AIAF Group

AIAF Group is a research group of ITA focused on using Artificial Intelligence to tackle problems in Finance field. The financial environment is very challenging for autonomous software, however, there are also some promising technologies and some subtle advantages in autonomous technologies. In fact, many financial institutions are using intelligent systems as part of the decision process and also trading real portfolios. Nevertheless, there is a long road ahead in the path to build autonomous finance agents that can beat the best human experts in a consistent way.

There are many possible aplications of AI in Finance. One of the most addressed by researchers is forecasting in Finance, Recent work point to some promising technologies, specially regarding the use of convolutional neural networks, Gated Recurrent Units (GRU), Deep reinforcement learning, N-BEATS and other architectures, specially when using ensemble methods.

The progress of digitization in finance has created new markets, specially the so called cryptomarkets, which includes Bitcoin, Ethereum and many other cryptoassets, which are characterized by high volatility and different views about their viability and methods of valuation. These features and breakthroughs in AI, such as deep learning, bring opportunities for innovation and research. However, there are also many pitfalls in financial environments, which are often misunderstood or simply unknown to many AI practitioners and researchers addressing it.

The use of autonomous financial agents require that trust should be established in its development, deployment, and operation. The concept of trustworthy artificial intelligence has five foundational principles according to some authors: (1) beneficence, (2) non-maleficence, (3) autonomy, (4) justice, and (5) explicability. One may argue that accountability is strongly related to the first two principles, beneficence and non-maleficence, and also to managing conflicts of interests in a reasonable way. It is well known that there are many possible conflicts of interest among analysts, managers and investors. Due to the fact that machine can have controlled or at least formally verifiable interests through software verification and validation, possible conflict of interests can be avoided or at very least controlled in a more efficient way. Autonomy refers in large extent on the promotion of human oversight (e.g., Guidelines), others also consider the restriction of AI-based systems autonomy, where humans retaining the right to decide when to decide at any given time. The justice principle is not to be understood judicially, as in adhering to laws and regulations, but instead in an ethical way. For instance, the utilization of AI should to amend past inequities like discrimination of any kind. The last principle, explicability is without any doubt, critical and challenging for autonomous traders, because given the environment complexity mistakes will happen and the ability to explain and justify past decisions is crucial, in order to build and keep trust in the system. Building trustworthy AI systems to Finance is a strong need but also a significant challenge.

Related Conferences and Journals

Scientific Journals

Computational Economics Journal

ACM Transactions on Economics and Computation

Applied Intelligence

Journal of Computational Science

Financial Innovation

The Journal of Financial Data Science

Evolving Systems

International Journal of Finance and Economics

International Journal of Data Science and Analytics

Quantitative Finance

Expert Systems with Applications

International Journal of Intelligent Engineering and Systems

Journal of Autonomous Intelligence

Workshops and Conferences

ACM International Conference on AI in Finance

WINE - Conference on Web and Internet Economics

ACM Conference on Economics and Computation (SIGECOM)

International Conference on Enterprise Infomration Systems (ICEIS)

Future Technologies Conference

IEEE/WIC International Conference on Web Intelligence and Intelligent Agent Technology

Workshop of Artificial Intelligence Applied to Finance - WAIAF (Organized by AIAF Group)

BWAIF - Brazilian Workshop on Artificial Intelligence in Finance

IEEE Conference on Artificial Intelligence (IEEE CAI 2024)

AIAF Publications

Current Projects

Project Title: Artificial Intelligence in Digital Finance and Cryptomarkets: Challenges and Opportunities

Project Funded by FAPESP.

Start Date: July, 2022, End: August, 2024

Coordinator: Paulo André L. Castro. Members: Attilio Sbrana, Fidel Esteves, Gustavo de Freitas Fonseca, Ricardo Gabriel Pontes Lins

Abstract.Artificial Intelligence (AI) and its related fields such as Machine learning and Robotics are changing the world, as we know it. It is also true for the financial field. Furthermore, the progress of digitization in finance has created new markets, specially the so called cryptomarkets, which includes Bitcoin, Ethereum and many other cryptoassets, which are characterized by high volatility and different views about their viability and methods of valuation. These features and breakthroughs in AI, such as deep learning, bring opportunities for innovation and research. However, there are also many pitfalls in financial environments, which are often misunderstood or simply unknown to many AI practitioners and researchers addressing it. Financial problems may be much harder than recognizing faces or driving cars, because patterns often evolve as time goes by and which information is relevant may change with the financial assets. In this initiative, we intend to (1) clarify and describe the financial environment for AI researchers and practitioners including the several pitfalls (2) develop conceptual and methodological procedures in order to tackle these problems in both traditional and cryptomarkets (3) identify or create tools to support the application of the defined procedures. This project benefits from recent and relevant work published in the intersection of Economics and Computation. In fact, ACM created a special interest group to encourage research at the interface between economics and computation, including Finance, Artificial Intelligence and many other fields. Initiatives as this one can benefit both areas and may have impact in practical applications not just, as AI based solution to financial problems, but it may also contribute to development or improvement of new AI algorithms

ProjectTitle: Data Analysis of the Clear Challenge dataset including Questionnaires and Trading Behaviour

Project Funded by XP Investimentos

Start Date: November, 2022, End: August, 2023

Coordinators: Paulo André L. Castro, Prof. Dr.; Carlos Forster, Prof. Dr.

Abstract.The project consists of analyzing data collected during the Clear Challenge to assess the impact of this competition on the risk behavior of day trader operators. The Clear Challenge is a competition sponsored by Clear Corretora and Grupo XP that aims to aims to educate for risk management in Day Trade in a practical way, encouraging effective change in customer behavior. In this project, we proposed a methodology for the analysis of questionnaire data along with its application on discovering insights from investor data motivated by a day trading competition. The questionnaire includes categorical questions, which are reduced to binary questions, yes-or-no. The methodology reduces dimensionality by grouping questions and participants with similar responses using clustering analysis. Rule discovery was performed by using a conversion rate metric. Innovative visual representations were proposed to validate the cluster analysis and the relation discovery between questions. When crossing with financial data, additional insights were revealed related to the recognized clusters.