Making AI Risk Controls Count in Insurance
Connecting AI assurance with underwriting: a proposal for insurance in the AI era
Companies that invest in safer AI operations should be able to have those efforts meaningfully assessed in insurance. We want to explore how to make that possible, together with insurers.
GhostDrift Mathematical Institute is developing a proposal to connect verifiable evidence about AI operations with insurance underwriting.
The goal is not an automatic premium discount for installing a particular technology. It is to establish which controls can be shown to work, in which operations, and within what limits — and to make that information useful to insurers assessing risk and considering coverage terms.
A stronger basis for underwriting AI risk
Insurance solutions addressing AI risk already exist. Munich Re, for example, provides AI insurance solutions through aiSure™ and explains that model quality and performance stability influence premiums. Connecting technical assessment with insurance is already a commercial reality.
The need for better underwriting information is also reflected in Japan. An April 2026 report published by Japan’s Financial Services Agency and Ministry of Economy, Trade and Industry calls for more sophisticated underwriting. It identifies limited historical loss data as an obstacle to evaluating and underwriting emerging risks, including AI and IoT risks.
Our proposal is not to replace historical loss data or existing risk assessments. It is to complement them with verifiable evidence about controls and operating practices where AI is actually used.
Installing a control is not the same as demonstrating that it works
Consider a logistics operation in which AI proposes delivery plans.
Alongside statements such as “we have a safety policy” or “we have installed monitoring,” what if an insurer could examine evidence showing whether processing was held when required information was missing, whether execution followed the required approval, and whether the assessed controls still apply to the system currently in use?
The insurer could consider not only whether controls exist, but what has been established about them — and what remains unverified.
In this article, AI assurance means examining the operation and limitations of measures intended to support safe and appropriate AI use, and turning that examination into evidence for decisions.
NIST’s AI Risk Management Framework addresses assessment under conditions similar to deployment, monitoring of systems in production, and documentation of the limits of generalizing beyond evaluated conditions. This supports an approach that goes beyond a one-time check at deployment.
It is this kind of assessment result that we want to connect with insurance. Evidence about one part of an operation should not be presented as a guarantee that the entire AI system is safe. Our objective is to preserve that distinction while making the findings useful to underwriters.
The opportunity for insurers extends beyond discounts
Premium reductions are not the only potential outcome we want to explore.
One starting point is to clarify what information would support an underwriting decision and what is currently missing. Where insufficient evidence prevents a decision, we want to examine whether the additional checks needed can be made more explicit.
Another is to support understanding of operations during the policy period. When a system or its use changes, relevant evidence could help determine which earlier assessments remain applicable.
Over time, we also envisage supporting factual review following an incident. However, the proposal is not to determine causation, legal liability, or claim entitlement solely from the presence or absence of records.
Our role is to support the quality of evidence available to insurers — not to replace underwriting judgment or specialist assessment.
A cycle in which risk improvement can be recognized
A company invests in controls. Their effectiveness becomes more demonstrable. An insurer evaluates the relationship between that evidence and the relevant loss exposure. The assessment can then inform the availability and terms of coverage.
We believe such a cycle could help position AI risk controls not only as a cost, but also as an investment that supports business activity.
However, more verifiable information is not the same as a demonstrated reduction in loss frequency or severity. Joint work would need to examine the relationship between the controls evidenced and actual loss risk, and connect the findings with actuarial analysis and underwriting practice.
That work could ultimately support improvements in premiums, deductibles, coverage limits, or other terms. We are not promising a particular discount. Nor do we propose certifying an entire company as “safe”: assessments should relate to defined operations, systems, and the scope that has actually been examined.
An invitation to insurers, reinsurers, and brokers
GhostDrift Mathematical Institute has filed a Japanese patent application concerning verification technology related to this proposal: Japanese Patent Application №2026–210256.
Our next objective is to explore concrete use cases with insurers, reinsurers, and insurance brokers.
A practical starting point would be a defined workflow, such as a logistics operation. Together, we would identify the technical questions that matter to underwriting and examine how evidence from operations could support those decisions. The findings could then inform integration with existing underwriting processes or the development of new insurance offerings.
This article is not a product-launch announcement. It is an invitation to jointly explore a connection between AI assurance and insurance. Technical details would be addressed in individual discussions subject to confidentiality arrangements.
We want to help build a future in which businesses can pursue AI-enabled opportunities while managing risk — and insurers can understand and support those efforts.
Primary sources and public materials
Japan’s Financial Services Agency and Ministry of Economy, Trade and Industry, report of the study group on enhancing corporate risk management, April 17, 2026
Particularly Section III-2, on underwriting, risk assessment, and limited loss data for emerging risks. Japanese-language publication.
Munich Re, “Frequently Asked Questions — aiSure™ / Insure AI”
Official information on AI insurance, technical risk assessment, and the relationship between model quality, performance stability, and premiums.
https://www.munichre.com/en/solutions/for-industry-clients/insure-ai/faq.html
NIST, “Artificial Intelligence Risk Management Framework (AI RMF 1.0),” January 2023
Particularly MEASURE 2.3–2.5, covering deployment-relevant assessment, production monitoring, and limits on generalizability.
IAIS, “Application Paper on the supervision of artificial intelligence,” July 2, 2025, final version
Supplementary reference on insurers’ own use of AI, governance, accountability, and record keeping. It does not directly address underwriting risks arising from AI use within insured businesses.
These materials are cited for background context and do not imply endorsement, approval, or partnership by any of the organizations referenced.
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