Problems in the US mortgage industry have shown weaknesses in the standard regulatory and economic capital approaches. Although a significant amount of discussion is occurring around how to segment portfolios or predict key variables in order to better fit the existing formulas, we believe that...

Unintended bias against protected groups has become a key obstacle to the widespread adoption of machine learning methods. This work presents a modeling procedure that carefully builds models around protected class information in order to make sure that the final machine learning model is...

In order to stress test loan portfolios for the impacts of climate change, historical events need to be analyzed to create templates to stress test for future events. Using the 2012 Midwestern US drought as an example, this work creates a stress-testing template for future droughts. The analysis...

How does one optimize a fitness function when the values it generates have a stochastic component? How does one simultaneously optimize multiple fitness criteria? These questions are important for many applications of evolutionary computation in an experimental environment. Solutions to these...

We develop techniques with general applicability in searching for low dimensional chaotic behavior in non-uniformly sampled time series data. This is done through the analysis of a specific example, viz. post-collapse core oscillations in globular star cluster evolution. Using simulation data...