Ryan Cunningham, CEO of American Mineral Resources, provides an analysis of how AI driven risk engines are reshaping mining finance by reducing settlement risk, improving transparency, and helping producers unlock working capital through faster, more reliable transaction verification.

Every few years the mining industry gets handed a new piece of technology and told it will change everything. Most of the time it changes a few things. Sometimes it changes the wrong things. The current wave is AI, and the pitch is familiar: safer mines, fewer incidents, better recoveries, and cleaner data. All of that is real, and all of it matters. But it’s not the most interesting question AI is going to answer for this sector.

The interesting question is whether AI can finally do something about settlement risk.

Settlement risk in mining is the quiet tax nobody talks about. Between the moment a tonne of concentrate leaves a mine and the moment the producer actually gets paid for it, an extraordinary number of things have to line up: assays, shipping documents, weights and moisture, treatment and refining charges, price participation clauses, offtake terms, financing covenants, and a chain of counterparties who all need to trust the same numbers. When any of those links fails, working capital gets trapped, financing costs rise, and disputes can drag on for quarters. For mid cap and junior producers, that drag isn’t a rounding error. It’s a strategic constraint.

AI powered risk engines, particularly the ones being piloted on Canadian copper, gold, and critical mineral projects, are starting to attack this problem from a different angle. Instead of bolting a model onto one part of the workflow, they ingest the full data picture: drillhole assays, mill performance, fleet telematics, environmental monitoring, lab results, logistics events, and offtake terms, and continuously score the operational and counterparty risk of every shipment in motion. When something drifts outside expected parameters, it flags before it becomes a dispute. When the data holds, the settlement clears faster.

The reason this matters is that AI is most credible where it can be checked. A model that predicts ore grade is only as good as the next reconciliation. A model that scores settlement risk gets graded every time a payment clears or doesn’t. That feedback loop is the difference between a science project and an actual operational tool.

There are real limits. AI doesn’t fix bad assays, it just surfaces them faster. It doesn’t make a counterparty solvent. It doesn’t replace independent verification: JORC, NI 43 101, qualified persons, lab certifications, etc. And the IEA is right to flag that the energy and infrastructure load of running these systems at scale is non-trivial. None of this is free.

But the direction is clear. The operators who pair AI risk engines with verifiable, on chain reporting of production and shipment data are going to settle faster, finance cheaper, and dispute less. That’s not a marketing story. It’s working capital, and working capital is the thing that determines whether a mid-cap producer can fund the next stage of growth out of cash flow or has to dilute shareholders to do it.

For investors, the signal to watch isn’t which miner has the flashiest AI press release. It’s which ones are publishing the metrics that let outsiders verify the system is doing what it claims: settlement cycle times, dispute rates, reconciliation accuracy, working capital turnover, etc. Those are the numbers that separate AI as a tool from AI as a talking point.

Mining has always rewarded operators who get the boring details right. AI doesn’t change that. It just makes the boring details harder to hide.

Read the article online at: https://www.globalminingreview.com/mining/08062026/ai-promises-safer-mining-but-can-it-cut-settlement-risk/