This package has been developed as part of a CIFRE PhD, a special PhD contract in France which is for the most part financed by a company. This company subsequently gets to choose which subject(s) are tackled.
This research has been financed by Crédit Agricole Consumer Finance (CA CF), subsidiary of the Crédit Agricole Group which provides all kinds of banking and insurance services. CA CF focuses on consumer loans, ranging from luxury cars to small electronics.
In order to accept / reject loan applications more efficiently (both quicker and to select better applicants), most financial institutions resort to Credit Scoring: given the applicant’s characteristics he/she is given a Credit Score, which has been statistically designed using previously accepted applicants, and which partly decides whether the financial institution will grant the loan or not.
Two subjects are tackled in this package:
- Reject Inference: using not financed clients’ information to build a scorecard (see
vignette("scoringTools")) - Quantization (discretization and grouping of levels)
The package’s website (obtained from pkgdown) is available respectively at:
Two other packages were made public during this PhD, which tackle more specifically:
- Quantization (discretization and grouping of levels) and interaction screening: package glmdisc (see adimajo.github.io/glmdisc)
- Logistic regression trees: package glmdisc (see adimajo.github.io/glmtree)
TODO:
- A Shiny app explaining all aspects of this work is also accessible by running
runDemo().- all models refit when one is deleted / added in reject inference…
- Gini tab with confidence intervals
- Quantization
- missing some parameters
- Histogram of each feature with default rate
- Histogram of quantized features with default rate
- ROC curve
- gini
- Logistic regression trees
- their parameters
- show tree structure
- ROC curve
- gini
- tabpanel for each segment of each model?
- tests for shiny app
- plot methods for reject_infered class
