How Do We Document AI Decision Rationale for Audit?

AI tools are increasingly involved in bordereaux processing, flagging anomalies, suggesting field mappings and highlighting validation issues for review.

As that involvement grows, so does a question that many delegated authority teams have not yet answered clearly: what exactly needs to be recorded when an AI tool contributes to a decision?

Internal audit, Lloyd's oversight teams and regulators do not just want to know that a decision was made. They want to know why, and who was accountable for accepting or overriding it.

Without a clear practice for capturing that rationale, organisations cannot reconstruct decisions after the fact. That is an audit and regulatory exposure that is entirely avoidable with the right documentation habits.

Why AI-assisted decisions need their own audit trail

Traditional audit trails were built around manual decisions. A person reviewed a bordereau, spotted an issue, made a judgement and recorded it, often in a sign-off log or exception register.

When AI contributes to that process, a new element enters the chain. The AI produces an output, such as a flagged inconsistency or a suggested mapping, before a human ever sees it.

If the record only shows the final human decision, it misses an important part of the story: what the AI actually suggested, and whether the human agreed with it, adjusted it, or overruled it entirely.

That gap matters because auditors and regulators are increasingly interested in exactly this point. They want to see that AI-assisted decisions carry the same, or greater, scrutiny as manual ones. A record that cannot show this leaves the organisation unable to demonstrate that human oversight was genuinely applied, rather than assumed.

How organisations have documented decisions before AI

Delegated authority teams already have established ways of recording decisions. Sign-off logs capture who approved a bordereau and when. Exception registers track issues identified during processing and how they were resolved. Reviewer notes provide context for judgement calls that do not fit neatly into a standard field.

These practices work well for manual decisions because the reasoning and the decision-maker are usually one and the same. The person who spotted the issue is generally the person who resolved it, and their notes reflect a single continuous thought process.

The limitation is that these formats were not designed with a second, non-human contributor in mind. They typically have no dedicated place to record what a system suggested, only what a person concluded. Extending these practices to cover AI involvement, rather than replacing them, is usually the right approach.

What good AI decision rationale documentation looks like

Good documentation of an AI-assisted decision captures three connected elements.

First, the AI's output: what was flagged, suggested or mapped, along with any confidence indicator or basis the tool provides, where available.

Second, the human reviewer's decision: who reviewed the output, and what they decided to do with it, whether that is accepting, adjusting or dismissing it.

Third, the rationale: a short, specific explanation of why that decision was taken. This is the element most often missing. It is not enough to record that a flag was "dismissed". The record should show why it was dismissed, in language a reviewer unfamiliar with the case could understand.

This is the central distinction to hold onto: logging AI output is not the same as documenting decision rationale. A system log can show what the AI produced. It cannot show why a human agreed or disagreed with it. Both pieces need to exist together, and the rationale needs to be captured by the reviewer at the point the decision is made, not reconstructed weeks later from memory.

AI tools can help lighten this burden by pre-populating structured fields, prompting reviewers for a reason when a suggestion is overridden, or summarising the flagged issue in plain language. What AI should not do is generate the rationale itself. The judgement, and the accountability for that judgement, needs to remain with the reviewer.

Operational considerations for implementation

A few practical decisions determine whether this documentation practice becomes genuinely useful or simply another administrative burden.

Storage and retention should align with existing bordereaux and audit retention policies, rather than creating a separate system or timeline to manage. If bordereaux records are retained for a set period, AI-related decision records should follow the same schedule.

Responsibility should be clear. The team member making the reviewing decision is usually best placed to capture the rationale, since they hold the context at that moment. A governance or compliance function should define what "sufficient" rationale looks like and periodically check that the practice is being followed.

Proportionality also matters. Not every AI suggestion carries the same weight. Material exceptions, particularly those where a human overrides an AI flag, warrant a fuller rationale. Routine, low-risk suggestions may only need lighter-touch capture. The aim is a defensible record, not an exhaustive one, and guidance should help staff distinguish a genuine rationale from a tick-box entry that adds no real value if the decision is ever questioned.

Example

A managing agent's oversight team uses an AI tool to flag inconsistencies in a monthly bordereaux submitted by an overseas MGA writing agricultural risk. The AI flags three claims records where reported currency codes appear inconsistent with the binder territory.

A DA analyst reviews the flags, confirms two are genuine errors requiring correction, and dismisses the third as a valid multi-currency arrangement permitted under the binder.

The analyst records the AI's original flags, the decision made on each, and a short rationale for the dismissal. Six months later, during an internal audit review, the audit reviewer can reconstruct exactly why the third flag was dismissed without needing to contact the analyst, because the rationale was captured at the time of decision.

FAQs

• Do we need to document every AI suggestion, even ones that are ignored?

  In principle, yes, particularly where the AI flagged something that a human then dismissed, since overrides are often the decisions auditors scrutinise most closely. Proportionality still applies: routine, low-risk suggestions may warrant lighter-touch capture, while material exceptions deserve a fuller rationale.

• Is it enough to keep the AI system's own logs as our audit trail?

  No. System logs typically capture what the AI produced, but not the human rationale for the decision taken afterward. A defensible audit trail needs both elements together: the AI's output and the reviewer's reasoning for accepting or overriding it.

• Who is responsible for maintaining this documentation?

  Responsibility typically sits with the team member making the reviewing decision, since they hold the context at the time. Oversight for defining what must be captured, and for how long, usually sits with a governance or compliance function.