08 March 2022 – The partnership will provide Inatech’s customers with an array of advanced risk analysis tools including what-if scenarios, portfolio & margin optimization, hedge effectiveness and value at risk (VaR) models.

Inatech, a Glencore company, is an industry-leading provider of cloud-based energy trading and distribution software. Inatech has now entered into a strategic partnership with cQuant.io, a US-based analytics company that provides innovative and easy-to-use advanced risk analytics.

In fast-paced and complex energy markets, the ability to manage trades and to cover risks in real-time is more critical than ever. Currently, Inatech’s intuitive energy trading and distribution system, Techoil, supports the needs of energy traders and fuel marketers. Now, with the integration of cQuant’s powerful risk analytics capability, Techoil can offer “out of the box” risk analysis tools like Risk Reduction Value and Stress Testing.

Jean-Herve Jenn, CEO of Inatech commented, “The energy trading landscape is very dynamic and facing the new market, operational and credit risks. The addition of new fuels and the partial automation of trading has vastly increased data volumes, reporting and complexity of trades. The management needs to be able to rely on accurate and real-time risk reporting and the risk managers need AI-based decision support to create real value for businesses. We are confident that the integration of cQuant.io’s innovative technology into Techoil will empower our clients with a seamless and complete solution. Moreover, unlike current counterparts, we offer the assurance of a one-stop solution and unrivaled customer support without the burden of expensive upgrades and product development uncertainties.”

David Leevan, the CEO of cQuant.io added, “We are excited to partner with Inatech with whom we share similar principles and a vision to support the industry. Both cQuant.io and Inatech have built cloud-native, multi-tenant, SaaS solutions. cQuant offers sophisticated analytics and a large library of energy models. With this integration, we are confident that we will be able to help utilize data analytics to make the right decisions in this high-stakes, and time-sensitive environment.