Tradeteq has developed an advanced credit-scoring model for SMEs and corporations. It leverages a broad set of data sources, including data on each company in the supply chain as well as each receivable. Its advanced machine learning tools apply a sophisticated, evidence-based credit score for each company.

Clients receive early warning signs when a supplier or counterparty is in distress or at risk of not fulfilling credit or trade requirements. Tradeteq’s algorithms predict the potential impact on each business to ensure the risk of interrupted trade flows or payments is minimised.

Tradeteq is complementing its in-house, London-based AI team with a university-based research team. The six-month collaboration with WUST’s Computer Science department will explore how company and trade flow data can be better analysed to improve supply chain credit analysis.

The project will be led by Dr Tomasz Kajdanowicz, an assistant professor at the Department of Computational Intelligence, and Dr Przemysław Kazienko, Ph.D., an academic professor and leader of ENGINE - the European Centre for Data Science.

Dr Kajdanowicz has authored over 100 research papers in the domain of AI, machine learning, network science, diffusion processes, data mining and decision support systems. He has also been a principal investigator for over ten national and international research projects, and was previously a Visiting Scholar at Stanford University, USA.

Dr Kazienko, Ph.D., has authored over 200 research papers on topics including complex networks, machine learning, data mining and data security. He is also a Senior Member of the Institute of Electrical and Electronics Engineers and a board member of Network Science Society.

Michael Boguslavsky, Ph.D., head of Artificial Intelligence at Tradeteq, comments: “The traditional credit scoring process is outdated, and remains reliant on a small number of accounting entries. Technology is the key to achieving greater transparency and rigour in the credit scoring process and minimising the risks associated with global trade flows.

“By partnering with some of the leading academics in the field of graph machine learning, we hope to explore new approaches for data analysis and take a bolder approach to testing new credit scoring models underpinned by our machine learning technology. This will allow us to identify new models that unlock intelligent data and introduce greater efficiency, transparency and standardisation for importers and exporters when assessing the risks in their supply chain.”