UMNAI’s Ken Cassar will be attending the 7th European COST Conference on Artificial Intelligence in Finance organised by COST Action Fintech and Artificial Intelligence in Finance. The aim of this conference is to bring together European academics and industrial practitioners to discuss the application of Artificial Intelligence in Finance. The conference is hosted by the BFH Bern University of Applied Sciences and will be held in person in Switzerland and also online.

The Institute for Applied Data Science & Finance aims to establish itself as a leading Swiss research institute for data-driven, finance-based and strategic insights, analysis and value creation. To this end, an interdisciplinary team of around 25 researchers conducts research and teaching on topics relating to data science, data ethics, data and technology management, data-based business models, corporate financing, digital financing, taxation, accounting and financial reporting.

UMNAI’s Neuro-symbolic AI forms the foundation of our Hybrid Intelligence Framework, a system that enables the integration of human knowledge with explainable AI in a collaborative manner. The framework empowers decision workflows that apply intentional reasoning and oversight to ensure fit-for-purpose decisions. In Hybrid Intelligence powered decision workflows, human crafted tests, measurements, and assessments intelligently interrogate each prediction to fully understand it. With awareness and understanding, decisions are programmatically aligned to meet the intentions and obligations of the operator.

UMNAI’s Neuro-symbolic AI and Hybrid Intelligence Framework are particularly suited to the application of AI in regulated industries such as financial services, insurance, and other highly regulated industries.

If you’d like to meet Ken at the conference to learn more UMNAI’s about Hybrid Intelligence and Neuro-symbolic AI for FinTech and financial applications please contact us.

Check out the conference programme HERE.

Register for the conference HERE.

About FIN-AI COST ACTION

The financial sector is the largest user of digital technologies and a major driver in the digital transformation of the economy. Financial technology (FinTech) aims to both compete with and support the established financial industry in the delivery of financial services. Globally, more than $100 billion of investments have been made into FinTech companies and Artificial Intelligence (AI) since 2010, and continue growing substantially. In early 2018, the European Commission unveiled (a) their action plan for a more competitive and innovative financial market and (b) an initiative on AI with the aim to harness the opportunities presented by technology-enabled innovations. Europe should become a global hub for FinTech, with the economy being able to benefit from the European Single Market.

The FinAI COST Action will investigate AI and Fintech from three different angles: Transparency in FinTech, Transparent versus Black Box Decision-Support Models in the Financial Industry, and Transparency into Investment Product Performance for Clients. The Action will bridge the gap between academia, industry, the public, and governmental organizations by working in an interdisciplinary way across Europe and focusing on innovation.

The key objectives of the Action are:

·       to improve transparency of AI supported processes in the Fintech space

·       to address the disparity between the proliferation in AI models within the financial industry for risk assessment and decision-making, and the limited insight the public has in its consequences by developing policy papers and methods to increase transparency

·       to develop methods to scrutinize the quality of products, especially rule-based “smart beta” ones, across the asset management, banking and insurance industries.

Research Goals

·       Develop approaches to evaluate innovative financial services and their providers, especially in the FinTech domain, building on Machine Learning methods, focusing on prediction (early warning) of operational fragility, fraudulent and illegal behavior ranging from appropriation of loaned funds to money laundering activities

·       Development of conceptual and methodological tools for establishing when black-box models are admissible and, to the extent possible, making them more transparent

·       Receive input from regulators and practitioners’ communities and validate results with regard to increasing transparency of artificial intelligence applications

·       Development of methodologies for reducing the false discovery rate in financial research and applied financial investment management

·       Disseminate to the public and share with regulators the results on investment product performance

·       Creation of the first European platform comparing the out-of-sample performance of banks’ investment products, insurance-linked investment products and asset management products available to the general public