Programme

Session 1 - Mining in the Trump Era

London’s capital markets are undergoing a period of significant change, shaped by regulatory reform, renewed IPO activity and rapid innovation in how markets operate. This presentation will examine the evolving landscape of the London market and the emerging signs of recovery in IPO activity, with particular consideration of the mining sector and London’s continuing role as a global centre for mining finance.

The session will explore recent reforms to the UK capital markets, including changes affecting the Main Market and AIM, and consider their implications for both existing listed companies and businesses seeking access to public capital. Looking ahead, it will also examine innovations being developed by the London Stock Exchange, including extended trading and tokenisation, and what these developments could mean for the future of London-listed issuers and the competitiveness of the UK market.

Global mining M&A is being reshaped by a new era of strategic competition for resources. Driven by geopolitical fragmentation, resource security concerns, defence spending and the energy transition, countries and corporations are increasingly treating critical minerals, defence metals and gold as strategic assets rather than simply saleable commodities. Against a backdrop of strong gold prices, central bank buying and fiscal uncertainty, the sector is seeing renewed consolidation, cross-border partnerships and sovereign-backed investment. These macroeconomic and geopolitical forces mean that control of high-quality mineral assets is becoming an imperative, driving sovereign investment and consolidation in the industry.

Session 2 - Mining in the Age of Trump 2

Session 3 - Maximising your Bang for Bucks

AI in mineral exploration is pointed at its hardest problem, predicting where the metal is, using data that is incomplete in two ways. Whole regions are never sampled, and samples are rarely fully analysed. Those gaps are usually filled during data preparation, and the model is never told which values were measured and which were filled in, so it ranks the ground as if every guess were a measurement.

This talk argues that a prospectivity map must answer three questions: where, how sure, and how do we know. A model aware of what has not been measured can leverage this information to rely more on other data sources, and rank areas by the cheapest decisive test rather than by probability and certainty. For those funding a program, this can turn a map into a series of small, early ways to be wrong instead of one expensive way at the end, and leave agentic AI where it belongs: between the contractors rather than at the drill target.

Public Reports under the South African Codes and Johannesburg Stock Exchange Listings Requirements disclose an uncertain future mining business, not merely technical information. Although the Codes prescribe standards, guidelines and recommendations under the principles of transparency, materiality, competence and reasonableness, their effectiveness depends on decision-useful disclosure of the physical and economic mining business pursued This paper considers how compliance in substance may be assessed in Public Reporting practice.

This paper proposes the Real Mine construct as a benchmark for this assessment. It links the corporate objective, the Mineral Asset in its physical and market context, and the mining business investors are asked to support. It tests whether the Public Report communicates that relationship through four lenses: absolute physical configuration that reflects the corporate objective, relative economic evaluation, mining value chain confidence fundamentals, and earned maturity.

An empirical review of Competent Persons’ Reports and equivalent Public Reports in a regulated setting indicates recurring gaps between disclosure in form and substance. These include weak explanation of the mining method footprint required to meet the corporate objective, uneven Modifying Factor evidence, weak estimate accuracy disclosure, limited risk-proportional treatment of uncertainty, and evaluation assumptions not consistently aligned with the financing cost, maturity and confidence of the reported case.

The Real Mine construct therefore offers Competent Persons, Competent Valuators, issuers and reviewers a practical benchmark for testing whether Public Reports move beyond compliance in form to disclose the mining business and real risk investors are asked to assume.

Russia's engagement across Africa has grown steadily, blending resource access, diplomatic realignment, and a push to counter Western influence. This presentation examines Moscow's objectives and how it exerts influence — from private military contractors to more direct military engagement — and assesses the resulting risks for international operators and mining entities, including security, regulatory, and reputational exposure. It closes with the practical cost of operating in these environments and approaches mining companies can take to counter the threat.

Session 4 - The Importance of Technical and Commercial Communications 

Many investors assume that a mineral project reporting “Measured” or “Proved” is a guarantee of low risk and high return.

A point frequently ignored is the difference between a Mineral Resource and a Mineral Reserve. Mineral Resources have the potential to be economically viable; Mineral Reserves are the economically viable portion of a deposit after the application of modifying factors.

The CRIRSCO reporting codes that were introduced post Bre-X are designed to deliver transparent public reporting, not to eliminate risk or uncertainty. Their purpose is to ensure that material information, based on reliable data and reasonable assumptions, is reported in a transparent manner so that an investor (and their advisors) can judge risks and potential. The investment decision remains with the investor.

The degree of risk and uncertainty associated with a Mineral Resource is by definition higher than that for a Mineral Reserve — directly affecting project NPV, IRR, payback period and risk (up and downside).

A geological model is based on the Competent Person’s interpretation of the data available to them. So while the CRIRSCO codes require confidence in quantity, grade, densities, shape and physical characteristics for detailed mine planning, the nature of the beast is that “the other stuff” — colour variations, presence of clay and obvious deleterious parameters (boiler ash in a tailings dam that is to be reprocessed) — may be noted in the “raw data”, but is frequently absent from the final block model.

Two theoretical cases, albeit based on reality, illustrate the financial consequences of data attrition.

Example 1: In a tailings reprocessing project, dumps were declared at the Measured level of Mineral Resource confidence. No metallurgical test work was conducted because “all tailings are the same”. Colour variations in the tailings material were observed, as was the presence of charcoal; however both were ignored in the MRE. The colour variation was observed as material calcined to produce sulphuric acid, resulting in the occlusion of remaining gold that was planned to be leached in a Carbon in Leach (CIL) plant. The presence of refractory gold as well as charcoal resulted in material under-recovery of gold, resulting in the project being abandoned.

Example 2: A restarted underground gold project had the kriged estimates replaced by optimistic “grade trends”. The mining modifying parameters were overtly optimistic — all historical achievements were ignored, based on the premise that “we are a new management team and will not make the same mistakes”. When the mine went into operation (with a Measured Mineral Resource and Proved Mineral Reserves), it severely underperformed against the base case, was refinanced three times, until it finally closed.

These examples are not compliance failures but failures of systems thinking. Mining projects are complex adaptive systems where small, early “positive tweaks” can result in subsequent project failure — the Butterfly Effect in action. The reasons why these early stage decisions are made are multifaceted and can be analysed through a variety of behavioural theory models such as cognitive bias (e.g. optimism bias), agency theory, economic signalling, fear of missing out (FOMO) and the Greater Fool theory.

As an investor, the answer is simple, though tedious — know your market, read the full Technical Report (not just the Executive Summary), verify that modifying factors have been stress-tested — not assumed — and that the Competent Person has the relevant experience. Is a Measured Resource classification optimistic? Should you, for valuation purposes, downgrade the confidence classification and assign a higher discount factor?

The application of CRIRSCO standards has improved the quality and transparency of public disclosure; however it must be remembered that they remain minimum standards.

The best defence for capital remains the informed investor who understands both the rules and their limits.