Scholar
Marcos Machado
Google Scholar ID: TFul1UYAAAAJ
Assistant Professor, University of Twente
Citations & Impact
Citations
522
H-index
7
i10-index
6
Publications
20
Co-authors
9
list available
Resume
Academic Achievements
- - Published multiple papers including 'Advancing credit risk assessment in the retail banking industry: A hybrid approach using time series and supervised learning models' (2025)
- - 'An analytical approach to credit risk assessment using machine learning models' (2025)
- - 'Advanced analytics to improve energy efficiency of steel industry - A systematic review on ladle logistics' (2025)
- - 'How can consumers without credit history benefit from the use of information processing and machine learning tools by financial institutions?' (2025)
- - 'Modeling commodity price co-movement: building on traditional time series models and exploring applications of machine learning algorithms' (2025)
- - 'Predicting retail customers' distress in the finance industry: An early warning system approach' (2025)
- - 'Green AI in the Finance Industry: Exploring the Impact of Feature Engineering on the Accuracy and Computational Time of Machine Learning Models' (2024)
- - 'How can Artificial Intelligence (AI) be used to manage Customer Lifetime Value (CLV)—A systematic literature review' (2024)
- - 'How can artificial intelligence help customer intelligence for credit portfolio management? A systematic literature review' (2024)
Research Experience
- - Over seven years of experience working in the Brazilian and Canadian banking industries
Education
- - Ph.D. in Modelling and Computational Science from Ontario Tech University (Canada)
- - MSc in Industrial Engineering from the University of Sao Paulo (Brazil)
- - BSc in Mathematics from the Federal Institute of Education, Science, and Technology of Ceara (Brazil)
Background
- Currently an Assistant Professor in Business and Information Systems at the Industrial Engineering and Business Information Systems (IEBIS) department of the University of Twente. His current research interests are focused on the applications of machine learning algorithms, data science, and analytics to solve business problems.
