3 citations · 5 across the 3 of their papers we have counts for
3 papers
The Challenger: When Do New Data Sources Justify Switching Machine Learning Models?
Vassilis Digalakis, Christophe Pérignon, Sébastien Saurin +1
Organizations often have an incumbent predictive model in production when new data sources become available. Because historical training data lack the new features, a challenger mo…
Measuring the Driving Forces of Predictive Performance: Application to Credit Scoring
Hué Sullivan, Hurlin Christophe, Pérignon Christophe +1
As they play an increasingly important role in determining access to credit, credit scoring models are under growing scrutiny from banking supervisors and internal model validators…
The Fairness of Credit Scoring Models
Christophe Hurlin, Christophe Pérignon, Sébastien Saurin
In credit markets, screening algorithms aim to discriminate between good-type and bad-type borrowers. However, when doing so, they can also discriminate between individuals sharing…