segmentation, where customers with lower-risk profiles benefit from reduced premiums, intensifying competition among insurers to attract these lower-risk individuals.
Regulatory considerations also play a pivotal role. The implementation of dynamic pricing models must navigate a complex landscape of regulations that ensure fairness and non-discrimination. Insurers must rigorously test their AI models for biases and ensure transparency in how premiums are calculated. Regulatory bodies might also scrutinise these models to prevent any form of pricing that could be deemed unfair or exploitative. Therefore, while AI presents exciting opportunities for innovation in pricing, insurers must balance these advancements with ethical considerations and regulatory compliance to fully harness the potential of AI in shaping the future of (re)-insurance products.
Each case demands a tailored approach, balancing innovation with privacy and compliance.
Considering the evolving regulatory landscape, how can (re)-insurance companies in Luxembourg leverage AI and generative AI while ensuring compliance and data privacy?
In Luxembourg’s (re)-insurance sector, leveraging AI and GenAI while adhering to regulatory requirements and data privacy involves strategic considerations. Not all activities in the reinsurance value chain are regulated and this gives some good opportunities to get started with AI. For instance, AI can be effectively utilised in marketing and sales processes where client data is not directly involved, similar to the emerging use of AI Copilots in MS Office products which can be used for day-to-day tasks.
The nature of the insurance product also limits or opens up the applicability of AI. For example, the use of AI in life insurance, which often involves sensitive health data, is quite different from its application in property and casualty commercial products or handling parametric insurance. Each case demands a tailored approach, balancing innovation with privacy and compliance.
Insurers should also have a close watch on the Dora. By 17 January 2025, EU financial entities and their ICT providers must comply with Dora. This act opens up for a shift towards cloud computing which can significantly reduce the costs and burdens of on-premise technology. This transition is particularly advantageous for smaller to medium-sized financial institutions because they can use cloud-native AI services from large cloud providers. This can help them to leverage AI solutions effectively and in compliance with regulatory frameworks.