Planning and Scheduling for Financial Services (FinPlan'23)

ICAPS'23 Workshop Prague, Czech Republic July 9-10, 2023

Aim and Scope of the Workshop

Planning and scheduling are mature fields in terms of base techniques and algorithms to solve goal-oriented tasks. Planning approaches have been successfully applied to many domains including classical domains (e.g., logistics and Mars rovers) and, more recently, in oil and gas as well as mining industries. Similarly, scheduling approaches have also been applied to many industrial applications. However, very little work has been done in relation to the problems in the finance industry, which spans a diverse range of activities in financial markets, corporate finance, insurance, banking, and accounting. Recently, some large financial corporations have started AI research labs and researchers at those teams have found that there are plenty of open planning and scheduling problems to be tackled by the ICAPS community. For example, these include trading markets, workflow learning, generation and execution, transactions flow understanding, fraud detection, and customer journeys. In addition, planning problems tackled in other settings like dialog management and network penetration; and richer problem formations involving planning along with learning and scheduling, would also be relevant here.

Topics of Interest

The workshop includes - but is not limited to - the following topics:

  • planning and scheduling in trading and markets
  • process mining of organizational workflows
  • planning, execution and simulation of organizational processes
  • explainable planning for financial applications
  • goal reasoning in the context of financial services
  • goal and plan recognition from customers behavior or transactions history
  • human-AI teaming in organizational processes
  • plan similarity and plan diversity
  • anomaly detection using planning techniques
  • planning with unstructured information
  • plan visualization and explanations for finance
  • applications of planning, learning and scheduling in financial organizations
  • use cases, datasets, benchmarks and applications of planning for financial services
  • links between planning and other disciplines related to finance (e.g., behavioral economics, econometry, markets, …)

Important Dates

  • Submission deadline [EXTENDED]: April 4, 2023
  • Notification of acceptance/rejection: April 28, 2023
  • Camera-ready upload: May 12, 2023
  • Workshop: 09 July, 2023

The reference timezone for all deadlines is UTC-12, i.e., AoE.

Submission Details

We invite two kinds of submissions:

  • full papers, making an original contribution (up to 9 pages including references);
  • short papers presenting industry challenges or outlining ideas around planning for financial services (up to 5 pages including references);

Please note that paper submissions should not be anonymous, and will undergo single-blind peer review. Every submission will be reviewed by members of the program committee according to the usual criteria such as relevance to the workshop, significance of the contribution, and technical quality. Submissions should be formatted using the ICAPS style. The final submission must be in PDF. Papers should be submitted on Easychair submission page:

Papers should follow the AAAI author kit instructions for formatting.

Workshop Program

The proceedings can be found here.

List of Accepted Papers

The list of the accepted papers are:

Workshop Schedule

Time (GMT)

Session 1

  • [14:00-14:05] Initial Remarks
  • [14:05-14:14] “Predicting Customer Goals in Financial Institution Services: A Data-Driven LSTM Approach”. Andrew Estornell, Stylianos Loukas Vasileiou, William Yeoh, Daniel Borrajo, Rui Silva
  • [14:14-14:23] “Value Detection Rate: A Performance Metric for Payments Fraud Detection”. Danial Dervovic, Saeid Amiri, Michael Cashmore
  • [14:23-14:35] “Deep Reinforcement Learning for Optimal Portfolio Allocation: A Comparative Study with Mean-Variance Optimization”. Srijan Sood, Kassiani Papasotiriou, Marius Vaiciulis, Tucker Balch
  • [14:35-14:44] “Surrogate Assisted Monte Carlo Tree Search in Combinatorial Optimization”. Saeid Amiri, Parisa Zehtabi, Danial Dervovic, Michael Cashmore
  • [14:45-15:30] Panel: “Potential and Challenges for Planning, Reasoning, Learning and Optimization Techniques in Finance”. Shirin Sohrabi (IBM), Daniel Borrajo (JP Morgan), Shailesh S Radha (Borealis Global Analytics LLC)

  • [15:30-16:00] Coffee Break

Session 2

  • [16:00-16:17] “FinRDDL: Can AI Planning be used for Quantitative Finance Problems?” Sunandita Patra, Mahmoud Mahfouz, Sriram Gopalakrishnan, Daniele Magazzeni, Manuela Veloso
  • [16:17-16:34] “Accelerating Benders Decomposition via RL Surrogate Models”. Kyle Mana, Stephen Mak, Parisa Zehtabi, Michael Cashmore, Daniele Magazzeni, Manuela Veloso
  • [16:34-16:43] “Can LLMs be Good Financial Advisors?: An Initial Study in Personal Decision Making for Optimized Outcomes”. Kausik Lakkaraju, Sai Krishna Revanth Vuruma, Vishal Pallagani, Bharath Muppasani, Biplav Srivastava
  • [16:45-17:30] Invited Talk: “LLMs can’t plan, but they can help you in planning.” Subbarao Kambhampati (Arizona State University)
  • [17:30-17:35] Wrap up

Invited Speakers

Subbarao Kambhampati

Subbarao Kambhampati is a professor of computer science at Arizona State University. Kambhampati studies fundamental problems in planning and decision making, motivated in particular by the challenges of human-aware AI systems. He is a fellow of Association for the Advancement of Artificial Intelligence, American Association for the Advancement of Science, and Association for Computing machinery, and was an NSF Young Investigator. He served as the president of the Association for the Advancement of Artificial Intelligence, a trustee of the International Joint Conference on Artificial Intelligence, the chair of AAAS Section T (Information, Communication and Computation), and a founding board member of Partnership on AI. Kambhampati’s research as well as his views on the progress and societal impacts of AI have been featured in multiple national and international media outlets. He can be followed on Twitter @rao2z.

Shirin Sohrabi

Shirin Sohrabi is a principal research scientist and research manager at IBM T.J. Watson Research Center in Yorktown Heights, New York. Her research interests are in the area of Artificial Intelligence (AI) with a focus on AI planning and its many applications. Shirin Sohrabi joined IBM T. J. Watson Research Center in 2012, after receiving her Ph.D. in Computer Science from University of Toronto. She has served as program co-chair of ICAPS 2020, as Novel Application Track co-chair of ICAPS 2018-2019, and as System Demonstration Track chair of AAAI 2018. She received the outstanding reviewer award at ICAPS 2016. She regularly serves on the Senior Program Committees of ICAPS, IJCAI, and AAAI. She is an ACM and AAAI senior member. She is a member of ICAPS executive council and also serves as the diversity and inclusion chair.

Shailesh S Radha

Sailesh S Radha is the founder of the startup Borealis Global Analytics LLC, which is developing an AI-enabled decision support platform that explores and exploits global market opportunities by simulating hundreds of global equity allocation strategies without risking capital. The platform also uses ML/AI models and algorithms to transform financial data from various data providers into country-level insights and sentiments and present them using visually pleasing dashboards. Sailesh is the pioneer of medium-term yield forecast (CY-M), a yield measure, developed from the modified application of Shiller’s PE in an international setting. Sailesh has been published in the Journal of Portfolio Management and the Journal of Beta Investment Strategies. He has received a patent on a pioneering financial analytical database in the recent past and is currently working with his team on some patentable ML-/AI-enabled investment processes. Formerly, he was Vice President at CCM Investment Advisers LLC in Columbia (South Carolina), where he served as Chief International Equity Strategist and Portfolio Manager and conceived the International Stock Valuation Model (I-SVM). He joined CCM after graduating from the Darla Moore School of Business (University of South Carolina) and then receiving his master’s in law & Diplomacy from the Fletcher School of Law & Diplomacy (Tufts University) in Boston, MA.

Daniel Borrajo

Dr. Daniel Borrajo is a Research Director at J.P. Morgan AI Research. He is also a Professor at Universidad Carlos III de Madrid (on leave), where he was Head of the Computer Science Department and Head of the Planning and Learning Group. He has more than 35 years of experience of work on AI, from the research side as well as developing AI solutions for companies. His main research interests are in the integration of the two main AI paradigms: model-based (e.g. AI Planning) and model-free (e.g. Machine learning). He has been Program Chair of ICAPS, regularly serves in the program committee of leading international AI conferences, and he is currently Associate Editor of the Artificial Intelligence Journal.

Workshop Committee

Program Committee

  • Daniel Borrajo (J.P. Morgan AI Research, Spain)
  • Michael Cashmore (J.P. Morgan AI Research, UK)
  • Giuseppe Canonaco (J.P. Morgan AI Research, Spain)
  • Mark Feblowitz (IBM, USA)
  • Fernando Fernández (Universidad Carlos III de Madrid, Spain)
  • Andrew Murray (University of Strathclyde, UK)
  • Alberto Pozanco (J.P. Morgan AI Research, Spain)
  • Rui Silva (J.P. Morgan AI Research, UK)
  • Shirin Sohrabi (IBM, USA)
  • Biplav Srivastava (University of South Carolina, USA)
  • William Yeoh (Washington University in St. Louis, USA)
  • Parisa Zehtabi (J.P. Morgan AI Research, UK)

Organizing Committee

  • Parisa Zehtabi (J.P. Morgan AI Research, UK)
  • Alberto Pozanco (J.P. Morgan AI Research, Spain)
  • William Yeoh (Washington University in St. Louis, USA)
  • Biplav Srivastava (University of South Carolina, USA)


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