STAC Summit, 27 June, 2024, Singapore

• Tinku Gupta, Chief Information Officer, SGX Group

Tinku offered words of welcome from SGX Group.

• Jack Gidding, CEO, Strategic Technology Analysis Center

The STAC Benchmark Council’s mission is to accelerate technology discovery and assessment in the finance industry. Jack will outline how that works and how trading and investment firms can benefit.

• Jack Gidding, CEO, Strategic Technology Analysis Center

Jack will provide a preliminary look into STAC-AI™, an LLM benchmark suite guided by the priorities of financial firms, which measures a full solution stack -- from the model to the metal™

• Warren Yong, Head of Technical Account Management, Asia, KX

Getting large language models (LLMs) to provide accurate and relevant output is a well-known challenge. It’s even more formidable in the fast-moving landscape of capital markets. Trading and investment firms care deeply about the sequence of events and how information changes over time. The key to unleashing the power of GenAI for markets is to integrate source content into a timeseries framework and to enable LLMs to operate on the wealth of traditional financial timeseries that firms already amass. Through practical, high-impact use cases in alpha gen, trade execution, and risk, Warren will argue for a new kind of retrieval augmented generation (RAG) that fully leverages the temporal properties of information.

• Thijs Jacobs, Group Chief Technology Officer, SGX Group
• Sanjeev Chatrath, Partner, Financial Services, EY
• Stuart Loy, AWS Solutions Architecture Leader, Global Financial Services, AWS
• Philippe Damay, Senior Presales Engineer, KX
• Chris Morgan, Vice President, AI Solutions, VAST Data

Most financial firms experimented with LLMs in 2023. Some have solutions in production, and those who don't are mostly planning to in 2024. Once an initial solution is in production, AI architects will face the consequence of their success: demand for more users and use cases. But scaling generative AI is more complicated than usual. With more use cases come broader model governance challenges. LLM instances can be in short supply, scattered geographically, and diverse in capability. And fixed or variable costs can be "eye watering" (as one hedge fund described their first bills for AI inference). Fortunately, every few days the research and vendor communities supply new models, software, hardware, managed services, and design patterns to tackle these problems. Which of these have the most promise and will be practical in 2024? And what kind of architectural frameworks are best in a world of such rapid change? Our panel of experts will dig into these questions and yours.

• Jack Gidding, CEO, Strategic Technology Analysis Center

Jack will discuss the latest Council activities and test results relating to 1) derivatives risk computation and 2) deep time-series analytics.

• Ankur Gupta, Director, E-Trading Data Analytics, Bank of America
• Atul Vidwansa, VP Sales (APAC), DDN
• Dave Weber, Director and CTO, Global Financial Services Industry, Lenovo
• Alexis Crowell, VP and CTO APJ, GM Technology Solutions, Software & Services, Intel

Banks and hedge funds require more compute, storage, and networking to meet increasing demands for trading and risk analytics. Data volumes continue to balloon, regulations require more simulations, and new market opportunities require new analytics. However, generative AI and cloud have famously become "gravity wells" for the IT industry, driving its product roadmaps. On the one hand, this may increase the options available for financial HPC and data-intensive workloads, driving down long-term costs. On the other hand, AI and hyperscaler architectures can differ in important ways from those of today's trading and risk analytics, which presents challenges. To what extent can the finance industry benefit from the new products coming forth? How much longevity is left in existing approaches? Are there opportunities (or even imperatives) for trading and investment firms to rethink how they design their applications and infrastructure?

• Jack Gidding, CEO, Strategic Technology Analysis Center

Jack will discuss the latest Council activities and test results relating to 1) low-latency LSTM inference on market data and 2) network stacks in the cloud and on the ground.

• James Shannon, Head of Execution Research APAC, Optiver
• Patrick Miyamoto, Specialized Electronic Trading Infrastructure, Morgan Stanley-MUFG
• Davor Frank, Director - Global Fintech Network Sales Engineering, AMD
• Louis Liu, CEO, Shengli Technologies

To stay in the game, trading firms must manage ever-growing data rates and keep their architectures competitive, whether it’s making software faster or hardware smarter. But mounting requirements for regulation, compliance, and cyber are straining resources. Meanwhile, finding well-trained talent is only getting harder. What are the best strategies to navigate these constraints? What are the best buy/hold/sell strategies for technologies across the spectrum, from FPGA and ASIC to private clouds? Where does it make sense to buy third-party logic today? Our panel of experts will weigh in. To kick off, some of the panelists provided a short presentation: