Programme

Emilio Carrizosa, Professor of Statistics and Operational Research, University of Seville

Title of keynote: Raiders of the Lost Interpretability

Emilio Carrizosa is a data scientist, Professor of Statistics and Operational Research in the University of Seville (Spain) and President of the Spanish Network for Mathematics in Industry math-in.

The adoption of so called Explainable AI, which is typically 'black box' machine learning models accompanied by post-hoc explainability tools, are becoming more common; however, the concern remains for high risk area: can we trust post-hoc explainers?

His research interests include modelling and optimization in Machine Learning processes, with main focus on interpretability and fairness issues. He has published more than 150 papers on optimization and data-drive decision making, receiving by his contributions, among others, the medal of the Spanish Statistics and Operational Research Society (SEIO), and in 2024 the prize SEIO-BBVA Foundation to the best paper in Statistics and Operational Research applied to Data Science and Big Data.

Stephen Miller, Principal Data Scientist, Equifax

Stephen Miller PhD is a data science practitioner and researcher with nearly two decades of industry experience, including 10 years at Equifax where he has led innovation projects and teams as a technical leader and manager. As a Data Science leader within the Equifax Global DS Lab, he has authored 13 patents and applications and has presented multiple papers at previous conferences; including the CSCC 2023 Best Paper Award, for work on predictive modeling in affordability assessment.

Title of keynote: Machine Learning at the Credit Bureau: The Role of Predictive Modelling in a Regulated Landscape

Technological advancements are enabling the development and implementation of increasingly sophisticated predictive models, which are central to most credit decisions taken by lenders. At the same time, regulators are mandating that these decisions are fair and transparent for consumers. While sharing similar objectives, regulatory requirements vary across different countries. Changing macroeconomic conditions and lenders' own governance processes impose additional constraints on how models can be utilised in order to make such decisions.

Looking primarily through the lens of credit risk and affordability assessment, this talk will explore the ways the needs of regulators, consumers and lenders impact the development and use of predictive models and scores, together with the implications for lenders and credit bureaus.