AI is beginning to reshape pricing, valuations, and independent price verification. However, faster, and more automated processes only create value when the results remain explainable and defensible. This session will explore how firms are applying large language models, agents and advanced forecasting techniques to valuation workflows while preserving the lineage, observability and governance required by trading, risk, and audit teams. This session will examine the controls and evidence needed to support robust independent price verification processes.

Private credit portfolios are growing rapidly, but valuation remains one of the most challenging and consequential tasks. This session presents an end-to-end, data-centric approach to private credit and leveraged loan valuations that addresses the core challenge: how to extract structured insight from highly unstructured market data and apply AI-driven analytics to price illiquid assets with confidence. We will explore industry trends, pricing methodologies and how forward-thinking firms are automating valuation workflows and building robust reference data frameworks for private credit holdings.

Accurate entity identification is essential for revenue attribution, cross-sell, credit risk aggregation, and settlement. However, fragmented legal entity data still leaves many institutions with duplicate records, incomplete hierarchies, and unreliable matches. This session will explore how generative AI, machine learning and graph-based techniques are improving entity resolution at scale. We will examine how firms can unify customer and counterparty views, reduce manual exceptions, strengthen explainability and unlock greater value from enterprise entity data.

Managing public and private market data in a single, coherent, and governed view remains unsolved for most firms. The two markets move at fundamentally different speeds, leaving investment committees without a complete picture of the portfolio. The deeper challenge is that the data itself arrives fragmented across custodians, fund administrators, external managers, and asset classes, with no common quality standard or taxonomy. This session will explore how asset owners can achieve true whole-of-fund transparency, how asset managers can strengthen portfolio oversight, analytics, and client reporting, and how AI can turn a connected data foundation into a self-service knowledge layer.

This session will show the art of the possible with GoldenSource Scout, using buy-side examples around product launch readiness and data change intelligence. We will explore how AI can help teams move from unstructured product ideas to revenue-ready action by identifying the data, mappings, approvals, and dependencies needed to launch with confidence. We will also show how teams can ask natural-language questions, trace answers back to source records and understand material changes across ratings, classifications, pricing, and reference data.

Building on the GoldenSource Scout use cases, this interactive benchmarking session will ask you to step back and assess where AI could deliver the greatest value across your own organization.

Co-led with InvestOps, the session will use live polling and facilitated discussion to benchmark where buy-side firms are focusing AI today, which use cases they see as most valuable, what barriers are slowing progress and what requirements matter most for trusted adoption.

You will leave with practical insight into where your peers are focusing AI investment, how they are weighing risk and reward, and why governed, contextual data is becoming essential to making AI explainable, controlled, and usable in production.