Antonios Armonis is a data scientist with a background in economics and medical sciences. His expertise lies in applying machine learning techniques to financial and economic data, with a strong focus on predictive modeling, loan performance analysis, and Natural Language Processing (NLP). His thesis explores dynamic loan performance analysis and borrower transition modeling, studying how loans evolve between states such as performing, delinquent, and default, while incorporating factors like loan modifications and macroeconomic conditions.
He has experience in building scalable data pipelines, optimizing large-scale computations, and developing monitoring tools for structured financial data. In addition, he has worked on various AI-driven projects in finance, content moderation, and text analysis.