AI-powered covenant reporting for private lenders, and how it works.

AI-powered covenant reporting is the use of AI agents to test a borrower's covenants each period and report the result. The agents read the financials and the compliance certificate, recalculate each covenant from the facility agreement, and report headroom or breach. They then write up the result for an analyst to review and sign off. Done well, it is faster than a spreadsheet and harder to get wrong.

This guide is written by Levercon, which provides AI-powered covenant reporting as part of portfolio monitoring for private lenders in Australia. It answers for credit and operations teams at private credit funds and non-bank lenders.

What AI-powered covenant reporting does.

Covenant reporting is the part of the monitoring cycle that credit committees, investors and co-lenders read most closely. In Levercon's version, the work runs in five steps.

1. Spread the financials. The borrower's accounts are read in whatever format they arrive and spread into the lender's fields, each figure linked to its page.
2. Recalculate every ratio. Leverage, interest cover, gearing and LVR are calculated from the facility agreement's own definitions.
3. Test every covenant against the register. Each result is reported as headroom or breach, and a cure is sized where one is needed.
4. Check the compliance certificate. The borrower's certificate is compared with the facility agreement and the lender's own calculation, and any disagreement is flagged.
5. Write it up for sign-off. The result goes into the monitoring note in the lender's template, with each statement cited, and is routed to an analyst.

Why recalculating beats reading the certificate.

A compliance certificate is the borrower's own statement that it is in compliance. Most covenant tracking simply records what the certificate says. That catches nothing when the certificate is wrong, because the error is copied into the lender's records.

Recalculating from the facility agreement's definitions is what catches it. An add-back the agreement does not allow, a period annualised the wrong way, or a debt figure that leaves out a facility will all show up as a gap. AI makes recalculation affordable on every loan, every period, rather than on the few the team has time to check.

Why headroom matters more than pass or fail.

A covenant that passed at 4.1x against a 4.5x limit is a different loan from one that passed at 2.0x. Pass or fail hides that difference until it is too late to act. Headroom, tracked period on period, shows which borrowers are drifting towards a breach while there is still time to talk to them. Good covenant reporting puts that trend in front of the credit committee, not just the test result.

What the record needs to show.

A reviewer, an auditor or an investor should be able to follow any reported covenant back to its source. That means the page of the borrower's accounts, the definition in the facility agreement, the calculation and who approved it. FSC Standard No. 30, which applies from 1 July 2027, is specific about that kind of record. An AI-powered process can produce the evidence every period. The approval still belongs to a named person.

How Levercon does covenant reporting.

Levercon's covenant agent tests every covenant against the lender's register, reports the headroom or the breach, sizes the cure and writes up the result for review. Its compliance certificate agent flags where the certificate, the facility agreement and the lender's calculation disagree. Every figure can be traced to the source document, model input and covenant definition behind it. Analysts approve results inside Claude, Copilot or ChatGPT via MCP.

Client data is hosted on AWS, exclusively in Sydney, and Levercon is priced per loan, per month, with pilot opportunities. In its published case study, a $500m corporate lender cut monitoring from nine hours to two per loan. For the whole cycle, read AI-powered portfolio monitoring for private credit.