introduction
Capital market firms continue to invest heavily in data management. Emerging tech-savvy players are using advanced tools to reshape traditional models. By combining meta data and cognitive computing, firms can better understand how their systems interpret architecture, business logic, and data. Meta data and cognitive computing offer powerful capabilities—from smarter information management to AI-driven decision-making—enabling a machine-led approach to capital markets.
What is Meta Data and Cognitive Computing? Meta Data, in simple terms, is a form of structured knowledge representation (using knowledge graphs) for a particular domain or task—bridging the gap between human knowledge and machine understanding.
It analyzes existing legacy data through NLP (natural language processing) and ML (machine learning) technologies and identifies financial terms, entities, and products (such as equity and derivatives), along with prices, brokerage, and rates. It also includes their inter relationships. This reduces the difficulty in search criteria and data retrieval across various systems and applications. The same content is then utilized by cognitive computing via a search platform built using NLP technologies and neural networks. The entire solution facilitates the interpretation of both structured and unstructured data. Over time, this will evolve into an “as a service” model.
common challenges across data management in capital markets
- Data governance challenges: In capital markets, each product can span multiple asset classes, increase complexity and require diverse systems and data sources. As the front-office trades more complex global products, the middle- and back-offices face a surge of data inputs. Instruments like swaps and derivatives pose greater challenges than equities due to their dynamic nature, leading to fragmented platforms and lack of standardization that disrupt daily BAU.
- The infinite spectrum of financial data: Financial data utilizes disparate formatting guidelines. Though several financial firms currently deal with SWIFT message formats, many counterparties use a variety of formats for data representation, ranging from Excel files to rich-text files. Sometimes, naming conventions vary drastically from firm to firm. For example, financial products such as derivatives are complex due to the dynamic nature of data—such as margin requirements, collateral arrangements, and diverse structures like swaps, options, and futures. According to KPMG 70 to 80% of enterprise data is unstructured, residing in PDFs, faxes, images, emails, and other formats.
- Absence of integrated technologies: Financial firms often rely on Bloomberg, Reuters, and SmartStream, which are difficult to integrate for seamless data flow. Many still use outdated platforms not built for modern data needs, leading to error-prone manual governance, which can be costly and inefficient—especially as operations scale. Upgrading these systems is expensive and time-consuming.
current market survey trends and challenges in data management in capital markets
Market surveys highlight a strong focus on data quality, availability, and advanced technology in capital markets. As per Deloitte About 90% of respondents prioritize data integration and reliability, while 75% to 85% emphasize easy access and usability. Between 2023 and 2024, U.S. banks planned to invest $4 billion to $5 billion in data initiatives. AI and cloud transformation are key trends, aiming to reduce costs by 35%, improve performance, lower latency by 30%, and enhance data integration by 25% to 30%.
use case 1: leveraging a Meta Data database for prospective clients’ investment in products
Prospective clients reach out for accurate and timely data for making informed investment choices across various products that a firm or a bank offers. A platform that integrates a broad spectrum of market data resources can help firms utilize data more effectively. It ensures that relevant information is delivered right when it is needed. This capability plays a significant role in gaining prospective clients. Example: Broadridge has developed unified data Meta Data called BRx across asset servicing and trade data environment. BRx involves harmonizing data from various sources and reuse across functions, whereas Tradeverse platform extends the Meta Data to harmonize multiple asset classes for seamless integration between trade and post trade workflows across front-office, middle-office and back-office.
Solution and benefits: Machine learning models interact with various new reports, analyst recommendations, product performance databases, and market trends. Meta Data helps create a knowledge database where financial advisors can perform a natural language-based free text search (For example, “provide the performance of the of ABC fund of PIMCO in the last 6 months” Or “mention the reports where the price of Alpha services stock is greater than $300”). Based on the defined Meta Data, results are filtered to provide relevant data (~80% to 90%) and ~50 to 60% streamlined data integration from disparate sources and 20 – 30% faster decision making by financial advisors to help prospect clients.
use case 2: leveraging “cognitive computing technology” for fund launch
A product team reviews various aspects of funds such as “prospectus investment objective”, prospectus investment policy”, “various instruments”, “fund investment strategy”, “regulatory structure”, “expected share classes for launch”, “derivatives usage”, etc., on prospectus documents (cutting across various languages) which typically deal with varied document types, unstructured formats, dynamic texts (which can be different from fund to fund), resulting in manual-intensive work to identify obligations and ensure compliance. For example: Deutsche Börse AG developed Cognitive Computing frameworks to use RegTech for Post trade digitization for static data representation at a centralized location for interpretation and predictive analytics to manage diversified data sources.
Solution and benefits: An AI-powered bot utilizing cognitive computing query language facilitates quicker searches that can interpret and translate, utilizing a set of concepts and interrelationships within the available text in the funds related documents and 3rd party sources. It utilizes cognitive computing modeling (structuring and representing data from the extracted text) to provide appropriate field inputs, which can then be utilized in downstream processes to update the funds and asset classes in the core data platform systems for fund launch. The same cognitive computing query search, in addition, helps assess compliance. This AI-powered cognitive computing modeling bot can deliver ~15-25% increase in fund launch success rates, ~30-40% improvement in data accuracy, and ~20-30% improvement in resource efficiency.
conclusion
This blog highlights the challenges of data management in capital markets. Firms in this sector can leverage AI, machine learning, and deep learning to enhance productivity, differentiate services, and improve customer engagement. As industry transitions to a machine-driven model, integrating business logic into machines will be crucial. Meta Data and cognitive computing web technologies present significant advancements in this space. While adoption is gradual, early adopters will gain a competitive edge. According to the Boston Consulting Group, most financial institutions are expected to implement advanced modeling techniques based on AI and ML by 2027, driving substantial innovation and new market trends.
