What is OTRM? Operations Trade Risk Management for Commodity and Energy Trading

Commodity and energy trading is operationally complex by nature. A single trade can move through multiple teams, systems, documents, counterparties, physical events, and commercial obligations before it is fully settled.

Commodity or Energy Trading and Risk Management (C/ETRM) platforms have become fundamental to how the industry understands its finances and manages market risk. Much harder to modernize has been the operational work that every trade entails. Across commodities operations, much of it still relies on spreadsheets, emails and manual reviews, with institutional knowledge patching disconnected workflows.

The resulting process bottlenecks and data gaps have direct consequences. Operational risk is factored into the business as far as possible, but even known issues can be difficult to control through disjointed practices. Other problems go by unseen, obscured by high volumes of fragmented data. As trading ecosystems grow in scale and complexity, the costs become harder to absorb: team pressure rises, policy and compliance failures become more likely, and margin can be eroded by discrepancies found too late.

Chartis Research highlights this problem in its paper on controlling operational risk and revenue in a volatile commodities ecosystem. Its commodities specialists point to multiple areas of complexity: the movement of physical trades through disrupted supply chains, convoluted contracts, extensive documentation, varied and changeable legal and regulatory situation, and error-prone manual document processing.

The confluence of these complexities is the environment in which Operations Trade Risk Management, or OTRM, is emerging.

OTRM Defined: A New Layer of Operational Intelligence for Commodity Trading

OTRM is a new technology category for commodity and energy trading operations. It focuses on the operational activity triggered by each trade: the data, documents, events, reconciliations, and handoffs that determine whether that trade is executed cleanly.

Combining commodity-specific agentic AI, smart interfaces, and a shared data foundation, OTRM coordinates operational workflows across the trade lifecycle – from trade capture and trade confirmation through logistics, finance, reconciliation, payment, and compliance. It works alongside C/ETRM systems, which remain central to portfolio management, valuation, positions, exposure, and trade risk.

Chartis defines OTRM as a parallel operational intelligence and operational automation layer, driven by agentic AI, that sits alongside the C/ETRM cycle. This framing explains why OTRM is integral to a broader technology architecture that enables the industry act with comprehensive oversight and control over the complete trade lifecycle.

Operational Automation Alone Is Not Enough: Why Connected Workflows Define OTRM

Automation of a task, or workflow composed of a sequence of tasks, can deliver meaningful gains. Invoice matching can be more accurate. A confirmation workflow can move faster. Document reviews demand less manual effort. For operations teams dealing with high volumes of repetitive work, such improvements matter.

The larger change comes when those workflows are connected. Data captured at trade entry can affect confirmation. Operational events can affect exposure, pricing, cash flow, and revenue. A document used in one process may become the evidence needed to resolve an issue elsewhere. This is what defines OTRM – a shared data foundation that enables automation across the entire trade lifecycle.

Automation that is limited to specific tasks or workflows keeps the benefits local, but when multiple automated workflows are connected the value compounds. Data captured in one process is instantly available to another. Exceptions can trigger follow-on actions. Operational events can be assessed in the context of detailed and continuously updated data.

This is the basis for treating OTRM as its own technology category:

• Functionally, it creates a connected operational environment where execution data can move across systems and teams with context attached.
• Organizationally, it means avoided risk, reduced losses, and a more agile, scalable operations organization.
• Strategically, it elevates the value of operations by established a level of data visibility, comprehensiveness and trust that was not previously possible. Firms gain deep, real-time insights that help them to rebalance risk assessments and optimize revenue outcomes, ranging from real-time responses to unfolding events to the development of long-term commercial strategy.

Why Commodities Operations Need OTRM Now

Commodity and energy trading firms understand the limits of manual effort and institutional knowledge. Operations leaders have learned to work within those limits, often with considerable skill. The wider business has also absorbed an accepted level of operational friction, risk and cost.

The pressure on that model is increasing. Market volatility, shifting sanctions, documentation requirements, fragmented data, and constrained team capacity all make manual coordination harder to sustain. At the same time, the consequences of late or incomplete information are becoming more material. A delayed document can affect settlement. An overlooked contract term can affect commercial options.

The possibility to counter that pressure comes from advances in AI and workflow automation. Firms can now automate work that was previously too variable, document-heavy, or context-dependent for conventional tools. Agentic AI is especially important because it can work through variability, context, and data gaps while still operating within controlled workflows.

This is why OTRM is becoming relevant right now. Advances in technology have allowed long-standing operational problems to be addressed at a time when the cost of leaving them unresolved is rising.

How OTRM Changes Commodities Risk Management and Revenue Outcomes

For operations leaders, OTRM addresses familiar pain points across contracts, confirmations, inventory, invoicing, counterparty checks, finance, and payments. As more manual work is automated, teams spend less time chasing information and more time resolving exceptions, improving processes, and supporting better decisions.

As that change in working practices becomes embedded, the role of operations can evolve. The organization remains responsible for execution processing, but it also becomes a source of rich, trusted data that gives leaders more tactical control and a sounded basis for strategic planning. Better operational data can help reveal where risks are forming, where value is leaking, and where commercial action makes the difference between profit and loss.

The business impact follows from that shift. OTRM improves control by giving teams better visibility into operational status and exceptions. It supports scale by allowing capacity to grow without the same linear increase in manual workload. It reduces risk by identifying discrepancies earlier. It supports revenue optimization by aligning data sooner, surfacing contractual obligations and options, and enabling action before delays or discrepancies become financial losses.

For the C-suite, OTRM represents a new enterprise-scale capability. It connects operational execution to risk mitigation and commercial performance. This is much more than a reduction in manual work: OTRM gives the commodity and energy trading industry the means to evolve more efficient, resilient and profitable business models.

Read the Chartis Paper

Chartis explores this shift further in OTRM and Agentic AI: A Perfect Match. The paper explains why energy and commodities operations are well suited to agentic automation: they are event-driven and structured around contractual triggers, obligations, and decision points, yet difficult to manage with conventional automation because of unstructured data, varied formats, contextual judgement, and information gaps.

Firms that invest in this capability can move toward low-touch, highly controlled operations, with better visibility of contingent events, revenue leakage, commercial options, and risk exposure across the trade lifecycle. Those slower to embrace the possibilities may still see gains from automation, but will lack the force multiplier of data connectivity and its strategic possibility to update their business model.

For a deeper view of how OTRM is changing commodities and energy trading – and why agentic AI makes this possible – read the Chartis paper.