Prof. Stefan Lessmann completed his PhD and habilitation at the University of Hamburg in 2007 and 2012, respectively. He then joined the Humboldt-Universität zu Berlin in 2014, where he heads the Chair of Information Systems. He serves as an associate editor for DSS, IJDS, IJF, and other international journals, as well as a department editor for BISE. Stefan has secured substantial research funding and published several papers in leading international journals (e.g., EJOR, DSS, TNNLS) and conference proceedings (e.g., ICML, ICIS, ECIS). His research focuses on machine learning and artificial intelligence (ML/AI) methodologies, as well as their applications in informing, supporting, and automating decision-making in marketing and risk analytics.
Commonly employed methodologies in this scope include, but are not limited to, natural language processing, causal machine learning, and procedures for explainable and responsible AI. Stefan actively participates in knowledge transfer and consulting projects with industry partners, from start-up companies to global players and not-for-profit organizations.
About
In this course, we will start by reviewing the Basel and IFRS 9 regulation, then, we discuss how to leverage alternative data sources for advance credit risk modeling and do feature engineering and followed by an overview of variable selection and profit driven performance evaluation.
Elevate your learning with Live In-Person Class where you'll engage directly with instructors, ask questions, and sharpen your analytics prowess. Experience the closest thing to interaction and take your skills to new heights.
Who Should Attend
Credit risk/scoring managers, data miners, those involved in model vetting/validation and auditing, risk strategy developers, and credit risk executives.
Learn How To
- Leverage Alternative Data – Discover how to use non-traditional data sources for advance credit risk modeling and feature engineering.
- Smart Variable Selection & Profit-Driven Evaluation – Learn techniques to choose the right variables and assess model performance with a business impact focus.
- Advanced Modeling Techniques – Explore cutting-edge methods like ensemble models, neural networks, and Bayesian networks.
- Master Low Default Portfolios & Validation – Understand challenges in low default environments and best practices for validation.
- Stress Testing & Risk Resilience – Gain insights into stress testing frameworks to strengthen risk management.
- Benchmarking & Performance Measurement – Learn how to assess and compare models effectively for better decision-making.
This Course is HRDC Claimable
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Trainer Profile
Prof. Stefan Lessmann