Energy traders should look outside their industry for tools to improve risk reporting accuracy.

Energy traders who successfully improve the accuracy and timeliness of their risk reporting stand to benefit from improved margins, reduced controls and the ability to more quickly move in the market.

Specifically, if energy companies integrated supply and demand position data with their intra-day risk reporting, there would be multiple benefits. Risk throughout the portfolio could be reduced. Fines or penalties for being out of balance would fall. Insight into counterparty and contract profitability would improve. Ultimately, trading strategies could be optimised, with the associated benefits of competitive advantage in the market.

*Supply and demand data*

Yet few organisations are using either supply or demand generation data in this way. Position reporting is organised around intra-day schedules but supply and demand data is often unaccounted for in these processes. Trading desks are forced to perform a "true up" - manual reconciliation of the data to position reporting days or weeks after the fact, sacrificing margin or even moving from profit to loss due to "unexpected" changes. Therefore, the choice facing trading organisations today is between an analytic capability that allows them to look backward and report what happened, and a capability that allows them to understand what is happening now and react to it in real time.

The reluctance to take the most obviously beneficial route stems from a perceived inability to ensure supply and demand data is granular enough to revise the position in quarter hour increments. Attempts at systemic solutions to this problem end up in one of two, untenable, outcomes: either the information can be processed intra-day but at insufficient granularity, which means the need for post-day adjustments remains, or the data is granular but the time to acquire, process and analyse it is too long to allow it to be used on the same day.

Many traders who have tried have found the cost and time taken to integrate the data outweighs what is gained in profitability. Other, completed, attempts have failed or simply been prematurely abandoned due to exorbitant expense or lack of demonstrable success.

*Massive IT spend is not necessary*

The myth prevalent within many trading organisations is that these issues cannot be resolved without a massive IT investment. In reality, companies are simply trying to solve the problem with the wrong tools - typical tools of the trading world such as commodity trading and risk management tools, or back-office financial systems. These options, besides being large and complex, also require a significant investment of time and money and cannot be quickly adapted. While Excel-based tools are flexible, they lack either the ability to handle larger volumes of daily incoming data or the transparency and auditing ability needed to provide confidence at scale.

Energy traders should look to other industries for inspiration. As an example, industries such as telecommunications and manufacturing make good use of process-driven analytic tools. These have many of the key elements needed to ensure that detailed data is quickly and granularly analysed. Specifically, they allow business users to create and change analytics quickly and without a major IT engagement, and they allow both logic and data to be modelled in the same tool. Equally important is that process-driven solutions support an analytic methodology where discovery and analysis happen simultaneously.

*Finding the right solution*

When looking to acquire this type of technology, organisations should select a solution that:

* enables them to access and apply data quickly, including acquiring and analysing data in the near real-time.

* maintains sufficient data granularity to improve position reporting at quarter-hour increments.

* is able to analyse supply and demand data in the context of trading and contract logic.

* supports an agile analytic methodology, allowing business teams to adapt and tweak analytics and explore new data sources quickly and easily.

* creates output that can be audited and tracked, providing confidence in output and reducing the likelihood of needing revisions.

* delivers value within three to six months and is able to consistently adapt itself to new data inputs and analytics within days or weeks.

This type of tool can be managed and maintained by the process analytics team or perhaps in the mid-office. This eliminates the risk and expense of a large-scale IT implementation without sacrificing functionality.

*Complex analytics*

Energy managers often raise objections to such an approach, believing trading to be unique or too mathematically complex to find guidance in other industries, or that only companies steeped in trading and risk management expertise can possibly provide solutions in a timely, cost-effective manner. Experience elsewhere has shown that complex analytics can be implemented quickly and efficiently by leveraging solid technology.

To successfully adapt solutions from other vertical industries, energy traders should take an approach based on quick timelines and minimal risk. "Big bang" solutions should be avoided and more attention focused on small systems that address core parts of the risk reporting process such as the integration of generation data. By keeping initial timelines and investments short, organisations will maintain more flexibility to mix and match technologies and avoid becoming trapped in a substandard solution due to large investments of time and money.

They must also acknowledge that existing trading and risk management technologies have a role to play. Companies should pay attention to the ease with which new technologies can be integrated into existing architectures and data models. After all, even the most simple tool can result in a blown budget due to integration costs.

Victor Milligan, director of utilities and energy markets, Martin Dawes Analytics.

Email: vmilligan@mda-data.com