Decision making is a cognitive process that involves selecting a course of action or choice from among multiple alternatives. It's a fundamental aspect of human life and is present in various contexts, ranging from everyday situations to complex professional, personal, and strategic scenarios, such as resource allocations, risk management, strategic planning, cybersecurity, supply chain management, Web information systems (e.g., information retrieval and recommender systems), and so forth. Factors influencing decision making may include cognitive biases (mental shortcuts that can lead to errors in judgment), emotions, cultural and social influences, personal experiences, time constraints, and the availability of information. Optimization, on the other hand, is a systematic process that aims to find the best possible solution based on specific criteria. It involves mathematical and computational techniques to minimize or maximize an objective function while adhering to a set of constraints. Optimization seeks to identify the globally optimal solution, which is the best solution achievable according to the defined criteria. Multiple optimization techniques have been proposed and applied in machine learning and AI, such as convex optimization, non-linear optimization, evolutionary algorithms, multi-objective optimization, game theories, etc.

Both decision making and optimization have been widely applied in multiple domains, including business strategies, supply chains, fintech, etc. There are several scenarios in financial areas where decision making and optimization play an important role, such as portfolio optimization, risk managements and predictions, sustainability and ESG optimization, financial time-series forecasting, financial fraud detections, customer churn predictions, financial news and reports, large language models for financial services, financial information retrieval and recommender systems, and so forth.

Call For Papers

The primary objective of this International Workshop on Decision Making and Optimization in Financial Technologies (DMO-FinTech) is to facilitate the integration of decision-making and optimization principles within the area of financial technologies and services. Broadly speaking, we welcome submissions from both academia and industry to discuss their latest progress or findings in related areas.

Topics of Interest in DMO-FinTech include but are not limited to:

Decision Making and Optimization Methods (within FinTech Applications)
Group Decision Making and Negotiation Analysis
Multi-Criteria Decision Making
Multi-Objective Decision Making and Optimization
Linear/Non-Linear Optimization
Time-Series Decision Making and Optimization
Evolutionary Algorithms
Applied Optimization Technologies
Deep Learning, Transfer Learning, Reinforcement Learning
Human factor-based Decision Making, e.g., personality, trust, emotional analysis
Visualization and Interface Design to Assist Decision Making and Optimization

Data Mining and Machine Learning Tasks (within FinTech Applications)
Classification, Regressions
Time-Series Predictions
Clustering
Association Rule Mining
Outlier Detection
Feature Engineering

FinTech Tasks and Applications
Business or Financial Analysis
Financial Portfolio Optimization
Risk Management and Predictions
Financial News or Reports
Sustainability/ESG in Financial Investments
Financial Large Language Models (FinLLM / FinNLP)
Scalability and Efficiency in Financial Services
Multilingual Challenges in Financial Services
Conversational Systems/ChatBots for Finance
Multi-Modal Financial Knowledge Discovery
Financial Time-Series Forecasting
Financial Fraud Detections
Customer Churn Predictions
Privacy-Preserving AI for Finance
Financial Information Retrieval and Recommender Systems


Submission Instructions

Authors should prepare their manuscripts by using the Author's Kit.
We accept the following submissions:
Long papers (up to 12 pages, including references and appendix) from academia or industries, to present comprehensive research work, including in-depth analysis, methodologies, results, and discussions.
Short papers (up to 8 pages, including references and appendix) from academia or industries, to discuss innovative ideas, work in progress and key findings.
Industry papers (up to 8 pages, including references and appendix), to share practical insights, experiences, theoretical contributions or research methodologies, and innovations relevant to relevant fields. Note that industry submissions could be industry preliminary results, or technical abstracts/reports.

Please submit your paper via EasyChair System.

To maintain the technical quality of the accepted papers, our workshop uses the double-blind review for all categories of submission. Authors should anonymize author names and identities in the paper submission. The steps for anonymizing your manuscript before submission include:
Remove authorship information (name, institution, titles) from the anonymized version of your manuscript file. ...
Don't mention grants or acknowledgements — those can be added to the manuscript prior to publication. ...
Avoid, or try to minimize, self-citation.
Avoid using terms like "our previous work", "our earlier work", etc.

Post-Publications
The accepted workshop papers will not be included in the PAKDD conference proceedings but are available on the webpage. Springer Nature will collaborate with PAKDD to publish workshop papers in book series (example1, example2).

Another option is to publish your work in a special issue collaborated with an academic journal. For example, accepted papers at the DMO-FinTech Workshop in 2024 were invited to submit their extensions to the Special issue on AI for Financial Services and Applications published in Discover Data, Springer.


Important Dates
Workshop Paper Deadline: February 22, 2026
Workshop Paper Acceptance Notification: March 15, 2026
Workshop Paper Camera-ready: March 29, 2026