Insurers must adapt to stay competitive and relevant in today's rapidly evolving digital landscape. Yet, a recent report on digital modernization in the insurance industry shows that legacy systems and infrastructures frustrate technology adoption.
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The report provides insights into the priorities of industry executives, offering practical strategies for insurers to overcome the challenges of legacy systems and infrastructures. By addressing these obstacles, insurers can stay ahead of the competition through the unhindered implementation of emerging and relevant technologies – namely, Gen AI.
Potential applications of Gen AI in different lines of insurance
In recent years, the pace of technological advancement has accelerated, from the birth of the Internet and fast mobile data to the public cloud and the modern app stack. Now, Gen AI has the possibility of unlocking the value of an insurance company's data estate to tackle common insurance challenges and open further opportunities.
In the consumer insurance industry, the potential of Gen AI includes supporting customer self-service, allowing customers to receive new quotes, endorsements and renewals without interacting with a call center agent. Advanced chatbots can advise the customer and automatically process their identity and contact details from their driver's license. With voice interaction widely expected to arrive soon, the concept of a zero-key quote becomes a reality on the horizon.
Other important use cases within consumer insurance include First Notification of Loss (FNOL) guided registration. In this scenario, the technology could automate tasks ranging from customer and policy identification to coverage checks to initial damage assessment and even next-best actions and full claims processing automation.
In commercial lines of insurance, Gen AI could streamline submission processing and augment underwriting support, including ingestion, risk summation, deep risk analysis, renewal comparisons, suggested wordings and so forth. This technology could also complete broker risk research, quote sheet drafting, common query-loop handling and premium invoice validation. However, in reality, almost half (45%) of insurance companies surveyed said legacy technology systems and infrastructure were the most significant barriers to adopting digital tools and new working methods. Also, 34% admitted that legacy technology stopped them from quickly getting new products and services to market.
Until insurance companies, whether consumer or commercial, can move beyond legacy systems and infrastructure, the possible business benefits that Gen AI may drive will remain out of reach.
Considerations for retiring legacy tech
While legacy technology has undoubtedly served the markets well over the years, it is undeniably hindering the industry now. Depending on the business and area of specialization, there are different approaches to dealing with legacy.
Re-platforming is part of a larger digital transformation that will require many key decisions. Insurance companies should consider the transformation more broadly to set themselves on the right path for future adaptability. They should ask themselves various questions: How are our clients' needs evolving and how can we build customer trust while improving processing times? What are the relevant industry trends? How will our product and channel strategy evolve? How can we deliver valuable services for less cost? How can greater automation be achieved where it's needed most? And can experts across the organization receive the right tooling to improve their efficiency and effectiveness?
Likewise, when decommissioning legacy systems, it is helpful to identify the pain points that need addressing. Naturally, different departments will have varying views, whether in marketing and product distribution, channel servicing, underwriting strategy and risk selection across lines and jurisdictions, or premium booking and claims management. Modern leaders are taking more of a product management approach to navigate these complex transformations.
Another important consideration is that insurers built their organizations around the previous generation of legacy policy administration systems and claims platforms. When the system is changed, it is necessary to review not only the organizational impact but also the broader opportunities that become available with the latest generation of technology enablement.
Data also plays a critical role in replacing legacy technology and systems with new data-focused technologies like Gen AI. One of the most significant impacts of legacy systems and their surrounding environment is the challenge of integration and data flow efficiency. Given their value, there is a slow but growing recognition that data-first thinking and data products are becoming first-class considerations. However, progress remains slower than many hoped.
While companies understand the value of good data foundations, proper data stewardship and growing an ecosystem of data services, there is still not enough progress on the basics within the insurance industry, which is a naturally data-rich sector. By putting the right data capabilities in place, insurers are better positioned to take advantage of these evolving digital technologies.
Lastly, when contemplating how to modernize, converge and simplify your estate, it can be tempting to oversimplify challenges. For example, many have wondered, "Why do I need more than one policy administration system?" Multinational insurers and those in different marketplaces with varying types of businesses will firmly require specialist capabilities unavailable on a single platform. The transformation journey often requires hard, detailed thinking. Gaining some simplification and conformity in the business is a good thing, as long as it doesn't hinder the in-market essentials by misunderstanding the needs and the opportunities ahead.
Moving beyond legacy tech
Being a relatively emergent technology, the potential of this wave of Gen AI has a journey ahead, with major capabilities released every six months or so. Realizing its value, an insurance company will be more complex in changing the business process, educating users, organizing data, applying domain-specific knowledge graphs, and so forth, but the potential ahead is significant.
Insurance companies should begin experimenting and familiarizing themselves with these technologies to capitalize on future opportunities. Crucially, they must prepare the foundations of their data estates for upcoming technology-enabled business opportunities.
