Jan Vanthienen

TL;DR: This paper contrasts the performance of a neural network survival analysis model with that of the proportional hazards model for predicting both loan default and early repayment using data from a UK financial institution.

TL;DR: This study investigates the detection and qualification of the most relevant explanatory variables for predicting purchase incidence using Belgian data, and extends beyond the standard recency frequency monetary modeling semantics by including alternative operationalizations of the RFM variables, and by adding several other (non‐RFM) predictors.

TL;DR: In this paper, the authors compare the performance of a neural network survival analysis model with that of the proportional hazards model for predicting both loan default and early repayment using data from a UK financial institution.

TL;DR: A complete framework to assess the overall performance of classification models from a user perspective in terms of accuracy, comprehensibility, and justifiability is proposed.

TL;DR: The results show that combining call-detail records with traditional data in credit scoring models significantly increases their performance when measured in AUC, and the calling behavior features are the most predictive in other models, both in terms of statistical and economic performance.