Beacon by CWAN: A Unified Full-Lifecycle Investment Platform

Beacon by CWAN has secured leading positions in 10 of 13 enterprise risk categories in the 2026 Energy Risk Software Rankings, establishing the platform as a primary tool for firms seeking to unify fragmented risk data. The software ranked first in the Risk registers category and second in Analytics, Creativity and innovation, and Multi-strategy storage.

The platform, which is owned by Clearwater Analytics (NYSE: CWAN), provides a unified system that spans the investment lifecycle, integrating trading and modelling with accounting and regulatory reporting. According to reporting by Risk.net on May 1, 2026, the software’s performance in the rankings follows a growing demand among energy firms for valuation consistency and enterprise-wide visibility.

Solving ETRM Fragmentation

A primary challenge for energy firms is the reliance on multiple Energy Trading and Risk Management (ETRM) systems. Many organizations operate a mix of legacy systems, regional platforms, exchange feeds, and bespoke spreadsheet workflows simultaneously, which often leads to operational silos.

Beacon by CWAN operates as an independent analytics layer that integrates with any source system, a characteristic described as genuine ETRM agnosticism by Brian Wood, Beacon’s principal product manager for energy and commodities. This architecture allows firms to aggregate data across various platforms to achieve a consistent view of mark-to-market, Greeks, and risk exposure across all positions.

For many clients, that means aggregating across multiple ETRMs [energy trading and risk management systems], which is a real operational challenge that we solve.

Brian Wood, principal product manager, energy and commodities at Beacon

This centralized exposure aggregation is designed to prevent the pitfalls seen during the market volatility of 2021 and 2022. During those periods, some firms with strong market risk frameworks were still vulnerable to liquidity squeezes or counterparty failures because those risks were managed in separate silos.

Strategic Integration with Clearwater Analytics

The capabilities of Beacon were expanded following its acquisition by Clearwater Analytics in 2025. The merger combined Clearwater’s expertise in investment accounting and portfolio analytics for institutional investors with Beacon’s specialized commodity trading and risk management tools.

This integration addresses a shift in the institutional investor landscape, as more portfolios now include exposure to energy infrastructure, real assets, and commodity-linked investments. By bridging investment portfolio analytics with commodity risk, the combined platform allows for a more holistic view of risk across public and private markets.

AI and Cloud Infrastructure

Beyond its risk rankings, the platform has integrated advanced technological features to reduce manual overhead. On January 27, 2026, Clearwater Analytics announced the deployment of embedded agentic AI capabilities within Beacon. These AI agents operate within the calculation engine, using data grounded in a firm’s actual positions and validated models to accelerate exposure analysis and model validation.

The AI integration is intended to reduce the time required for model validation from weeks to hours. It also enables the automatic generation of technical validation reports for quantitative analysts and executive summaries for Chief Risk Officers.

The platform’s cloud-native infrastructure further supports these functions through several specialized tools:

• Power & Gas Package: A turnkey solution that provides ICE and CME trading capabilities, live risk monitoring, and derivatives pricing without the need for custom development.
• Glint Framework: A Python-based rapid web application framework that accelerates the development of business logic into user interfaces.
• Spreadsheet Conversion: Tools that read and convert spreadsheets into cloud objects, allowing them to be subject to standard version control and governance while accessing live production data.

By utilizing an enterprise-scale dependency graph framework, the system identifies which inputs have changed and only recalculates dependent values, reducing compute costs and increasing the speed of large-scale scenario analysis.