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Informatica Data Capabilities Drive Salesforce’s Agentic Platform
Informatica’s integration into the Salesforce architecture has proceeded at pace since it was acquired by the customer relationship management software platform last year. The extent of that transformation is being demonstrated in a worldwide Salesforce touring showcase. In the glitzy shows, the role of the artificial intelligence-powered data management specialist’s capabilities within Agentforce – Salesforce’s…
Financial Operations Has an Invisible Tax Problem. AI Is Starting to Fix It
By Neil Vernon, chief product officer at Gresham. The financial services industry has spent considerable energy debating whether AI is production-ready for regulated operations. That debate has been asking the wrong question. Readiness is not the constraint; targeting is. The real question is whether firms have correctly identified where AI can address costs that have…
Advisory Summit Selects Finbourne as Fund Administration Provider
Finbourne Technology has been chosen as the strategic fund administration platform for Summit Group, a provider of fiduciary, fund and advisory services. The enterprise data management platform will consolidate instrument, pricing, holdings and transaction data from various market data providers, custodians and counterparties. This choice follows an evaluation process intended to upgrade the infrastructure across…
New Portal Offers SEI Clients Data Management Functionality
SEI has introduced an updated investment management platform that merges SEI Data Cloud with a portal named SEI Scope to manage data access, workflows and operations. Global investment managers collaborated on the system to cover the full operational lifecycle through automation and data governance, the provider of financial technology, operations and asset management services said….
MCPs in Data Management: Bringing New Order to Private Markets
Financial institutions have begun deploying Model Context Protocols (MCPs) as they have expanded the use of artificial intelligence applications and agents. The technology developed by Anthropic is an open-source contextual layer that helps coordinate models and data, enabling AI applications to connect with a multitude of other platforms and processes. In the first of a…
Immaturity of AI Shines Through in Report Highlighting Persistence of Age-Old Data Challenges
The relative novelty of artificial intelligence use within financial services has been highlighted in a global study of the technology’s adoption, with many data challenges remaining and blind spots in security still widespread. While the 2026 Global AI in Financial Services Report by the Cambridge Centre for Alternative Finance found firms were eager to integrate…
AI in Capital Markets Handbook 2026
AI adoption in capital markets has moved into a more disciplined phase. The priority is now controlled deployment: where AI can be used safely, where it can deliver measurable value, and how outputs can be governed, monitored and evidenced. The 2026 edition of the AI in Capital Markets Handbook examines how AI is being applied…
Data Reconciliation Hurdles Seen Holding Back Innovation
End-of-day reconciliation processes create data challenges that are constraining buy-side firms from achieving efficiencies from new technologies and AI initiatives. The challenges posed by reliance on long-established reconciliation processes come as the buy-side undergoes a transformation of its operating models to accommodate new data management, investment and settlement strategies. This challenge was highlighted in a…
AI In Financial Services: Where The Real Challenges Are Starting to Emerge
By Joe Norburn, chief executive of TCC and Recordsure. Across financial services, AI is now embedded in day?to?day activities, from fraud detection and onboarding to credit assessment and customer interaction. The UK Treasury Select Committee’s recent inquiry reflects just how widespread that adoption has become, especially among larger institutions. What stands out is not that…
12 Leading Vendors Operationalising AI & ML with Robust Data Pipelines
The transition of artificial intelligence and machine learning (ML) models from experimental sandboxes to production environments remains a persistent operational friction point. While quantitative researchers and data scientists can often demonstrate alpha in isolated backtesting environments, the institutionalisation of these models requires a level of data pipeline robustness, latency control and regulatory auditability that research…