Name: Data in the AI Era - Episode 4 - Trading in time: Why temporal AI is the next frontier for capital markets
Start: 2025-07-04T14:22:00Z
End: 2025-07-04T14:22:32.000Z
Location: BrightTALK

KX is the maker of kdb+ the world’s fastest time series database and analytics engine. Built for the world’s most demanding data environments, our industry-leading software can process and analyze time series and relational data at unmatched speed and scale for richer insights that drive better business decisions. Capable of running on any cloud, on prem, or at the edge, KX empowers developers and data engineers to build high-performance data-driven applications and turbo-charge their favorite AI and ML analytics tools. KX is trusted by the world’s leading companies across a range of sectors including capital markets, healthcare, manufacturing and telecommunications to deliver business value including risk mitigation, enhanced regulatory compliance, increased machine uptime and rapid development and launch of new products and services. For more information visit www.kx.com.

Data may be plentiful in capital markets but clarity is often in short supply.

In our latest episode of Data in the AI Era, Peter Finter sat down with Cat Turley, CEO and founder of ExeQution Analytics, to talk about the future of trading analytics and what it takes to turn data into action when speed, trust, and transparency are non-negotiable.

From her early days building analytics platforms at multinational banks to her current role helping firms make smarter use of high-cost data, Cat offers hard-won insights into the human, technological, and cultural forces shaping the front office. Cat’s message is clear: in capital markets, data isn’t the product. Understanding is. And too often, there’s a gap between the two.

Data in the AI Era - Episode 5 -  The future of trading analytics and getting more from your data

In the latest episode of Data in the AI era, host Peter Finter sits down with Heidi Lanford, a seasoned data and analytics leader with experience driving AI adoption at organizations like Fitch Group and Red Hat. Now a co-founder at NavAlytix AI, Heidi brings fresh insights into why AI projects succeed or fail and how businesses can overcome common obstacles to unlock true AI-driven transformation.

AI is often positioned as a purely technical challenge—one that requires better algorithms, larger datasets, and more computing power. However, Heidi argues that these elements, while important, are not the primary barriers to success. Instead, the real obstacles lie in leadership buy-in, cross-functional collaboration, and the ability to drive cultural shifts within an organization. Without these factors, even the most advanced AI solutions can struggle to gain traction or deliver meaningful business outcomes

Data in the AI Era - Episode 3 - AI success isn’t about technology — it’s about leadership

In this episode of Data in the AI era, host Peter Finter sits down with Time Magazine technology pioneer Mark Palmer to explore how capital markets firms are navigating the AI revolution. They discuss the practical applications of generative AI, the challenges of integrating AI into a highly regulated industry, and how AI can enhance—rather than replace—human decision-making.

Data in the AI Era - Episode 2 - Can GenAI drive real value in capital markets?

In the first episode of Data in the AI Era, Erin Stanton shares her expertise on navigating the challenges of data readiness, strategy, and management in today’s AI-driven landscape. Drawing on over 15 years of experience, she reflects on the evolution of her organization’s data strategy and the intentional steps taken to ensure their data supports scalable, high-impact AI initiatives.

Erin discusses how to prepare data for AI through transparency, disciplined processes, and a mindset open to learning from failure. From launching monthly ‘Machine Learning Mondays’ to hand-curating datasets, her team has fostered a culture grounded in collaboration and precision. Her insights demonstrate how a well-crafted data strategy can help organizations unlock AI’s potential while staying aligned with business goals and managing risk.

Data in the AI Era - Episode 1 - Redefining data readiness for AI success

What does it take to stay ahead in crypto’s high-stakes, high-speed environment? 

In a recent webcast, Nick Shindo, Business Development Director for Japan at KX hosted a conversation with Jad Sarmo, Head of Quantitative Development at B2C2, one of the world’s top institutional liquidity providers in digital assets. Together, they explored what it means to operate effectively in a market that never stops moving.

Mastering Crypto Data - Building analytics infrastructure for fragmentation, volatility and real-time edge

In a rapidly evolving landscape, trading research continues to grow in significance. As trading shifts towards more dynamic, learning-based models, understanding and measuring the incremental value of trader decisions, strategy behaviour and model outputs becomes even more critical. Effective trading research should not only explain what happened but delve deeper to explore the reasons driving this behaviour and identify actionable insights to improve future trading decisions.  

In this 45-minute interactive virtual workshop, we invite industry peers from both buy and sell side financial institutions to join us in a discussion, including a live analysis demo and Q&A session with industry experts and market practitioners, on how to go beyond measure & interpret your data to address real-world challenges in trading research and execution performance.

Optimize Your Execution: Trading Research Best Practice

Faster Data. Smarter Decisions. Stronger Outcomes.
The speed and precision of real-time analytics in capital markets are no longer just a competitive advantage—they’re essential for survival. From algorithmic trading and risk management to dynamic pricing and client trust-building, real-time data drives success across the financial world.

In this 40- minute webinar you’ll:
- Learn about the real-time data trends reshaping capital markets.
- Explore how generative AI, temporal analytics, and scalable data solutions are being used.
- Learn how industry leaders are managing billions (even trillions) of data points with groundbreaking technologies like KX.
- Gain actionable strategies to modernize legacy infrastructure without disruption.

Discover how firms like JP Morgan and leading hedge funds are transforming their operations with real-time analytics.

Real Time Insights within Capital Markets

KX and BestX join forces to discuss the many tech, regulatory, and market factors that are driving change within Best Execution Analytics. Topics covered include the adoption of the cloud, regulatory changes such as MIFID2 and RG97, and process automation.

As BestX co-founder Ollie Jerome shares: “A lot of our very large clients have gone through a similar process of management change over the last two or three years where now you have folks from an Equity background managing the cross-asset desks, including Fixed Income and FX.”

BestX is a 7-year-old technology company that partners with KX. They deliver Best Execution Analytics to help over 30 banks and almost 180 of the world's largest hedge funds asset managers clients to define, achieve and record a process of Best Execution across FX, Fixed Income, Equities, Commodities, Futures, and Crypto. They currently represent about $45 trillion of AUM (Assets Under Management).

Innovation, Change and opportunities in the world of Best Execution Analytics

“Three Innovative Quant Trading Apps and Three Ideas Everyone Should Steal”, is an interactive exploration of three innovative applications on Wall Street that leverage high-frequency data in creative ways for sense-and-respond intelligent action (trade execution), live-streaming research and strategy (for intraday trade strategy adjustment), and continuous risk management. While these use cases may seem particular to the capital markets, they exist everywhere.

This one-hour session with Conor Twomey (KX Head of AI Strategy) and Mark Palmer (Time Magazine Technology Pioneer) will explore these innovative use cases and explain three ideas everyone should steal in any application area dealing with streaming and historical data, and high-value decision-making in real-time.

Three Innovative Quant Trading Apps and Three Ideas Everyone Should Steal

Quants develop – and help implement - digital transformation within firms. These ideas can change, or even revolutionize, their organization across functions such as trading and risk management. Whatever your area of focus, today’s market unpredictability presents excitement, opportunities, and challenges for quants including: 
• Market volatility driving high volumes of data
• A model building ecosystem centered on the Python programming language
• The increasing use of machine learning
• The rise of cloud and microservices

Join Conan Hales, CFA, CQF, Solutions Engineer, KX and Harry Darell Brown, VP of Product Management for Financial Solutions, KX to explore the challenges and opportunities of Quantitative Research in 2022.

Referencing real-world examples, our speakers will discuss: 
• Why KX insights is the ideal platform for time series data analysis and model 
        development using languages like Python.
• How KX is positioned for the world of streaming data.
• A case study of how KX enabled an exchange to commercialize their unique dataset.

Register today and discover the future of quantitative research.

Quantitative Research Tooling for High Volume Financial Markets  (APAC)

Using Python, and perhaps Pandas? This webinar explains how you can provide production data workloads to your Python code and Jupyter Notebooks, adding 8x to 100x more data power to your models and reducing the time needed to take research into production.

This webinar explores how to: 
• build a data ingestion pipeline
• dramatically transform Python time series workflows and analytics (asof joins; xbar for time series formatting)
• augment and improve your data science in Jupyter Notebook using KX's Python Interface

Watch this webinar to see a demonstration of a computational finance workflow and to understand the same rigor and capabilities that can be deployed to any use case that utilizes Python and highly granular and high-frequency datasets.

kdb Microservices for Ultra-High Velocities on a Postage Stamp Footprint

With decades of combined experience at the sharp end of the Financial Services sector, the panelists discuss the various requirements that make up the perfect platform for trading analytics. In more detail, they’ll explore how a trading analytics solution should:
• Optimize trading outcomes
• Be futureproof
• Deliver Best Execution

The team discuss the opportunities for optimization and its dependency on finding value from multiple data volumes - and how repeatable any optimizations are. The panel also debate benchmarking, data management, application development, data pipelines, transparency, and more through real-world examples. We'll also have a deeper dive into how TCA can be utilized as a tool to help traders and data scientists better understand how their trades performed and can inform strategies for improvement.

Time Series Analytics - Powering Trading Outcomes (APAC)

When it comes to business initiatives driving technology spend, 1 in 3 organizations state that their top initiative over the next 12 months is to improve their data analytics for real-time business intelligence (Enterprise Strategy Group).  

This is largely driven by the fact that when using data to make informed business decisions, it is no longer enough to rely on business intelligence derived solely from historic (big) data. Instead, to keep pace in the digital era, insight must be combined - even supplanted - by continuous real-time data analytics.  

Taking advantage of real-time analytics, however, is not so straightforward. Data is growing, distributed data environments are increasingly dynamic, and with so many vendors and tools to choose from, organizations need help. 

In this discussion, ESG Senior Analyst Mike Leone is joined by Conor Twomey, Head of Customer Success at KX and Kiran Singh, Data Analytics & Data Science Leader at NextEra Energy, to discuss in-depth how organizations can find clarity from the chaos.  

Join them to learn about: 
- The business objectives driving the need for faster decision making through streaming analytics 
- The common challenges preventing success and the capabilities that matter most in streaming analytics platforms 
- What to look for in a modern data platform and how to determine if it is right for your business 
- How the cloud is supporting real-time data initiatives across different industries 
- A customer case study and common use cases for analytics that are delivering proven value for organizations 
- And more

A New Data Frontier: The Value of Streaming Analytics to a Data-first Business

Time Series Analytics - Powering Trading Outcomes

This discussion between Vats Vana, Vice President of IoT Strategy & Products at KX, and guest speaker Paul Miller, Vice President, and Principal Analyst at Forrester, covers several topical issues in Manufacturing, including:

• The catalysts for change – including a need for deeper analytics from ever-evolving Edge communication.
• Trends and challenges in the industrial IoT space.
• The value of contextual data and the importance of understanding how timely insights need to be.
• The connection between insights, decisions, actions, and outcomes.
• The measurable ROI of data analytics from Manufacturing IoT
 
Calling upon decades of experience, Vats and Paul examine the relationships and conflicts between tech, data, people, and processes, the cultural gap between OT and IT, and attempt to establish whether real-time is the right time. 

Through case studies and real-world examples, Vats will lead the discussion on the value of ‘context’ and how rich insights help drive the decisions, actions, outcomes, and change needed for modern manufacturers to survive and thrive.

The Value of Timely Data in Modern Manufacturing

In a world where the smallest margins represent millions of dollars, attempts to manipulate the market are inevitable. And with few regulations governing this “new” asset class, Cryptocurrency markets are no exception and are considered particularly vulnerable to hacking, fraud, money laundering, and other criminal activity. 
 
Justin Handsman, FICC & Derivatives Specialist, KX, and Charles Gregory, VP of Product Management for Surveillance Solutions, join host Steve Wilcockson, KX to explore the challenges, opportunities, and nuances of Trade Surveillance across multiple markets.  
 
Referencing real-world examples, the team discusses:
• The similarities and differences between traditional and digital exchanges, from the exchange and trader point of view.
• Impact of data volume and the difficulties of building the “perfect picture” - compounded by market depth challenges.
• The importance of real-time transactional visibility.

Trade Surveillance in Equities and Crypto Markets

Data in the AI Era

In this episode of Data in the AI Era, Peter Finter is joined by Ashok Reddy, our CEO, to unpack what it really means to compete in the real-time economy, and why most capital markets firms are still held back by infrastructure that can’t scale. Ashok explains how firms are redefining speed and adaptability, and why temporal AI may be the key to building systems that perceive, reason, and act across time.

Data in the AI Era - Episode 4 - Trading in time: Why temporal AI is the next frontier for capital markets

Real Time Analytics

Artificial Intelligence

Capital Markets

Financial Markets

Analytics

Fintech

Trading Analytics

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Data in the AI Era - Episode 4 - Trading in time: Why temporal AI is the next frontier for capital markets

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