Why Fijishi

The question

When an AI system decides a claim, a price, an application or a fraud flag, someone may later ask three things about that one decision: which rule did it meet, who was accountable for it, and who could have stopped it. The person asking may be a regulator, a court, a customer's lawyer, an auditor or a reinsurer. More and more, the answer has to be evidence about the decision itself, not a policy document.

Why now

The Fijishi Jurisdiction Index 2026 examines AI in insurance decisions in 38 entries across 34 jurisdictions. In 12 entries, AI-specific rules are already specified and enforced. In 14 entries, the binding rule reaches reinsurers directly. Each entry ends with one sourced question that the public record does not yet answer. The Regulatory Deadline Calendar lists, with sources and dates, the rules in force, the rules coming and the drafts still pending.

What already exists, and what each covers

A great deal of serious work already exists, and a firm may need several pieces of it. Each covers something real, and each describes its own scope:

  • Management-system standards – ISO/IEC 42001, with ISO/IEC 23894 on AI risk management and ISO/IEC 42005 on AI impact assessment, certified by accredited bodies under ISO/IEC 42006. A certificate shows that an organisation manages AI through a documented system that operates as documented.
  • Risk frameworks and principles – the NIST AI Risk Management Framework and the OECD AI Principles guide how organisations identify and manage AI risk. They are voluntary and are not certifications.
  • Harmonised standards – CEN and CENELEC are developing the European standards that will support the requirements of the EU AI Act.
  • AI system and agent assurance – AIUC-1 for AI agents, Singapore's AI Verify testing framework, the IEEE 7000 series and IEEE CertifAIEd, and testing by certification bodies examine how a system behaves or how it was designed, against their own criteria.
  • Algorithmic and ethics audits – schemes such as ForHumanity's, and audit firms such as BABL AI, audit systems against defined criteria, often for a specific law such as New York City's bias-audit rule.
  • Governance software – platforms such as Credo AI and Holistic AI help firms run AI inventories, policies and assessments.
  • Public AI safety and security institutes – such as the UK AI Security Institute and the US Center for AI Standards and Innovation – evaluate the capabilities and risks of advanced models.
  • AI insurance – insurers that cover AI performance and liability take on part of the financial risk.

What Fijishi answers

Fijishi answers the question that comes after these: for one named AI decision, in one jurisdiction, does it meet the rule that binds it – and can the firm show who was accountable and who could stop it, in a way that would hold up before a regulator, a court or an insurer?

A firm can hold a management-system certificate, run a risk programme and test its models, and still be asked that question about a single decision. A Fijishi finding is built to sit alongside that work. It uses the firm's existing records as evidence, names the rule it measures against, and states the result in words.

Fijishi does not replace any of the work above, and does not compete with a firm's own auditors or counsel.

How we work

  • Every finding names the rule or standard it measures against.
  • The fee is fixed at intake, before any work begins, and does not depend on the finding.
  • Where an engagement produces a finding, the client owns it.
  • Fijishi does not certify a system it helped design or build, and does not accept a fee from the vendor whose contract it reads.
  • Our Index method and our draft criteria are published, corrections are public and dated, and companies named in the Index have a right of reply.
  • See an illustrative finding (hypothetical; not a client engagement).

Who does the work

Fijishi is led by its two founders, Akhil Sharma and Preethi Sharma, and every finding is examined and signed by both of them. There is no hand-off to junior staff and no sales layer between a firm and the people doing the work; the fee pays for their time. Fijishi does not design, build, sell or implement AI systems, so no other part of its business depends on what a finding says. We take a limited number of engagements at a time, so that both founders work on each one, and we aim to start within two weeks of agreed scope.

Where this is going

Certification against named rules comes first: it is how criteria are tested on real systems. Alongside it, we publish our own draft criteria and develop them in public – a working draft, then public comment, then a numbered version that can be tested in a certification, then use. Early testing laboratories and rating agencies became references others rely on in the same way: by measuring, publishing and correcting in public, until their work was used.

Our drafts are at the first stage, and none is yet a standard. The first, on who can stop an AI decision, will be the first we open for public comment. The firms that use our findings, and the people who comment on our drafts, are the ones who will make them standards – or show us where they fall short.