Since the 1970s, LexisNexis has been providing a variety of data services to financial institutions. Data Management Insight spoke to Danielle McCormick, vice president of product, Global Nexis Solutions, to discuss how financial institutions are approaching AI, trusted data and the future of enterprise intelligence.
Data Management Insight: Hello Danielle, when were LexisNexis’ data operations begun, and how?
Danielle McCormick: LexisNexis has spent more than five decades building large-scale licensed information systems used in highly regulated industries. We began digitising legal and news content in the early 1970s, well before digital information access became mainstream.
That foundation matters today because enterprise AI systems are only as trustworthy as the data they are grounded in. From the beginning, our focus has been on delivering authoritative, attributable information that professionals can defend and act on confidently. Over time, that expanded beyond legal research into global news, company intelligence, financial information, and risk data that now support enterprise workflows across financial services.Today, that same foundation powers AI-enabled research, analytics and workflow automation for institutions operating in highly regulated, high-stakes environments.
DMI: How is LexisNexis helping financial institutions solve their data challenges?
DM: The challenge for financial institutions is no longer access to information. It is the operational burden of validating, reconciling and synthesising fragmented information across multiple systems under intense time pressure.
Investment banking, research and strategy teams still spend significant time manually cross-checking sources, validating data consistency, summarising long documents and preparing deliverables. That slows decision-making and limits scalability.
LexisNexis helps reduce that burden by providing licensed, attributable news, company, legal and market intelligence that can be integrated directly into AI-assisted workflows.
Through Nexis+ AI and Nexis Data+, firms can:
- accelerate research and synthesise workflows
- reduce manual validation effort
- improve confidence in AI-generated outputs
- embed external intelligence directly into internal AI environments
Importantly, we are seeing organisations evolve beyond standalone AI tools toward governed enterprise AI ecosystems. In addition to providing financial institutions with enterprise AI platforms securely and at scale, our role is to provide trusted intelligence infrastructure that integrates into internal systems and agent frameworks, and is more deeply embedded in their workflows.
DMI: You have a huge catalogue of news and other data. When did you begin compiling that, and how are financial clients using it?
DM: Our news archive dates back more than 45 years, and today we licence content from tens of thousands of global sources, including major newswires, trade publications, regional media, regulatory content, company disclosures and broadcast transcripts.
But the differentiator today is not simply the volume of content. It is that the content is from valuable sources and is licensed, attributable and structured in ways enterprise AI systems can safely consume.
That is increasingly important as financial institutions strengthen governance around AI-generated outputs and model traceability.
Clients use our data across several high-value workflows:
- monitoring companies, sectors, and market signals
- benchmarking competitors across fragmented datasets
- supporting due diligence and transaction analysis
- accelerating preparation of investment memos, CIMs and client deliverables
- monitoring counterparty and reputational risk
- powering internal AI research assistants and retrieval systems
Increasingly, firms are embedding Nexis Data+ directly into internal AI environments through APIs, AI services and connectors so trusted external intelligence becomes part of their daily workflow infrastructure rather than a separate research destination.
DMI: What are the newest challenges that LexisNexis is helping clients overcome?
DM: One of the biggest shifts we are seeing is the move from isolated AI experimentation toward connected, enterprise-wide AI workflows.
Financial institutions are combining internal models, proprietary datasets and external intelligence sources to automate portions of research, monitoring and analysis workflows. The challenge is ensuring those workflows remain governed, transparent and grounded in trusted data. That is where we are focused.
Firms need AI systems that:
- operate on current, licensed and attributable information
- support auditability and governance requirements
- improve reliability and reduce validation risk
- integrate securely into existing enterprise architecture
Regulators and internal governance teams are asking more detailed questions about how AI-generated outputs are produced and validated. Institutions need to demonstrate lineage, provenance and traceability behind insights used in critical business decisions.
In that environment, licensed and verifiable data becomes a strategic requirement, not simply a content consideration.
DMI: How do you harness AI to provide clients with their data?
DM: Our focus is not simply applying AI to content retrieval. It is enabling governed intelligence workflows. Nexis+ AI allows users to ask natural language questions and generate cited outputs grounded in licensed news, company and financial information. That helps teams accelerate research and synthesis while maintaining transparency into source material.
We have also invested heavily in interoperability and enterprise integration. Through Nexis Data+ and connectors, organisations can securely connect trusted external intelligence into internal AI systems, co-pilots and agent frameworks without requiring large-scale custom integration work. The goal is to help firms operationalise AI safely inside existing workflows rather than forcing users into disconnected standalone experiences.
Equally important, our AI deployments are built around governance principles that matter deeply to financial institutions:
- licensed and attributable content
- transparent citations
- enterprise-grade security controls
- strong data privacy protections
- no customer data used for model training
That combination of trusted data, workflow integration and governed AI is where we believe long-term enterprise value will be created.
DMI: What does LexisNexis see as the next big thing in client data needs?
DM: The next phase of enterprise AI adoption in financial services will centre on integrated, automated workflows rather than isolated productivity tools. The firms moving fastest are focused on compressing the time between market signal, insight generation and decision execution, while still maintaining governance and human oversight.
That creates growing demand for:
- AI-ready licensed data
- cross-source intelligence synthesis
- explainable AI outputs
- enterprise interoperability
- workflow-integrated research automation
We also expect demand to increase for systems capable of synthesising insight across very large document sets while preserving traceability back to original sources.
Ultimately, the institutions that gain advantage will not simply have more AI tools. They will have stronger data foundations, better governed workflows and the ability to operationalise trusted intelligence at enterprise scale.
DMI: What are your development plans for 2026?
DM: Our 2026 priorities are centred around three areas:
1. Deeper enterprise AI integration
We are continuing to expand how Nexis Data+ and Nexis+ AI integrate into internal enterprise AI ecosystems, including agent frameworks, AI services, intelligent assistants and research workflows.
2. Governed AI and trusted intelligence
We are continuing to invest in licensed content, attribution, transparency and enterprise-grade governance capabilities to help financial institutions scale AI responsibly.
3. Workflow acceleration
We are focused on reducing the operational burden associated with research, validation, monitoring and synthesis of workflows, particularly in high-intensity environments like investment banking, corporate strategy and risk analysis.
That includes investments in:
- advanced document analysis
- multi-source synthesis
- proactive monitoring and alerting
- structured workflow automation
- expanded global content coverage
Our objective is not simply to provide more information. It is to help financial institutions operationalise trusted intelligence faster, more safely and at greater scale inside the AI-powered enterprise.
Explore how organisations are balancing AI innovation with trusted data, governance, and human oversight in the LexisNexis Future of Work report. Read the report.
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