Explore a comprehensive conference talk on loan portfolio engines presented by Stanford's Kay Giesecke at Conf.Startup.ML. Delve into typical questions and challenges in the field, including those specific to Asset-Backed Securities (ABS). Learn about risk classifiers and their underlying mechanisms, as well as time-varying factors and correlation in loan portfolios. Examine the transition function and its applications through practical examples. Evaluate predictive performance and understand pool-level modeling techniques, including limit laws and second-order approximations. Discover computational considerations and strategies for optimizing loan pools, with real-world examples to illustrate key concepts. This in-depth presentation offers valuable insights for professionals and researchers in the fields of machine learning, finance, and risk management.