The gig economy is set to surpass $873 billion by 2027, marking it as one of the fastest-growing sectors globally. Yet insights on gig mobility remain largely inaccessible and fragmented. In this session, we’ll explore how investors are harnessing gig mobility data to gain a differentiated view of the market and uncover insights into the performance of hundreds of publicly traded and thousands of privately held companies worldwide.

This session will outline global trends impacting the industrials sector, including trade policies, geopolitics and supply chains. What are the common challenges faced by investors looking to track this industry and which dataset types are growing in popularity and impact?

As technological advancement continues to have a profound impact on the evolution of quantitative investing, how are funds leveraging alternative data to deliver alpha and manage risks? What challenges do quants face in sourcing and integrating new datasets? How can artificial intelligence optimise investment strategies and where does human oversight remain essential?

With government data quality declining due to low survey response rates and budget cuts, understanding the effects of growth, inflation, geopolitical shifts, and policy shocks on the worldwide economy is increasingly difficult for asset managers. This session will discuss an innovative multimodal AI approach, designed to increase the depth and breadth of domain expertise, while maintaining explainability. The demonstration will track global labour markets - generating JOLTS+ data and the effect of Trump administration policies - in real-time.

The continued rise of ESG investing has fueled a new branch of alternative data, but despite growing popularity, ESG policies and practices continue to attract criticism from many. This session will consider some of the problems with ESG data, look at the current regulatory landscape and highlight the potential advantages and use cases for risk mitigation and alpha generation.

As investment managers navigate an ever-expanding array of data sources, extracting meaningful insights can be challenging. Large language models (LLMs) offer robust capabilities, but their limitations in managing complex, dynamic databases are increasingly evident. This session will explore how Agentic AI, a framework that integrates adaptive, task-specific agents, addresses the challenges of data overload, enhances decision-making and uncovers alpha in an evolving investment landscape.

Amid the AI-boom, some companies have faced accusations of ‘AI-Washing’, or falsely overstating the use of Artificial Intelligence to capitalize on the attention this trending technology attracts. How can alternative data help to expose AI innovation vs hype? Where is AI having a tangible impact on data optimisation in the investment sector? What shifts are we seeing in the regulatory landscape?