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The ETRM of the future: when genAI meets traditional platforms
Currently, agentic AI can provide seamless and user friendly workflows orchestration for approvals, confirmations, margin and actual reconciliation. But what genAI cannot provide is robust calculation engines working for any kind of asset class, product and lifecycle status running on top of an optimized data model able to host millions of transactions without performance issues
The evolution of the ETRM will bring the best of both worlds: robustness, performance and auditability together with low human intervention and high-speed insights generation.
ETRM platforms are becoming more standardized, while customization and added capabilities move to satellite solutions. SaaS has accelerated deployment and reduced costs. ETRM remains the cornerstone of the trading architecture.
The new ETRM uses modular building blocks that can work together or independently. APIs and MCPs simplify integration, while regular updates support new regulations and assets. ETRM is becoming an interconnected platform.
GenAI can answer trader questions, automate trade capture, check margin calls and credit limits, and support KYC profiling. Human validation remains essential, making clear data ownership critical.
GenAI can generate, reconcile and exchange trading documents, while human teams monitor processes and manage exceptions. Sensitive outbound activities still require second level validation to reduce errors and risk.
How NTT DATA helps
Evaluate the quality of your data, generate data lineage to assure the source info is trustful, and define a datalake that can support all your business, monitoring data input and output and becoming the single source of truth.
Select your fit for purpose C/ETRM based on your requirements. Make a quick deployment of the standard solution and migrate your data.
Redesign the business processes to be straightforward. Define KPI’s and control loops and second-level validations. Define the boundaries of automation and the role of the human. Create a roadmap of processes subject to automation.
Define a future-ready architecture, allowing agents to access information, identify triggers and events and orchestrate actions based on company rules.
Execute the implementation of the agents. Take lessons learned and define a playbook for automatic controls. Evaluate the performance and run continuous improvement framework.
Avoid delays in trade capture, confirmation exchange and settlement reconciliation. Monitor your performance KPI’s and follow around-the-sun operations in a centralized way.
Modernizing ETRM operations can reduce manual effort, simplify the technology base and give teams faster access to the data and insights they need. The result is a more efficient operating model with lower transaction costs and greater flexibility to apply company specific knowledge
Provide instant access to raw data and compute DIY insights based on user needs.
Automate all life cycle actions providing more capability for your teams.
Using standard products, the number of issues, the effort for upgrades and vendor-locking is reduced.
Take the difference into your AI agents, with your particular context and knowledge. Aligned with company policies and procedures.
AI will assume the role of process and workflow orchestration and will automate the majority of the operations without human intervention.
