5 ways AI is supercharging research in financial services

As the capital markets industry has expanded both in scope and complexity, research has only become more essential. Since the late twentieth century, globalization, specialization, and increasingly complex regulatory frameworks have all elevated research from an interesting competitive differentiator to a competitive imperative. Now, with the application of increasingly powerful AI solutions, research is poised to become the defining factor in determining winners and losers in a rapidly shifting landscape.  

At Microsoft, we develop highly tailored, long-term technology partnerships with financial services firms around the world. Increasingly, this includes co-innovating with AI to help unlock new business value and deepen customer relationships. At present, enhancing research and analytics with AI is one of the primary transformation levers for investment banks, asset management firms, and financial data and analytics providers. In many cases, it is helping to solve longstanding challenges around deriving greater value from data and rapidly converting insights into competitive advantage. 

Realizing the promise of data-driven research through AI 

AI is rapidly changing the nature and value of advanced analytics in research. Traditional analytics have long helped firms understand what happened and why—but AI is helping them predict what will happen next and prescribe optimal courses of action in real time.

This shift from retrospective analysis to proactive intelligence can help firms unlock new sources of value and ultimately develop groundbreaking new products that redefine the competitive landscape. 

As innovative firms recognize the potential of AI, they also see the opportunity to address longstanding challenges that hinder effective research. Among these:  

• Data overload and complexity
  Financial markets are inundated with massive volumes of data from diverse, often siloed sources that can be difficult to integrate and synthesize. This makes it hard to access the right data at the right time, which can slow decision-making and heighten risk. As data requirements become more complex, solutions are needed that can unify, structure, and analyze data at scale to deliver timely, actionable insights.
• Fragmented workflows across user journeys
  Research analysts frequently struggle to navigate large volumes of disparate data housed in disconnected systems, tools, and formats, leading to time-consuming manual data compilation and synthesis. The increase in non-integrated tools, applications, and data structures disrupts business workflows and can lead to inefficiencies, duplication of effort, errors of omission, and delays in decision-making.
• Dependency on traditional data sources
  Many firms and analysts rely heavily on conventional market reference data, company fundamentals, industry reports, and databases, which often lack real-time insights and limit the speed and accuracy of market predictions. As new opportunities arise, firms need solutions that can extract more value out of existing sources while also making it easy to incorporate alternative and real-time sources—enhancing both predictive accuracy and responsiveness to market shifts.
• Information overload and time constraints
  Research and analyst professionals are always challenged to keep up with reports, emails, meetings, and chats. The overload tends to slow decision-making and increases the risk of missed opportunities. Stringent regulatory compliance requirements add additional demands.  

Five ways AI redefines the value of research in financial services 

AI gives financial services firms new solutions to these longstanding barriers and opportunities to use data in new ways that can differentiate their offerings. Here are five important areas where AI can change the game: 

1. Advance analysis with AI-powered analytics 

AI-powered analytics empower research analysts to cut through the noise of information overload and extract valuable insights with unprecedented speed and precision. The combination of AI with predictive analytics empowers researchers to analyze historical patterns more deeply, identify emerging trends, and make more informed investment decisions. This can ultimately help to improve engagement and win rates. 

A prime example of this is our partnership with Moody’s where we co-developed innovative solutions for research and risk assessment. Moody’s Research Assistant significantly increases productivity and effectiveness, with users reporting up to 80% time savings on data collection and 50% on analysis during the pilot phase.¹  

2. Accelerate operational efficiency through intelligent automation 

Traditional research processes—such as manual data compilation, synthesis, and report generation—are time-consuming and error-prone. AI-powered automation transforms them by integrating data sources, automating repetitive tasks, and promoting seamless collaboration across teams, which results in faster turnaround times, reduced operational costs, and improved operational efficiency.  

With tools like Microsoft Copilot, Researcher agent, and Analyst agent, firms can significantly boost productivity and operational efficiency. These AI-powered assistants can handle such tasks as summarizing investor reports and earnings calls, creating presentation-ready visualizations from raw data, and drafting research documents and client-ready insights quickly. This frees up valuable time for analysts to focus on higher-value activities, such as strategic analysis and client engagement. 

3. Deliver real-time insights 

To help meet the accelerating pace of business, AI-powered applications empower financial services firms to surface real-time insights from a variety of sources including market news, earnings reports, and social media.  

Bridging knowledge across platforms helps analysts identify emerging trends faster and develop better investment strategies. For example, AI can continuously monitor global news sources and sentiment signals to identify early indicators of market shifts and potential disruptions. Firms can then use this information to react swiftly and make proactive investment decisions ahead of competitors. 

Firms can build new AI-powered solutions that incorporate real-time data into advanced searches, personalization, and recommendations, using innovations like the powerful vector database built by KX—essentially, a specialized system that understands the meaning and context of a huge set of data types such as text, images, or PDFs. It aims to help financial institutions seize opportunities faster by turning real-time data into real-time action. 

4. Empower employees with high-value experiences 

AI-powered tools can transform how financial services professionals work with tools and solutions that support the most critical research functions, such as financial modeling and pitchbook preparation. Processes can be significantly streamlined while remaining interoperable, secure, and compliant.  

A good example of this is the innovation resulting from our long-term strategic partnership with LSEG (London Stock Exchange Group) to transform data with next-generation productivity and analytics solutions. One recent advancement is the launch of the LSEG Workspace Add-in, which integrates AI-powered insights into Excel and PowerPoint. With features like contextual data discovery and interactive charting, the add-in can help financial professionals work faster and more insightfully. 

Reducing the burden of manual tasks can also help boost job satisfaction. The integration of AI into daily workflows helps people focus on more intellectually stimulating activities, freeing up time for higher-value analysis and strategic thinking, and helping to attract and retain top talent. 

5. Deepen market understanding 

AI-powered analytics are transforming how analysts understand markets and convert insights into action. By processing vast amounts of financial data in real-time, AI can uncover complex patterns and correlations that were previously undetectable, such as market sentiment from news articles and social media or a real-time pulse on investor sentiment or market dynamics. Machine learning models can predict stock price movements with greater accuracy by integrating diverse data sources, including economic indicators and company performance metrics. 

A richer view of market forces and dynamics translates into better decision-making and sharper investment strategies. It helps firms anticipate emerging risks and opportunities sooner, enabling them to respond faster and more confidently in an increasingly volatile market landscape. 

Now is the time for agentic AI 

A new class of AI tools will soon deliver the ability to plan, reason, and take actions to achieve goals. In financial services, they will be able to gather, analyze, and contextualize information autonomously from diverse sources and proactively surface relevant insights—or even suggest strategic actions based on real-time developments. 

On the near horizon, advanced “orchestrator” agents will focus on new capabilities in distinct functional areas such as market intelligence, data aggregation, strategy simulation, reporting, and compliance. This holds the potential for powerful competitive advantages, helping analysts to stay ahead of market shifts, make more accurate predictions, and deliver higher-impact recommendations. 

Learn more 

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¹ Moody’s Investor Relations, “Moody’s Launches Moody’s Research Assistant,” December 2023.