When exploring the potential of Artificial Intelligence in the financial services industry, we need to look beyond the current day and anticipate how this technology could add the most value into the future, across operations, strategy and policy.
Professor Neil Quigley
Vinh-Thuy Tran (BNP Paribas)
Arman Eshraghi (Cardiff Business School)
Peter Cotton (Intech Investment Management)
Albert Bifet (University of Waikato)
David Goad (University of Sydney)
Jianshu Weng (Singapore AI)
Dan DongSeong Kim (University of Queensland)
Adrian Smith (University of Waikato Alumni)
Professor Albert Bifet
We brought together lead researchers, data practitioners and business leaders to talk about the opportunities and need for increasing awareness of the potential and for driving greater adoption of machine learning and artificial intelligence in the financial services industry.
When:
Monday 30 November (NZ Time - GMT+13)
Cost:
Free
Venue:
Gallagher Academy of Performing Arts, Hamilton NZ
Format (hybrid event):
Summit venue: Gallagher Academy of Performing Arts, Hamilton NZ
A mix of academics and industry members presented both research and real-world case studies to share knowledge and know-how about the value of machine learning and AI in finance.
A hybrid-event enabling attendees to participate in person or virtually and connect with attendees from across the world.
Designed to optimise cross-industry learnings & collaboration.
Leaders sharing best practices in leveraging machine learning and AI in the financial services sector.
Professor of Finance at Cardiff Business School
Professor, School of Mathematics and Computing Science at the University of Waikato (in person).
Head of AI - MakerSpace, AI Singapore
Waikato University Alumni
Distinguished Professor, Director of Australian Artificial Intelligence Institute (AAII)
VC at University of Waikato, Chair at the Reserve Bank of New Zealand (In person)
Chief Solution Architect, Automation and Innovation Hub, Sydney University / Microsoft Regional Director
Associate Professor, UQ Cyber Security Research Group, University of Queensland
Chief Data Scientist at Intech Investment Management
Data Analyst & Data Scientist at BNP Paribas
AI Director - Development and Partnerships at the University of Waikato
Event MC - Jannat Maqbool (University of Waikato)
| NZ Time | Session | Summary |
|---|---|---|
| 9 am (8pm GMT) | Welcome - Professor Neil Quigley Vice Chancellor - University of Waikato Session Chair - Dr Robert Durrant (University of Waikato) | |
| 9.15 am (8.15pm GMT) | Challenges in Banking Information Systems | Banking Information Systems (ISs) continuously generate large quantities of data as inter-connected streams, with such data streams needing to be processed online, to deal with critical business applications such as real-time fraud detection, network security attacks prevention or predictive anomaly detection. This presentation will highlight the needs and constraints in the banking sector to apply research and AI based models for streaming use cases. |
| 10 am (9pm GMT) | AI-Finance Nexus: Synergies and Challenges | In the past few decades, advances in artificial intelligence have had a symbiotic relationship with progresses made in the financial services sector. Hedge funds, in particular, have been at the forefront of such financial developments. This presentation will discuss the history and future prospects of the interaction between these two research fronts. |
| 10.45 am (9.45pm GMT) | Morning tea and networking break | |
Session Chair - Professor Ruili Wang (Massey University) | ||
| 11.15 am (10.30pm GMT) | Prediction and Personalisation | This talk will focus on the detection and adaptation of unforeseeable change in streaming data distribution, called concept drift, to increase prediction accuracy; and recommender system technology to support personalised services. |
| 12 pm (11pm GMT) | Frequently Repeated Prediction | In a new kind of prediction network, self-navigating Python, R and Julia algorithms conspire to produce superior electricity predictions than the official forecasts - then automatically review model residuals. This presentation will discuss the potential for collective real-time prediction, and demonstrate a prototypical host at Microprediction.org |
| 12.45 pm (11.45am GMT) | Lunch and networking | |
Session Chair - Dr Nirosha Wellalage (University of Waikato) | ||
| 1.30 pm (12.30am GMT) | Adaptive AI for Finance | Financial AI models trained before the pandemic are now finding that normal has changed, and some are no longer working as they should. Dealing with the evolution of models i.e., with concepts that drift or change completely, is one of the core issues in streaming. This presentation will provide an overview of machine learning for data streams, and introduce some popular open source tools for data stream mining. |
| 2.15 pm (1.15am GMT) | Managing the Risks | As a newer and often misunderstood technology, the deployment of Artificial Intelligence based solutions has a number of novel risks that financial institutions must consider as they continue to increase their reliance on this new form of technology. This presentation will provide insights on potential risks and ways to avoid and to mitigate their impact whilst getting the most out of your AI investments. |
| 3.00 pm (2.00am GMT) | Afternoon tea and networking break | |
Session Chair - Professor Michael Winikoff (Victoria University of Wellington) | ||
| 3.30 pm (2.30am GMT) | Explainability | Every day in the Finance industry, AI models are making insurance underwriting decisions, assessing insurance claims, assigning credit scores, and even buying/selling millions of financial instruments. It is not sufficient for AI models to perform well, we also need to understand how they work. This presentation will look into issues relating to explainability in AI, including what it is, why it is important, and how it can be achieved. |
| 4.15 pm (3.15am GMT) | Cybersecurity and Intrusion Detection | This presentation will cover security threats/attacks to AI (ML/DL) models and existing defence techniques, including some well known Machine Learning adversarial attacks such as poison, evasion attacks. Detection and robustness techniques will also be introduced including current research in Intrusion Detection. |
| 5.00 pm (4.00am GMT) | AI in Financial Services - Build, Buy, or Partner? | How can we (quickly) turn data into insight, when dealing with unstructured data sets such as customer verbatim? This presentation will consider the option to build - is this a trap for organisations of scale?, Buy - how good is AI straight out of the box?, or Partner - is there a middle ground that takes the domain expertise of the business and mixes this with the data scientists who live and breathe AI? |
| 5.45 pm (4.45am GMT) | Closing - Professor Albert Bifet LTCI, Télécom Paris & University of Waikato | |
| 6 - 7 pm (5 - 6am GMT) | Networking |
