Women in AI and Finance: Weds Afternoon
Artificial intelligence and machine learning have the potential to transform finance with wide-ranging applications from fraud detection to digital investment advisors. Our goal is to bring together women at the intersection of AI and finance and create a forum where they can share their experience, ideas and vision. We encourage professionals, AI/ML researchers and practitioners across industry and academia to participate in the workshop and advance AI in Finance through collaboration. With the workshop, we aim to improve the visibility of women in AI and Finance, provide an opportunity for networking, and encourage mentorship of junior female researchers in the field. Hopefully, we can all learn from each other’s perspectives and make some new connections along the way.
- Charese Smiley ( JP Morgan Chase )
- Zhen Zeng ( J.P. Morgan AI Research )
- Erin Babinsky ( Capital One )
- Lisa Huang ( Fidelity )
- Renyuan Xu ( UC Berkeley )
- Rene Zhang ( Fidelity )
Time Series in Finance: Representations and Learning: Weds Morning
Time series analysis is an old art with applications in many disciplines such as finance, medicine, commerce, and weather. Time series are central to all applications in the Financial Services – consumer and commercial – payments, lending, trading etc. Understanding time series in the real world is challenging due to complex underlying dynamics. We wish to leverage the successes in underlying methodologies and applications in adjacent fields to learn how they can be applied to finance. This workshop aims to gather theoretical and applied researchers interested in analyzing time series and developing new approaches to process sequential information. Special attention will be given to practical as well as novel representations and learning methods in the diverse group of speakers and attendees.
- Naftali Cohen ( JP Morgan AI Research )
- Yan Liu ( USC )
- Zhen Zeng ( J.P. Morgan AI Research )
- Rami Krispin (Data Science Manager)
- Srijan Sood ( J.P. Morgan AI Research )
AI in Africa for Sustainable Economic Development: Weds Morning
Artificial Intelligence (AI), coupled with an increase in the available data and improved computing infrastructure, is proving to be a significant facilitator in many sectors that are closely linked with achieving the United Nations Sustainable Development Goals. Many African countries have tremendous opportunities to use AI in various sectors including finance, agriculture, healthcare, infrastructure and food security. However, the lack of technical expertise, capacity, and the Covid-19 pandemic pose significant challenges. The use of AI in finance has been dominantly led by practices in the developed world that do not fit well to the unique financial ecosystem in Africa. Moreover, certain challenges exist that hinder the adoption of AI in Africa with examples including data scarcity and lack of suitable computing resources to train state-of-the art deep learning models. The workshop aims to bring together academic researchers, industry practitioners and financial experts to discuss the opportunities and challenges of applying AI to address key developmental problems faced in Africa.
- Mahmoud Mahfouz (J.P.Morgan AI Research)
- Samuel Assefa (J.P.Morgan AI Research)
- Toyin Aguda (J.P. Morgan AI Research)
- Benjamin Rosman (University of the Witwatersrand)
- Girmaw Abebe Tadesse (IBM Research Africa)
- Charese Smiley (J.P. Morgan AI Research)
The workshop will explore AI Governance, Compliance and Safety in Financial Services, with a focus on industrial experience (through the inclusion of practitioner and real-world case-studies). Reliance on algorithms in FS is becoming ubiquitous. Indeed, soon there will be innumerable algorithms making decisions with minimal human intervention in FS. Indeed, scaling to problems beyond humans is a major point of using such algorithms in the first place. With this corporations are increasingly concerned about their algorithms causing major financial or reputational damage. A high-profile case includes Knight Capital’s bankruptcy (~$450M) by a glitch in its algorithmic trading system. In response, governments are legislating and imposing bans, regulators fining companies, and the Judiciary discussing potentially making algorithms artificial “persons” in Law. As such there is an acute need for robust AI governance; under this must be captured two ends of the spectrum, namely legal compliance and engineering tools and solutions to address this. This workshop presents a series of panels and paper presentations that cover this spectrum: compliance, governance, auditing and safety. Each theme will be chaired by leading experts from academia and industry. Broader stakeholders ranging from those working on policy and regulation, to industry practitioners and developers, will be interested. We also anticipate the nature and scope of the AI governance and frameworks presented will inform those interested in systems of governance and compliance to regulation/standards.
- Adriano Koshiyama ( University College London )
- Emre Kazim ( University College London )
- Philip Treleaven ( University College London )
Applications of Natural Language Processing (NLP) and network science in finance have received tremendous attention within the last decade. An increasing number of areas from the applied finance community are successfully leveraging and blending tools from NLP, network analysis and graph machine learning, for tasks ranging from asset pricing, portfolio construction, and risk management, to understanding large scale supply chain networks, market crash and fraud detection. This workshop aims to illustrate the broad interplay between those techniques and analysis tools in the context of financial applications, showcasing a suite of problems of interest to both researchers and practitioners.
- Dhagash Mehta (Vanguard )
- Mihai Cucuringu (University of Oxford )
- Xiaowen Dong ( University of Oxford )
- Stefan Zohren (University of Oxford )
- Nitesh Chawla ( University of Notre Dame )
- Senthil Kumar (Capital One)
Explainable AI in Finance (XAI): Weds Morning
Explainable AI (XAI) forms an increasingly critical component of operations undertaken within the financial industry, brought about by the growing sophistication of state-of-the-art AI models and the demand that these models be deployed in a safe and understandable manner. The financial setting brings unique challenges to XAI due to the consequential nature of decisions taken on a daily basis. As such, automation within the financial sector is tightly regulated: in the US consumer credit space, the Equal Credit Opportunity Act (ECOA), as implemented by Regulation B, demands that explanations be provided to consumers for any adverse action by a creditor; in the EU, consumers have the right to demand explanations for automated decisions under the General Data Protection Regulation (GDPR). Safe and effective usage of AI within finance is thus contingent on a strong understanding of theoretical and applied XAI. Currently, there is no industry standard consensus on which XAI techniques are appropriate to use within the different parts of the financial industry – or if indeed the current state-of- the-art is sufficient to satisfy the needs of all stakeholders. This workshop aims to bring together academic researchers, industry practitioners and financial experts to discuss the key opportunities and focus areas within XAI – both in general and to face the unique challenges in the financial sector.
- Anupam Datta (Carnegie Mellon University)
- Himabindu Lakkaraju (Harvard University)
- Daniele Magazzeni (J.P. Morgan AI Research)
- Francesca Toni (Imperial College London & Royal Academy of Engineering)