AI is transforming market data management in finance by driving efficiencies and improving decision-making but while the technology offers substantial benefits, it requires attention to regulatory compliance and ethical considerations to ensure fairness. Below, Dan Kennedy, SVP at Gresham Tech, explains how AI integration is the future of market data provisioning, offering more efficient and user-friendly solutions to financial professionals.
The use of AI is becoming ever more prevalent across multiple areas of finance. According to NVIDIA’s fourth annual State of AI in Financial Services Report, a significant 91% of financial services companies are either exploring AI solutions or actively implementing them in their operations. These firms are leveraging AI to foster innovation, boost operational efficiency, and improve customer experiences.
Among the major growth areas, the use of AI to drive efficiencies in market data management promises substantial productivity gains for the financial services firms involved.
AI’s ability to rapidly process and customise large amounts of content positions it as a transformative tool in this sector. By tailoring data presentation based on user patterns and preferences, similar to how Google Ads works, AI enables users to access relevant information without being overwhelmed by unnecessary data. On top of all this, the natural language interaction style of new AI models will more effectively provision decision makers with relevant content, thereby enhancing the overall user experience.
In the financial services sector, the adoption of AI to customise data for users by enterprise data providers illustrates this trend and will bridge the traditional split between terminal products and bulk data feeds. By analysing previous queries and usage patterns, AI can present the most relevant market data, thereby streamlining decision-making processes for financial services professionals. This typically includes delivering tailored insights on market trends, trading volumes, and risk assessments.
This functionality is akin to AI-powered copilots in applications such as Microsoft Outlook, which assist in drafting emails and summarising actions. However, in this context, it is applied to financial data analysis, portfolio management, and regulatory reporting. The integration of AI in these systems not only improves the accuracy and relevance of the information provided but also significantly reduces the time and effort required by professionals to sift through vast volumes of data, ultimately driving up productivity and improving decision-making capabilities in the financial services sector.
The evolution of AI, particularly with generative models like those from OpenAI, highlights the shift towards natural language interfaces. These models enable users to interact with systems using plain language, simplifying complex tasks such as data analysis, market forecasting, and scenario planning. This evolution enhances the accessibility and usability of market data management systems, making sophisticated tools available to a broader range of users.
However, this integration also comes with regulatory considerations. The EU AI Act imposes a risk-based classification on AI applications, ranging from minimal risk to prohibited use cases like biometric surveillance by private entities. Compliance with these regulations requires rigorous scrutiny and transparency from AI providers, similar to existing standards for ESG and cybersecurity.
For financial institutions, which already allocate considerable resources to compliance, AI offers a potential avenue for operational efficiency, especially in customer screening and anti-money laundering efforts. The challenge lies in balancing AI’s benefits with the need to mitigate biases in training data. Ensuring fair and unbiased AI systems is key to maintaining trust in financial services. AI providers must implement safeguards to prevent biases and systematic exclusion, particularly in customer screening processes.
Moreover, the move to cloud computing for regulatory compliance brings with it its own set of challenges. Institutions must collaborate with cloud providers to ensure flexibility and scalability while avoiding vendor lock-in. The Digital Operational Resilience Act (DORA) in the EU, effective from January 2025, highlights the need for stringent oversight of third-party providers, including cloud services.
Achieving the right balance
The integration of AI in market data management is transforming financial services by raising productivity levels and delivering customised data solutions. The shift towards natural language interfaces makes complex tasks simpler, making them more accessible and user-friendly
However, it’s crucial to balance this technological advancement with regulatory compliance and ethical considerations to ensure AI systems remain fair and unbiased. As financial institutions increasingly adopt AI, they must focus on leveraging these technologies for operational efficiency while upholding strict compliance standards and fostering public trust.
The future of market data provisioning hinges on seamlessly integrating AI, creating more efficient, user-friendly solutions that empower financial professionals to make better-informed decisions with greater ease and accuracy. This harmonious blend of innovation and responsibility will define the next era in financial services and set a new standard for the industry.
About the Author
Daniel Kennedy has 25+ years experience across the financial services and technology industry, exposure to a broad range of geographies and wide-ranging product experience, and helps financial institutions solve broad and complex business challenges.
