Home Commodities Data and analytics firm of the year: Mobius Risk Group

Data and analytics firm of the year: Mobius Risk Group

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Data, says Eric Melvin, the founder and chief executive of Mobius Risk Group, has four conditions. “It’s unstructured, it’s siloed, it’s inaccurate and it’s latent. Whether you’re a Fortune 500 company, a private firm or an endowment, those conditions are everywhere,” he says.

“People pay an incredible amount of money for a fourth-quartile data outcome. That’s where we see an opportunity.”

Mobius, Energy Risk’s 2024 Data and analytics firm of year, is a Houston-based technology and advisory firm formed in 2002 to bring increased transparency to commodity and energy markets data. It uses technology to understand clients’ physical and financial positions, enabling them to measure, value, warehouse and report risk, facilitating strategic decision-making and enhanced operational efficiency.

Melvin describes Mobius’s RiskNet platform – its web-based multi-asset risk management platform – as more than a commodity trading and risk management (CTRM) system or a system of record.

“We’re really viewed as a system of intelligence,” he says. “What we find is that the same dataset the C-suite may be using for strategy and risk and capital decisions is being used all the way through the business – in settlements, reconciliation, accounting, invoicing, auditing, governance and so on.”

RiskNet enables users to combine that dataset with market information, meter readings, data from supervisory control and data acquisition (Scada) systems, data on counterparties and information on the firm’s exposures and hedges, and then to visualise that data in meaningful ways. “This gives our clients first-quartile outcomes for decidedly less cost,” says Melvin.

At its heart, the RiskNet system allows its users to see how cash is moving through the business, now and in the future.

“Most CTRMs give you a static view of the world,” he says. “What we do is change that to a system of intelligence and make it dynamic.”

A critical component, says Melvin, is the data that’s in commercial agreements. “Their bespoke components create so much optionality that you really need to understand. When you go to value things in the marketplace, you need to incorporate how that agreement will perform in different price environments and understand that last piece of optionality.”

He cites force majeure clauses as an example. “It’s like credit risk: it never matters until it matters, and by then it’s too late.”

Mobius negotiates thousands of such agreements for clients each year and has tens of thousands more in its systems, representing an enormous trove of data and potential insight. The company has developed a large language model (LLM) that anonymises and mines the data to feed those insights into RiskNet. “It’s early days, but the returns are interesting in terms of what we’re getting out of the process,” says Melvin. “The big challenge is to visualise, analyse and, most importantly, translate that back into peoples’ businesses. We still need people to translate that last mile.”

This approach illustrates how Mobius thinks about risk. “When you look at risk, you can’t just look at 70% of it – it’s the tail risk that will crush you.” Melvin notes that, in the energy markets, “we have two to four outsized two standard deviation-plus events every year. That suggests the market does not correctly look at risk. If you’re going to properly look at risk analytics, you need to have a comprehensive dataset, and that’s why we spend so much time with the documents.”

That approach can make a big difference to the bottom line. Melvin gives the example of a North American upstream natural gas company that was able to generate a “multi-million-dollar risk reduction and value uplift” by using RiskNet to visualise the potential outcomes of a transaction, taking into account expected cash receipts from physical sales and the impact on its risk and credit position.

“That is an example of the client understanding its commercial and financial risks involved in harvesting cash throughout its entire system,” he says. “Some of that was in the optionality in the agreements they had. As one of those tail events unfolded, they had the information to take advantage of the opportunity.”  

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