Risk Edge is a leading solution provider for
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Founded in 2013, Risk Edge delivers scalable, configurable analytics solutions for the Commodities & Energy industry — powering precision decisions at speed using AI and Machine Learning.
6+
Countries
$135B+
Client Revenue
10+
Years Experience
Powering Smarter Decisions in Energy & Commodities
Risk Edge was founded in 2013 to fill a critical need for scalable, configurable analytics solutions in the Commodities and Energy industry. Using the latest advancements in AI, Machine Learning, and cloud architecture, we deliver precision decisions at blazing speed.
Machine Learning
Risk Edge's "Predictive Engine" is built specifically for Commodity & Energy businesses to help you understand hidden patterns, risks, and price trends so businesses can be understood as a whole, and not just in parts!
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Market Edge
Market Edge is an AI-driven, web-based solution to apply analytics to price data of Commodities and Energy. With ML-based forecasting, advanced visualizations and technical indicators, Market Edge gives Traders and Risk Managers a decisive edge.
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VaR Edge
RiskEdge does valuation & risk (Value-at-Risk, Expected Shortfall) calculations for all your physical and derivative positions across counterparties. Its powerful engine delivers accurate results fast, even with computation-intensive Monte Carlo methods.
Learn More →Purpose-Built Analytics Platforms
Risk Edge has several software solutions and platforms delivering custom-designed Machine Learning & Risk Analytics. Built by domain experts to predict Yield, Demand & Supply, Defaults, Breakdowns, Risks and Prices.
Global Clients
Risk Edge has worked with some of the most well-known companies in the world, helping them solve complex problems. Our clients consistently remain long-term partners.
Whitepapers & E-Books
Deep-dive research and practical guides from the Risk Edge team — covering AI use cases, credit risk, derivatives, and more.
AI Use Cases for Energy and Commodity Trading Companies
Artificial Intelligence has shown great potential over the last few years in solving myriad problems of companies. While most of the concepts and techniques have been available to us for the last couple of decades, it wasn’t until lately that their usage picke...
ReadProfit and Loss Attribution Analysis
Most companies with a dynamic portfolio of assets usually analyze their daily / monthly P&L in great detail. One of the most commonly desired analyses is P&L Attribution – which can show the traders a breakdown of various factors which have affected the P&L....
ReadPredicting Credit Defaults using Machine Learning
Imagine a Counterparty with a history of default, beginning to slip on its payments again; or a relatively newer Counterparty with steadily increasing exposures beyond company’s comfort levels. You’d want to see red flags on these and other such cases much bef...
ReadResults That Speak for Themselves
Over a decade of delivering high-precision, real-time analytics to the world's leading commodity and energy companies.
Latest
Technology for customised solutions
Fast
Implementation done right
6+
Countries served globally
Real-time
Computations done in-memory
$135 Bn
Combined client revenues (2019)
Client Case Studies
See how leading commodity and energy companies use Risk Edge to transform their risk management and analytics.
AI Agents Platform (Marketing)
Revolutionizing Workflows with AI Agents: A Case Study on AI Agents as a Service
- Developed a modular architecture where each AI agent functions as an independent service.
- Implemented a scalable backend using FastAPI and a responsive frontend using Vite + React.
AI Journalist
Revolutionizing News Content Creation with AI: A Case Study on AI Journalist
- Developed a four-agent editorial workflow system: Planner, Writer, Editor, and Publisher.
- Planner agent builds structured outlines using topic and reference links.
AI Research Intelligence App
Transforming Market Research and Outreach with AI: A Case Study on AI Research Intelligence
- Built a multi-agent graph-based system acting as both a research analyst and communication strategist.
- Research Discovery module automatically gathers and summarizes relevant news, trends, and competitor insights.
Using Machine Learning to Predict Machine Failures
Client is a large Renewable Energy Company with more than 4 GW of production capacity.
- Risk Edge processed terabytes of data to deliver compact datasets for reporting and implemented Recurrent Neural Networks for failure prediction.
- The solution reduced downtime by 60% and expanded management reporting capabilities to include new parameters.
Automating Market Risks and P&L Calculations
Client is a leading Singapore based Agri-business operating in over 70 countries, and supplies food and industrial raw materials to over 22,000 customers worldwide.
- Risk Edge delivered a web-based solution within one month, enabling P&L simulation and crop yield prediction based on historical data.
- The solution featured 3D charting for visual insights, in-memory calculations for real-time parameter adjustments, and advanced machine learning algorithms for accurate yield prediction.
Yield Prediction for a Fortune 500 Agri Client
Client is a SE Asia based company who is one of the world’s largest producers of Oil Palm.
- Risk Edge brought in well-researched algorithms and created an ensemble of those algorithms to deliver results. The accuracy was around 82%, which was deemed acceptable by the management team.
- Hierarchical models, along with several other Machine Learning models, were used to predict Yield. Different algorithms were applied to interpolate missing values and extract seasonality and trends from critical parameters.
Using Machine Learning (NLP) to build a Robo-Advisor
Client is a leading Global Forbes 100 Investment Bank with more than USD 700 bn in Assets under Management.
- Risk Edge developed and implemented machine learning algorithms and managed offshore activities for the project.
- The scalable solution processed large data volumes, delivered insights via a NoSQL database, and enhanced advisor efficiency and client satisfaction.
Deep Learning : Journal Entry Anomaly Detection
Client is a Global Metals & Mining Company.
- Risk Edge used feature engineering to reduce data size and imbalance, and implemented AutoEncoder algorithms for anomaly detection.
- The solution incorporated statistical filters and AI to reduce false positives significantly, improving anomaly detection accuracy.
Counterparty Credit Risk Software
Client is a large SE Asia based company trading in Oil and Oil products (including Gas Oil, Fuel Oil and Petrochemicals) and is a part of Forbes Global 2000 companies list.
- Risk Edge delivered a Robust and scalable Counterparty Credit Risk Solution for the entire Portfolio and integrated it with their trading and back-office systems.
- The Client now sees each Counterparty’s Potential Future Exposure for multiple horizons on a daily basis. The solution also points when the Exposure is max / min and whether there is any concentration of risk among the counterparties
Risk Consulting – Automating Market Risk Management and Mark-to-Market P&L Calculations
Market Risks & P&L Calculations – For a large Agri-business
- Risk Edge leveraged its team's prior experience with large Agri companies to quickly understand the specificity of trading across different commodities, significantly reducing the time to execution.
- For one commodity, Risk Edge tweaked existing in-house systems to automate the process quickly, trained teams on input rules and formats, and guided them on extracting daily/weekly reports.
Consulting to build a new Hedge Framework
Client is an India based company with business primarily in Crude Oil Refining and Marketing of products.
- Risk Edge’s consultants studied the Client’s business processes for a few weeks along with their existing hedging models.
- A new proprietary model was built by Risk Edge’s Statistical and Business Analysis teams that suited the client’s requirements better.
