I recently had a call with Nitin Gupta with Risk Edge as he is currently in London working with a client. Risk Edge is an Indian vendor that specializes in developing web-enabled risk analytical tools in areas like credit risk, market risk and pricing. More recently, it has been focused on predictive analytical tools using machine learning and a highly intuitive graphical interface to allow users to drill into and collaborate on the analysis of data. It has even developed a tool to perform sentiment analysis on Twitter feeds and social media.
Risk Edge has closed two deals this year to date and also added a number of new tools to its portfolio of products. Credit Risk functionality has been added into the system (Potential Future Exposure), using the robust multivariate GBM Simulation method and it has launched a product for Derivative pricing – www.derivist.com – that is available freely on the web. It is designed to help people price different types of structured products.
It has also launched a big data analytics module, which project demand & supply, help companies gain deeper insights into their customer’s behavior, improve operational efficiencies, detect fraud, price their physical product right, plan output – basically find solutions for many such operational & strategic questions. Shortly, it will also be launching a Text Mining application on Derivist, allowing companies to mine tweets and documents – which can be used to study brands (e.g. Delta Airlines), events (e.g. Oil supply disruption), changing consumer behavior (e.g. gluten free wheat) and many other things. The tool had also predicted Brexit similarily to Google he told me. Nitin provided a demonstration of two of the products or ‘mini-apps’ – as he terms them. The philosophy is simple – keep the focus of each application to solving a problem and build a suite of tools that can help users solve a number of specific issues.
Risk Edge’s differentiation is its statistical and modeling knowledge that allows it to build tools in a web-enabled environment to solve various risk and analytical problems for its commodity trading clients. The tools seem to be fast in terms of performance and have an ability to graph in 3-dimensions allowing users to actually visualize ‘inside the data’.
Given the interest in predictive analytics in the commodity world, these tools should have a bright future and ComTech can see Risk Edge teaming up with other vendors, consultancies and data management specialists to help deliver sophisticated tools that extend the risk and analytical capabilities of other products as well as on a stand alone basis.