Lunch and Learn - Session 12: How AI can find themes in 25 years of notes
The Reserve Bank of Australia (RBA) conducts around 900 business liaison meetings each year, gathering insights from over 22,000 conversations with firms since the program began. These meetings provide timely, on-the-ground information about economic conditions across industries and regions. However, as these notes are recorded as free-text summaries, it is challenging for analysts to search, compare, or extract trends. This limits their ability to respond quickly to policy questions, track emerging risks, or use the full value of the data in forecasting. Manual processes may also introduce inconsistency or bias, especially when dealing with large volumes of information. The Liaison Analytics Tool is a secure, in-house AI tool that helps turn 25 years of business liaison meeting notes into a structured, searchable dataset. The tool is designed for use by the liaison team, helping them quickly find relevant insights, track topics, and extract firm-reported data.
Presenter
Callan Windsor
Callan is Head of the Data Science Hub. The Hub exists to harness the full power of data by applying emerging data science techniques to policy or business issues. Callan engages closely with subject-matter experts to formulate problems and develop innovative solutions to inform decisions. Callan's research increasingly draws on unstructured data sources and natural language processing techniques to address a variety of policy questions related to financial stability and banking (Callan Windsor | Researcher Profiles | RBA).
Facilitator
Daniel Parris
Daniel Parris is an Engagement Manager with GovAI, a whole-of-government program in the Department of Finance working to enable responsible and effective AI adoption across the Australian Public Service (APS). In his role, Daniel works with agencies to accelerate AI adoption through information sharing, capability building, and supporting cross-agency connections. He also oversees the APS AI Use Case Library, which is a central GovAI resource showcasing real-world AI applications in Government, promoting reuse, and fostering innovation across the public sector.
Before joining GovAI, Daniel worked across governance, sustainability, and policy settings. This included work on APS climate reporting, ESG research and methodology design for global investors, and academic research on sustainable finance and climate risk. Daniel has also facilitated group programs in diverse settings, from interactive public service workshops to immersive cultural experiences in remote Indigenous communities.
Participant benefits
Motivation and background
The tool’s solution architecture and the new capabilities it offers staff
Incorporating liaison-based text indicators into machine learning forecasting models
Suitable for
All Staff
Category and User level
This learning experience aligns with the Digital Profession at the Foundation level.
Price
Free of charge.