Artificial Intelligence for Finance (AIF) Section

Chair: Danilo Mandic
Email: [email protected]

The International Neural Network Society (INNS) is a leading organization dedicated to the advancement of Neural Networks and related fields. The rapid growth of Artificial Intelligence (AI) in finance has created a significant need for a platform where researchers, practitioners, and industry experts can collaborate and share knowledge, in order to drive progress, advance research and explore the vast potential of AI in the financial sector. The Section on Artificial Intelligence for Finance (AIF-Sec) within the INNS aims to foster a community of researchers, practitioners, and industry experts centered around the leadership role of INNS in the neural networks area, to facilitate the development of AI solutions that transform the financial sector.

Join the AIF Section

Membership

Membership is open to all INNS members interested in AI for Finance. The AIF-Sec also offers membership to Affiliate Members of INNS who are active in the field of AI for Finance.

Governance

The AI for Finance Section will be governed by an executive committee consisting of:

  1. Chair: Professor Danilo Mandic, Imperial College London, responsible for overall strategy and direction.
  2. Vice-Chair: Arta Babaee, Accrete Capital, UK
  3. Technical Coordinators: Giorgos Iacovides and Wuyang Zhou (Imperial College London, UK)
  4. Industry Liaison: Mahmoud Mahfouz, JP Morgan AI Division, London, UK, Dr Anna Cukic Armstrong, Aim Cube Ltd. and University of Greenwich, UK, Thanos Konstantinidis, ex BlackRock

Activities

The activities of the Artificial Intelligence for Finance Section will involve:

  1. Regular section meetings: Section meetings and online discussion forums to facilitate collaboration and sharing of knowledge and resources.
  2. Workshops and conferences: Regular webinars, online panels or online workshops.
  3. Publication and dissemination: Special Issues of international journals, special sessions in conferences, industry panels in conferences.
  4. Education and training: Development of training programs, online courses, and certification schemes for professionals and students in AI for Finance.

Objectives:

The primary objectives of the AIF section are:

  1. Foster collaboration: Provide a platform for researchers, industry experts, and enthusiasts to exchange ideas and resources on the application of AI in finance.
  2. Advance research: Support research on cutting-edge tools in AI for Finance, which improve both personal and corporate financial decision making and risk management.
  3. Industry engagement: Facilitate dialogue between academia and Fintech industry.
  4. Grooming of future talent: Provide a platform for the next generation of researchers to enter the field of AI for Finance.
  5. Knowledge dissemination: Disseminate research findings, best practices, and industry insights to the broader community interested in AI for Finance.

Scope

The AIF Section will focus on developing and applying AI techniques to improve financial modelling, forecasting, and risk analysis, including:

  1. Generative AI for finance: Exploring applications of Large Language Models (LLMs) for extracting nuanced insights from unstructured data to inform trading strategies and market predictions.
  2. Natural Language Processing (NLP) for financial text analysis: Applying NLP to analyze financial text data, such as news articles, social media data, and financial reports, to perform sentiment analysis and extract insights.
  3. Deep Learning for financial time series analysis: Utilizing deep learning techniques to analyze and forecast financial time series data, including stock prices, exchange rates, and commodity prices.
  4. AI for Portfolio Optimization and Risk Management: Developing AI-based solutions for portfolio optimization, risk management, and asset allocation, to balance return and risk in dynamic market conditions.
  5. Explainable AI for financial decision-making: Fostering the development of explainable AI techniques in finance, together with augmented and surrogate datasets enhanced deep modelling strategies.
  6. Big Data Analytics in Financial Markets: Analysing high-frequency high limit order book data to understand order book dynamics, and employing AI to process and interpret large-scale finance data for domain-informed decision making.
  7. Graph Theory for AIF: Applying graph theory to model complex financial networks, exploiting the locality of information to enhance portfolio management.
  8. Green, ethical, and fair Fintech-AI systems: Promoting sustainability to minimise the environmental impact in the development and deployment of AI and ML systems in finance and ensuring ethical standards and fairness in AI models to prevent biases.
  9. Multi-agent systems and Game-Theoretic analysis of financial markets: Developing and applying multi-agent systems to simulate and analyze the interactions of various market participants and utilizing game theory to model strategic behaviour and decision-making processes in financial markets.
  10. Reinforcement Learning in financial trading: Applying reinforcement learning techniques to develop trading algorithms adapt to market conditions and are capable of operating in complex decision-making scenarios.
  11. Federated Learning for financial privacy and security: Utilising federated learning to train machine learning models across decentralized financial data sources, while preserving data privacy and sharing insights without exposing sensitive data.

How to join?

Here’s the link to the membership application form:

Section Membership Application