Marcus Briggs Podcast 22: Inflated Feasibility Study Used to Misrepresent the Economics of a Southeast Asian Gold Development Project

In this episode, Marcus Briggs examines an inflated feasibility study fraud case where optimistic assumptions across multiple inputs were used to misrepresent project economics.

Welcome to today's episode, everyone. I'm your host, and today we are talking about the document that determines whether a mining project is worth building. The feasibility study. It is the foundational document that tells you whether a project can make money. It incorporates capital cost estimates, operating cost assumptions, processing recovery rates, and gold price projections. It is hundreds of pages of detailed technical analysis. And it is supposed to be the most rigorous assessment available.

But what if every single assumption in that study has been optimised to the most optimistic end of the defensible range? What if the gold price is above the consensus forecast? What if the capital costs are below what comparable projects actually spent? What if the recovery rates are at the upper bound of what the ore type could realistically achieve? No single assumption is impossible. But every single one has been set at the most optimistic end of the range, simultaneously. And the probability of all of them being correct at once is negligible.

Today we are looking at a case where a retired construction project management professional, a man with twenty-five years of experience reading feasibility studies, was invited to look at a gold project in Southeast Asia. He went through the capital expenditure estimates, the operating cost assumptions, the processing recovery rates, and the infrastructure section in detail. He had questions, but nothing that seemed unreasonable for a project at that stage. He was close to agreeing to participate in a pre-development financing round when his wife asked whether he had had the gold price assumptions checked by someone who followed that market closely.

He had assessed the study on its own terms. He had not benchmarked the key inputs against independent market data or comparable projects. That was the question that changed everything.

Let me introduce you to Dan F. He worked in construction project management for twenty-five years. Reading feasibility studies is something he has done hundreds of times. Costs, timelines, risk assumptions, sensitivity analysis. He knows what a well-constructed one looks like and he knows what corners cut look like too.

A former colleague of his had gone into resources investment consulting and invited him to look at a gold project in Southeast Asia that a client of his was developing. He thought Dan's background in large project evaluation would be useful in assessing the development numbers. Dan was happy to look.

The feasibility study was substantial. Several hundred pages. He went through the capital expenditure estimates, the operating cost assumptions, the processing recovery rates, and the infrastructure section in detail. He had questions but nothing that seemed unreasonable for a project at that stage. The gold price assumption used in the base case was on the optimistic side but not absurdly so, or so he thought at the time.

Dan told us, and I am quoting him directly: "I was being invited to participate in a pre-development financing round. The amount was meaningful to me but not ruinous. I was close to agreeing when my wife asked whether I had had the gold price assumptions checked by someone who followed that market closely. I had assessed the study on its own terms but I had not benchmarked the key inputs against independent market data or against comparable projects."

He contacted someone through a recommendation from a former client of his who worked in the resources sector. What came back changed his view entirely. The gold price used in the base case was significantly above the range that analysts covering the sector considered supportable over the project's proposed mine life. The capital cost estimates were below the range seen at comparable operations in the same region by a margin that made the projected returns implausible. The recovery rates assumed in the processing section were at the upper end of what the stated ore type could realistically achieve.

Dan told us: "No single assumption was impossible. But every single one had been set at the most optimistic end of the defensible range, simultaneously. The probability of all of them being correct at once was negligible. I did not invest."

Now let us talk about how this actually works, because this is a fraud that is specifically designed to defeat the scrutiny of technically competent reviewers.

The feasibility study is the foundational document in the development finance of a mining project. A bankable feasibility study, prepared to the standard required by project lenders and institutional equity investors, incorporates independently verified capital and operating cost estimates, metallurgical test work, infrastructure assessments, and economic modelling across a range of gold price scenarios. The credibility of the document as a basis for investment depends on the independence and rigour of the inputs that underlie it.

In the junior and mid-tier mining sector, feasibility studies are frequently prepared to less rigorous standards than those required for project finance from major lenders. Studies prepared for the purpose of attracting pre-development equity capital from private or retail investors may use the same format and terminology as bankable studies while incorporating assumptions that are not independently verified and that reflect the promoting party's interests rather than a genuinely conservative assessment of the project's economics.

The systematic optimisation of key assumptions, setting gold price, capital cost, operating cost, and metallurgical recovery each at the most favourable end of the defensible range simultaneously, is a specific and identifiable form of economic misrepresentation. No individual assumption necessarily crosses the threshold of obvious implausibility. In combination, they produce a projected return profile that would not survive contact with the stress-testing applied by experienced sector analysts or project lenders. But that combination can appear credible to a technically literate reader who does not benchmark the inputs against independent market and operational data.

Dan was a retired construction project management professional with extensive experience evaluating large infrastructure feasibility studies. His skills were directly relevant to significant portions of the mining feasibility document, including capital cost construction, project scheduling, risk identification, and infrastructure assessment. His engagement with the document was therefore genuine and competent across those dimensions.

The invitation to participate was framed around the value of his specific expertise. That framing served to activate professional confidence rather than financial caution. It positioned Dan's review as validation rather than independent scrutiny. His familiarity with feasibility methodology in adjacent sectors gave him a well-founded basis for assessing the structure and format of the document. It did not, without specific sector exposure, give him a reliable basis for benchmarking the key mining-specific inputs against the range of outcomes observed at comparable gold projects.

His wife's intervention, questioning whether the gold price assumptions had been independently benchmarked, reflects the kind of basic input verification that is routinely performed by institutional investors. Technically oriented reviewers can overlook this when focused on the methodological rigour of the document itself. A feasibility study can be well-constructed in its methodology while being fundamentally misleading in its inputs. The quality of the former does not validate the latter.

The feasibility study was a professionally formatted document of several hundred pages covering all standard sections of a mining feasibility assessment: geology and resource estimation, mining method and schedule, metallurgy and processing, infrastructure, environmental and social considerations, capital cost estimate, operating cost estimate, and economic analysis.

The economic analysis section presented a base case using a gold price that sat above the consensus forecast range published by major commodity research houses at the time the study was produced. The capital cost estimate for the processing facility and associated infrastructure was presented with a level of precision implying detailed engineering, but the unit costs used in the estimate were materially below the range documented in cost databases covering comparable processing installations in the same region over the preceding five years.

The metallurgical recovery rate applied in the economic model was at the upper bound of results from the test work reported in the metallurgy section, rather than at a central or conservative estimate as is conventional in studies intended to represent a realistic base case. The operating cost estimate similarly used staffing ratios and consumable unit costs that reflected best-case assumptions rather than typical outcomes for operations of comparable scale and location.

Each of these inputs, taken individually, fell within a range that could be argued as defensible. The pattern of their simultaneous optimisation was the identifying characteristic of deliberate economic misrepresentation rather than reasonable professional judgement.

When the investigation was conducted, they benchmarked the principal assumptions in the economic model against independent market data, sector cost databases, and publicly available performance data from comparable operating mines in the region.

First, the gold price assumption. It was compared against the consensus long-term price forecasts published by twelve commodity research houses covering the metals sector. The assumption used in the study sat above the range of all twelve forecasts at the time of the referral. A sensitivity analysis conducted using the mid-point of the consensus range reduced the project's projected returns to a level that would not have supported the investment terms being offered to Dan.

Second, the capital cost estimate. It was compared against data from cost benchmarking databases covering processing plant construction in Southeast Asia over the preceding five years. The study's estimate was below the lower bound of the comparable project range by a margin that could not be accounted for by the project-specific factors described in the study.

Third, the metallurgical recovery rate. It was assessed against the full dataset of test work results reported in the study's metallurgy section. The base case model had used the single highest recovery result from a limited programme of variability tests, rather than the average or a conservative estimate reflecting the range of results. The use of the maximum observed result as the base case assumption had no methodological justification in the study itself.

Dan did not invest. He communicated his decision and the basis for it to his former colleague, who indicated that he would raise the specific concerns with his client. No further contact was initiated by the promoting party, and the pre-development financing round was not publicly concluded.

The lesson is this. When you read a feasibility study, do not just assess whether it is methodologically sound. Assess the inputs. Benchmark the gold price against independent forecasts. Benchmark the capital costs against comparable projects. Benchmark the recovery rates against the full range of test results. A rigorous method applied to optimistic inputs produces a rigorous-looking document with misleading conclusions. The two assessments, methodological quality and input validity, are distinct. And both are necessary.

Dan's wife asked the right question. She asked whether the assumptions had been benchmarked independently. That question exposed the fraud. If you are looking at a feasibility study, ask yourself the same question. Where did these numbers come from? And how do they compare to what is actually happening in the real world?

Thank you for joining us today. If this episode has shown you that a rigorous-looking document can hide optimistic assumptions, then it has done its job. Stay curious, stay cautious, and we will see you next time.

For the full case details, findings, and original narrative, Please read the original close call case study.