Applications of Machine Learning for Behavioral Modeling in Financial Institutions 2024

This intensive online training aims to make participants familiar with the key machine learning tools used for non- linear statistical inference. Starting from multivariate discriminant analyses methods, multivariate OLS models, Ridge and LASSO regressions, decision trees, random forests, gradient boosting algorithms to artificial neural networks of various configurations also termed as “deep learning”. It will address the key areas of applications in asset liability management and customer credit scoring, commencing with describing the traditional (statistically based) practices and the more recent developments based on machine learning support.

While the first part of the training course is conducted in a traditional lecture style, the second half of the course is case study based and requires attendees to interact among themselves to achieve an optimal output for the cases presented. The course facilitator will act as a moderator and to some degree as a catalyst for eliciting optimal solutions for the case studies. The attendees will be asked to critique the suggested solutions and strive to deploy the best suited models for each business case.