Leveraging Data as a Differentiator

Unlocking the Power of Historical Data in FinTech

The financial markets are evolving at an unprecedented pace, with firms increasingly seeking high-quality historical market data to drive smarter trading decisions. In a special episode of FinTech Focus TV, recorded live at TradingTech Summit London, Toby Babb sits down with Elliot Banks, Chief Product Officer at BMLL, and Ben Collins, Head of Sales (EMEA & APAC) at BMLL, to explore the critical role that historical data plays in market analytics, AI-driven trading, and overall financial technology advancements. The conversation highlights the transformative power of data in modern financial markets and how firms can harness its potential for superior decision-making.

The Growing Demand for High-Quality Historical Data

As financial markets continue to generate vast amounts of information daily, firms are recognising the value of level 3 historical data as a key differentiator. BMLL has positioned itself as a leader in this space, providing high-quality, consistent historical data across more than 120 venues. Elliot Banks explains how BMLL’s mission is to make historical market data more accessible, allowing firms to analyse trends, enhance trading strategies, and improve execution efficiency. With enormous datasets being produced—up to four terabytes daily from the U.S. equity options market alone—managing, cleaning, and leveraging this data has never been more critical. The implications extend far beyond simple data access; firms that can effectively handle and process large datasets will ultimately gain a substantial competitive advantage.

Why Firms Are Moving Away from Real-Time Providers for Historical Data

One of the major shifts in the industry is the increasing recognition that real-time and historical data should be managed separately. Ben Collins highlights that many firms, including hedge funds, banks, asset managers, and sovereign wealth funds, are realising they no longer need to source historical data from real-time providers. Instead, they are turning to specialists like BMLL, who focus exclusively on historical data and provide cleaner, more reliable datasets that can be used for AI-driven trading, post-trade analytics, and market structure analysis. This change underscores the importance of strategic data sourcing, ensuring that financial firms can rely on comprehensive, accurate datasets that support better trading strategies and investment decisions.

The Evolution of Market Data and Trading Strategies

Toby Babb notes how the industry’s perception of historical data has shifted over the past few years, with firms now viewing historical analytics as a speciality. Elliot Banks agrees, explaining that firms are increasingly aware of the distinction between real-time decision-making and the deeper analysis that historical data enables. Whether it’s for alpha generation, regulatory reporting, or performance benchmarking, firms that leverage high-quality historical data can make better-informed decisions, ultimately leading to stronger trading outcomes. The ability to efficiently sift through vast historical datasets and derive meaningful insights is what separates successful firms from those that fall behind in an increasingly data-driven market.

The Role of AI in Financial Markets and Its Dependence on High-Quality Data

Artificial intelligence and machine learning continue to dominate conversations in FinTech and trading technology, but both Elliot and Ben emphasise that AI is only as good as the data it’s built on. Poor-quality data leads to inaccurate models, wasted resources, and unreliable outputs. The cost of managing and maintaining vast datasets has grown significantly, making data integrity and quality paramount. Without clean data, quants and analysts spend valuable time scrubbing datasets instead of extracting actionable insights. The reliance on AI for financial modelling, risk assessment, and trading automation necessitates a solid foundation of structured, validated historical data to ensure accuracy, reliability, and efficiency in financial markets.

BMLL addresses this challenge by delivering structured, reliable data that firms can use to power AI models, predictive analytics, and advanced trading algorithms. With cloud-based solutions such as Snowflake and Databricks, firms can now implement cutting-edge AI technologies more efficiently, ensuring their models are trained on accurate, gap-free datasets. The future of AI in financial markets hinges on the ability to harness data-driven strategies that improve trading precision, risk management, and overall market efficiency.

Overcoming AI Scepticism in Trading and Investment Firms

During the episode, the conversation shifts to the ongoing scepticism surrounding AI in financial markets. Toby references a recent survey that found 7% of professionals still believe AI is just hype, though Elliot suggests the actual percentage is likely higher. Many financial institutions remain cautious, not necessarily because they doubt AI’s capabilities, but because they struggle to find practical, revenue-generating use cases for the technology. Understanding the business impact and tangible applications of AI remains a crucial step in widespread adoption within financial markets.

Ben Collins points out that while some remain sceptical, many firms are already integrating large language models (LLMs) into their trading and market analysis workflows. He notes that AI’s true potential lies in its ability to optimise workflows, improve efficiency, and provide deeper market insights. Whether it’s helping hedge funds make better trading decisions or enabling HR departments to streamline operations, AI is already making an impact across multiple facets of financial services. This underscores the necessity for firms to invest in AI-driven analytics while ensuring robust data governance frameworks underpin them.

Market Structure, Trading Efficiency, and Data-Driven Decision Making

A core theme throughout the discussion is how market structure is evolving and how data can be leveraged as a differentiator. Ben explains that BMLL’s data helps firms better understand liquidity patterns, execution strategies, and market share dynamics. By analysing historical order book data, traders can make more informed decisions about where to execute trades for optimal results. This not only enhances trading performance but also provides strategic insights into evolving market trends, allowing firms to anticipate shifts in liquidity and adjust their strategies accordingly.

One of the key innovations in this space is the ability to use AI-powered analytics to predict liquidity trends. For example, traders could use historical data to answer questions like, “Where will I find the most liquidity for a Vodafone order between 9 AM and 11 AM?” These insights can drastically improve execution strategies and overall trading efficiency. Understanding how liquidity flows within different trading environments provides firms with a powerful competitive edge, enabling them to execute trades with greater precision and reduced market impact.

The Future of Market Data and Trading Analytics in 2025

As the conversation draws to a close, Toby asks Elliot and Ben about their outlook for 2025 and beyond. Ben explains that BMLL is continuing to expand its reach across different client types, having started with exchanges, then moving into the sell-side, agency brokers, and now buy-side firms. The goal is to establish a common language for data and analytics that can be used across the entire trading ecosystem. By creating consistency in market analytics, BMLL is helping exchanges, brokers, and institutional investors communicate more effectively and make better trading decisions. Firms that integrate data-driven insights into their trading operations will be best positioned to navigate future market complexities.

The Critical Role of Historical Data in FinTech’s Future

This episode of FinTech Focus TV provides invaluable insights into the changing landscape of market data, AI-driven trading, and FinTech innovation. With firms increasingly recognising the importance of historical data analytics, BMLL is at the forefront of delivering the structured, high-quality datasets needed to drive the next generation of trading strategies. Harnessing historical market data effectively will be the defining factor in shaping the future of trading and financial services.

As FinTech recruitment specialists, we at Harrington Starr understands the critical need for top talent in quant finance, trading technology, and data analytics. If you’re looking to build a team of experts who can harness the power of historical market data and AI, get in touch with us today.

This discussion is relevant to Harrington Starr as a FinTech recruitment firm because it highlights the growing demand for data-driven talent in the industry. As financial markets become more reliant on high-quality historical data, firms like BMLL are at the forefront of developing solutions that optimise trading strategies, improve AI models, and enhance market structure analysis. This shift means that the need for professionals skilled in quant finance, data analytics, AI, and trading technology is greater than ever.

Harrington Starr plays a crucial role in helping businesses find the right talent to capitalise on these advancements. With firms recognising the importance of structured, high-quality data, hiring experts who can build, manage, and leverage these datasets is essential. The conversation around AI scepticism and adoption in financial markets also underlines the demand for professionals who understand machine learning applications, regulatory challenges, and risk management.

This episode of FinTech Focus TV with Elliot Banks and Ben Collins of BMLL, hosted by Toby Babb, highlights the critical role of high-quality historical data in driving AI, trading strategies, and market efficiency. As firms navigate evolving market structures, the demand for structured, reliable data continues to grow. The conversation reinforces how data-driven decision-making is shaping the future of FinTech and trading technology. With firms increasingly recognising the value of historical analytics, the need for specialist talent in quant finance, AI, and market data has never been greater. BMLL’s innovations demonstrate why data quality is the foundation of FinTech’s future.

By staying connected with industry leaders like BMLL, Harrington Starr ensures that its recruitment strategies align with market trends, helping companies hire data-driven professionals who can drive innovation, efficiency, and competitive advantage in the FinTech sector.