Andrea Tesei
Co-Founder & CEO @ Aptus.AI
Article written by Aptus.ai as part of the sponsorship of ACA Insurance Days 2025, whose content is the sole responsibility of its author.
The Role of Reliable AI in the Insurance Sector
Artificial intelligence is often discussed in compliance primarily through the lens of speed and costs. Faster searches, quicker answers, and real-time alerts are frequently presented as the main sources of value. While speed can undoubtedly improve operational efficiency, it is not the defining challenge for compliance functions in the insurance sector.
In insurance and reinsurance, the consequences of regulatory and legal decisions are often long-term. Errors may surface years later, during supervisory reviews, claims management, or cross-border disputes. In this context, speed without reliability does not reduce risk—it amplifies it.
The real question for insurers is therefore not how fast compliance information can be produced, but how reliable, explainable, and defensible that information is over time.
Compliance in the insurance sector presents structural characteristics that differentiate it from many other financial activities.
First, insurance undertakings operate with long-term liabilities. Regulatory interpretations applied today can have effects well beyond the immediate reporting cycle, influencing product design, reserving practices, claims handling, and governance decisions years into the future.
Second, particularly in Luxembourg, insurance and reinsurance activities are inherently cross-border. Passporting regimes, group structures, and international client bases require firms to navigate multiple legal and regulatory frameworks simultaneously. Regulatory obligations are rarely confined to a single jurisdiction, and their interaction often requires careful contextual interpretation.
Finally, insurance supervision is not limited to one-off compliance checks. It is based on ongoing supervisory dialogue, where firms are expected to justify their interpretations, demonstrate governance processes, and provide traceable reasoning behind key decisions.
These characteristics place a premium on accuracy, consistency, and explainability—qualities that cannot be compromised in the pursuit of speed.
Despite increasing regulatory complexity, many compliance functions still operate within a predominantly reactive framework. Regulatory changes are identified after publication, assessed under time pressure, and implemented as isolated adjustments to existing processes.
This model presents clear limitations:
As a result, compliance risks becoming a “firefighting” function—focused on addressing immediate issues rather than supporting informed, forward-looking decision-making.
The growing use of AI in compliance has intensified a critical trade-off: speed versus reliability.
Fast outputs that are based on incomplete sources, lack jurisdictional specificity, or cannot be traced back to authoritative references may appear efficient in the short term. In regulated insurance environments, however, they introduce new forms of risk—particularly when outputs must later be explained to auditors or supervisors.
Reliability in compliance requires:
Without these elements, speed becomes a false economy. A slower, well-founded answer is often far more valuable than a rapid but opaque one.
When designed with reliability as a core principle, AI can play a meaningful role in transforming compliance from a reactive obligation into a proactive governance function.
Rather than simply accelerating responses, reliable AI systems can support:
Importantly, such systems do not replace professional judgment. Instead, they strengthen it by providing compliance professionals with clearer, more structured, and more defensible information.
This approach aligns closely with the insurance sector’s broader risk management culture, where transparency, accountability, and control are central values.
As regulatory expectations continue to evolve, insurers face a strategic opportunity. Compliance can remain a reactive function focused on meeting minimum requirements, or it can become an integral part of governance and decision-making.
Reliable AI makes the latter increasingly achievable. By improving the quality, consistency, and explainability of regulatory information, it enables insurers to anticipate regulatory implications earlier, support strategic choices with greater confidence, and reduce long-term risk exposure.
In a sector built on managing uncertainty, proactive and well-governed compliance is not a cost—it is a source of resilience.
The future of compliance in the insurance sector will not be defined by speed alone. It will be shaped by the ability to combine technological capabilities with reliability, context, and accountability.
AI has a role to play in this evolution, but its true value lies not in faster answers, but in better ones. For insurers operating in complex and international regulatory environments, this distinction is critical.
Doing compliance faster may feel efficient.
Doing compliance reliably is what makes it sustainable.
Author note
This article reflects the perspective of Aptus.AI, a legal-tech company working at the intersection of regulation, technology and compliance in Luxembourg, and a proud member of ACA.
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