Papers should be submitted on CMT3 by 20th October, 2024
Submission URL (Submissions closed): https://cmt3.research.microsoft.com/MLFW2024
The scope and topics of the proposed workshop are (broadly defined and not limited to) machine learning models for financial wellness and investor modelling that:
provide real-time and long-term decision support systems for investments (securities, residential, commercial, etc.), loans and debt consolidation, insurance, real estate, etc. to enhance investors’ financial resilience
act as automatic or semi-automatic assistive artificial intelligences (AI), such as a Robo-advisor or Advisor-in-the-loop (hybrid), for clients, advisors, institutional investors, etc.
considers future investment behavioral biases of clients relative to their history
are used to gain human attention for financial behavior interventions, convince investors to alter decisions, and support financial wellness
incorporate virtual data assistants for more natural interactions with investors
improve financial literacy to guide financial decision-making and minimize errors detrimental to stated investment goals
incorporate equitable recommendations through machine learning constrained by fairness
utilize sentiment analysis, information retrieval, and large language models to analyze phone calls, textual correspondence, or any other alternative data sources for conversational recommender systems
use large language models (LLMs) for information retrieval in financial documentation and behavioural intervention of financial transactions
ethical AI in the realm of recommender systems and investor modelling
Consideration of Multiple Stakeholders in Financial Applications and Services
Transparency and Explainability in Financial AI
Bias Reductions and Fairness in Financial AI
Financial optimization with Environmental, Social, and Governance (ESG)
Human-in-the-loop for Financial AI
Intelligent User Interfaces or Visualizations to Facilitate FAT (Fairness, Accountability, and Transparency)
Human-AI Collaborations in Financial Systems
Overview of Industry Challenges
Short papers from financial industry practitioners that introduce domain specific problems and challenges to academic researchers. These papers should describe problems that can inspire new research directions in academia, and should serve to bridge the information gap between academia and the financial industry.
Algorithmic Tutorials
Short tutorials from academic researchers that explain current solutions to challenges related to the technical areas mentioned above, not necessarily limited to the financial domain. These tutorials will serve as an introduction and enable financial industry practitioners to employ/adapt latest academic research to their use-cases.
All submissions must be PDFs formatted in the Standard ACM Conference Proceedings Template (or, ACM LaTeX templates, use the sigconf template). Submissions are limited to 4-8 content pages, including all figures and tables but excluding references.
Following the conference submission policy, reviews are double-blind, and author names and affiliations should NOT be listed.
Papers that are accepted will be presented as oral presentations, depending on schedule constraints. Abstracts of accepted papers will be posted on the workshop website but will not be archived online by the workshop. Accepted papers will be published and archived in
Conference papers will be encouraged to submit their full manuscripts to the