FIS modernizes Commodity Risk reporting with new data architecture and AI
Recently, I had the opportunity to see the new reporting functionality in FIS Commodity Risk Manager (CRM, formerly known as Kiodex), which is based on AI-enabled technology. Initially I expected AI to be the main focus of the discussion. However, surprisingly, AI itself was not the dominant topic.
The CRM reporting had previously relied on legacy technologies that had been planned for replacement for longer time. Several alternative approaches have been considered, but recent developments in AI-enabled data and analytics architectures have provided a practical pathway to resolve this challenge, even though AI plays a secondary rather than primary role in the overall solution.
A key step toward a modern reporting architecture for CRM was the decision by FIS to partner with Snowflake, which effectively provides a scalable data layer for AI and analytics applications.
Snowflake is natively integrated with AI-enabled tools, including platforms such as ThoughtSpot, an agentic analytics solution that enables dashboard creation and query-based analysis on top of Snowflake-hosted data. From a development perspective, the primary engineering effort was concentrated on establishing data pipelines from CRM into Snowflake. This interface supports both intraday and end-of-day data transfer via Snowflake APIs. According to Peter Moore, Senior Director of Solution Management for FIS’s commodity and energy solutions, integration between Snowflake and CRM is delivered as part of a standardized offering rather than as bespoke, project-specific development.
Once data is available in Snowflake, it can be combined with external datasets, enabling a broader reporting scope that extends beyond CRM data alone. Users can select relevant Snowflake tables and generate dashboards, charts, and reports for a wide range of analytical requirements. AI capabilities are present in this architecture but are not the primary driver. Users can interact with the system through both traditional structured query tools and natural language prompts for AI agents.
According to Peter, the primary objective of this initiative was the creation of a unified data layer combined with a modern BI interface. The fact that the chosen BI platform supports AI-driven interaction is therefore a secondary benefit rather than the core value proposition.
Implementation timelines for customer projects are expected to be relatively short, potentially around two months, according to Peter. When asked about common concerns such as hallucinations and cost management, he expressed confidence that these can be effectively addressed. On one hand, AI systems learn and improve over time through user feedback, where users validate correct outputs and flag incorrect ones. On the other hand, tight collaboration with ThoughtSpot and continuous software updates from the vendor help address reliability and performance issues.
There are also multiple ways to address cost issues, especially related to token-based pricing introduced by major LLM providers. On one hand the reporting dashboards based on Snowflakes data can be built without any AI involvement, on the other hand the actual use of token can be effectively controlled in case the AI agents are involved.
According to Harshad Kolpyakwar, Head of Product Management, Energy Solutions at FIS, the strategic partnership with Snowflake, announced earlier this year at the user conference, enables FIS to leverage Snowflake’s data platform capabilities across both its ETRM and CTRM product portfolio. The reporting approach implemented for CRM will be extended to other FIS energy solutions, most notably the flagship product – FIS Energy Trading Risk and Logistics Platform (ETRLP). According to Harshad, adopting a Snowflake-based data infrastructure combined with ThoughtSpot reporting capabilities represents a significantly faster and more efficient path to AI-enabled reporting than the previously considered approach of building a proprietary semantic data layer within ETRLP.
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