Introduction

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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

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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

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Important Dates

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