Licensed data
Do you have access to licensed, ethically sourced data?
Generative AI in financial services
Explore how banks, wealth managers, insurers, and other financial institutions, can better prepare to scale generative AI by connecting to reliable data, developing systems for AI governance, and measuring impact across teams.
AI Readiness
Is your financial services organization enterprise AI ready?Cited, multi-source answers
Can you trace and validate answers your genAI tools produce?
AI and data privacy
Does your research stay secure within a dedicated cloud?
Evaluate your AI capabilities
Take the AI capability quiz to discover your organization's AI persona.
Scaling Enterprise AI in Finance
Answers powered by licensed contentYour financial services firm may have already run genAI pilots in investment banking and across the broader business. It's important that those pilots become secure, adopted, and measurable. Scaled deployment should support high-value work, including:
- Market and sector analysis
- Company profile construction
- Portfolio monitoring
- Financial document summarization
- Competitor and regulatory monitoring
- Emerging risk detection
- Client material preparation
Connecting Data
Power genAI in financial services with trusted dataFinancial services firms need content that is high quality, structured, enriched, permissioned, and accessible for AI use cases such as: RAG, copilots, document analysis, company profiling, filings summarization, news and regulatory monitoring and governed research workflows
- RAG
- Copilots
- Document analysis
- Company profiling
- Filings summarization
- News and regulatory monitoring
Preventing Shadow AI in Finance
Reduce shadow AI risk through governance
Often emerging when employees cannot access approved tools that meet their workflow needs, shadow AI describes unauthorized AI usage within an organization. It often leads to security risks and inaccurate output.
Effective AI governance addresses: which tools are approved for which use cases, what data can and cannot be used, how AI-generated outputs should be reviewed and customized, when source citations are required, when human review is required before outputs inform client, investment, risk or compliance decisions, who owns risk validation and escalation, how usage is monitored across teams and how policies are communicated and enforced
-
-
-
-
Measuring Productivity
Prove AI value through workflow efficiencyTo scale genAI, financial services leaders need to show measurable value beyond experimentation. Enterprise AI value should also be assessed through workflow quality, decision support, risk reduction, user adoption, and commercial impact. A recognized value framework helps firms compare use cases, prioritize investment and communicate results to senior leadership.
Frequently asked questions
Enterprise AI is the use of artificial intelligence across business-critical workflows in a financial services organization, including market research, due diligence, client service, risk and compliance, supported by secure infrastructure, governed data, clear policies and measurable outcomes.
AI readiness means a firm is prepared to deploy AI securely and responsibly at scale. It includes trusted data, licensing clarity, governance, workflow integration, vendor readiness, and value measurement.
Licensed data helps firms use AI with greater confidence by clarifying usage rights, improving source transparency, and supporting defensible outputs. This matters most for regulated generative AI in financial services workflows.
Firms can reduce shadow AI risk by providing approved tools, setting clear policies, controlling data access, training employees, monitoring usage and embedding AI into secure enterprise workflows.
AI ROI should be measured through productivity, workflow efficiency, faster turnaround in research and document review, decision quality, risk reduction, adoption, repeat usage and business impact.
Firms should evaluate licensing, provenance, citation support, metadata quality, API integration, security, scalability, governance support, validation support, cost control, and regulated-industry experience.
Generative AI can help financial professionals synthesize market, sector and trend research, draft company profiles, summarize earnings reports and filings, and monitor news or regulatory changes. These outputs should be verified against trusted sources and customized before they inform client advice, investment decisions, risk assessments or compliance work.
Explore genAI in finance
Built for professionals who can't afford to get it wrong. Fill out the form to receive a personalized demo of genAI for financial services.
LexisNexis, a division of RELX Inc., may contact you in your professional capacity with information about our other products, services, and events that we believe may be of interest. You can manage your communication preferences via our Preference Center. You can learn more about how we handle your personal data and your rights by reviewing our Privacy Policy.