Name
Abstract | ||
|---|---|---|
We present a methodology for improving credit scoring models by distinguishing two forms of rational behaviour of loan defaulters. It is common knowledge among practitioners that there are two types of defaulters, those who do not pay because of cash flow problems ('Can't Pay'), and those that do not pay because of lack of willingness to pay ('Won't Pay'). This work proposes to differentiate them using a game theory model that describes their behaviour. This separation of behaviours is represented by a set of constraints that form part of a semi-supervised constrained clustering algorithm, constructing a new target variable summarizing relevant future information. Within this approach the results of several supervised models are benchmarked, in which the models deliver the probability of belonging to one of these three new classes (good payers, 'Can't Pays', and 'Won't Pays'). The process improves classification accuracy significantly, and delivers strong insights regarding the behaviour of defaulters. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1057/jors.2014.50 | Journal of the Operational Research Society |
Keywords | Field | DocType |
credit scoring,statistics,domain-knowledge,constrained clustering,banking,game theory | Loan,Actuarial science,Domain knowledge,Willingness to pay,Computer science,Common knowledge,Constrained clustering,Game theory,Cash flow | Journal |
Volume | Issue | ISSN |
66 | 5 | 0160-5682 |
Citations | PageRank | References |
12 | 0.58 | 12 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Cristián Bravo | 1 | 124 | 10.63 |
| Lyn C. Thomas | 2 | 329 | 25.24 |
| R. Weber | 3 | 85 | 7.55 |