Abstract: 

The use of classification methods in credit risk analytics is widespread. In recent years, however, the use of survival analysis (which is a method mainly used in the biomedical field) has gained increasing interest because this method allows you to create classifications that change over time, hence creating time-based predictions. The objective of this session is to give a short overview of what types of problems can be solved using survival analysis, why survival analysis is useful, and how to use survival analysis when working with credit data. It is recommended that attendees are familiar with the general idea of regression models.

Bio: 

Lore Dirick is Director of Data Science Education at Flatiron School (https://flatironschool.com). She joined Flatiron School in May 2018, where she built Flatiron School's 15-week Data Science Program, that by now has graduated hundreds of students globally, from the ground up. Before joining Flatiron School, Lore worked as a Curriculum Lead for online data science school DataCamp, where she built out the R and Python curriculum and taught online data science courses to over 70,000 students. She earned a PhD in Business Economics at KULeuven (Belgium), where she was a member of the statistics department and performed research on advanced credit risk modeling techniques.