AI Risk: Rising fast, but losses still lag behind

From a claims perspective, however, the current loss situation appears relatively calm. The number of clearly confirmed AI-related losses remains limited, and if assessed purely on today’s claims data, AI would not yet rank among the most significant loss drivers. This apparent gap between perceived risk and actual losses requires closer attention.

A large “grey area” exists in which AI involvement is difficult, if not impossible, to prove. In many incidents – whether cyber attacks, business decisions or operational failures – AI is likely to play a role, even though it cannot be conclusively identified. As a result, current claims data may underestimate the true level of underlying risk.

“From a claims perspective, the picture is still relatively calm – but this should not create a false sense of security,” says Michael Daum, Head of AI Claims Center of Excellence at Allianz Commercial. “In many cases, AI involvement is highly likely, even if it remains difficult to prove.”

What is already evident is that AI-related exposures are emerging across multiple lines of business simultaneously, but in very different forms. In cyber, AI is increasingly assumed to be used in areas such as phishing or malware development, even if its involvement cannot always be verified. At the same time, intellectual property disputes related to the training of large language models are gaining prominence, alongside data protection issues where AI is deployed without sufficient safeguards.

In liability insurance, risks are particularly relevant in connection with autonomous systems and AI-supported decision-making. One of the most visible areas currently is Directors & Officers insurance, where so-called “AI washing” – the overstatement or misrepresentation of AI capabilities – is already leading to claims. In addition, AI is significantly enhancing the effectiveness of fraud schemes, from business email compromise to advanced social engineering and voice-based attacks.

“AI is not a risk that is limited solely to certain business areas and applications,” Michael Daum explains. “It is emerging across many insurance products simultaneously – often not as the direct cause, but as an aggravating factor.”

Looking ahead, there are strong indications that AI-related losses will increase substantially. AI acts as a powerful risk accelerator, with the number of incidents and related litigation rising globally. External observations point to a clear upward trend.

What sets AI apart from many traditional risks is its probabilistic nature. Errors are not exceptions but an inherent feature of the technology, even when systems are properly designed and implemented.

When such systems are deployed at scale and embedded into everyday processes, even a small error rate can translate into a significant number of incidents within a short period of time. Combined with the speed of deployment, evolving governance frameworks and the widespread use of AI tools across organizations, this creates a risk environment in which losses can escalate rapidly once they begin to materialize.

“AI fundamentally changes how risk behaves,” says Michael Daum. “Unexpected output and errors are not an exception, they are inevitable. When not addressed and managed properly, even small deviations can lead to significant losses quickly.”

At its core, AI risk operates on several levels. One lies within the technology itself. AI systems often generate outputs with a high degree of apparent confidence while remaining sensitive to small changes in input and prone to various forms of bias. This can have significant implications in areas such as decision-making, risk assessment or negotiation.

A second level arises from the way AI is implemented within organizations. While experience with AI continues to grow, many companies are still developing the necessary governance, controls and expertise, often under considerable pressure to adopt the technology quickly. Even under responsible use, the probabilistic nature of AI means that errors cannot be fully eliminated.

A third level relates to indirect effects. In many cases, AI is not the direct cause of damage, but rather a force multiplier that enhances existing risks by making attacks more convincing and fraud more effective.

Against this backdrop, it becomes clear that AI risk is not purely a technological issue. It is a business, governance and leadership challenge that requires a holistic approach. The fact that losses remain comparatively low today should not lead to complacency. Historically, losses tend to lag behind adoption – but once they emerge, they can develop quickly and at scale.

Effective risk management therefore starts with a clear understanding of the fundamental characteristics of AI and their translation into concrete business risks. Organizations need to make deliberate decisions about which risks can be mitigated, which can be accepted, and which should be transferred. At the same time, the opportunities offered by AI remain significant. Realizing these opportunities sustainably depends on addressing risks in a transparent and proactive manner.

“AI offers enormous opportunities,” Michael Daum concludes. “But those opportunities can only be realized sustainably if risks are understood, addressed early and managed proactively.”