A linear programming alternative to discriminant analysis in credit scoring

Abstract

T h r typical technique used to ronstruct c:rc.dit scwing modrls is discriminant analFsis. This paper presents a descriptivc. example arid empirical analysis to illlistrate how linear progtamming rnight hr used to solve di .iminant type problems. Results of indic,ated that the linear Ixogr;irnmiiip proredurr performs w e l l in sol\ ing the examplc credit scoring problrm. In addition, the s~ruc,ture of thc linear progralnming inotlel was such that changes cwnld he readily initde to reflect c.it1it.r cwriwrvative or liberal lending policies.

The Use of PCA in Reduction of Credit Scoring Modeling Variables: Evidence from Greek Banking System

“…This preference of us related to LR on the accuracy of the fitted Credit Scoring Model and the predictive probability of being 'bad', is something which is a research subject for a long time in the topic of CSM, though a lot of alternative ways are present in Credit Rating Modeling. For example, an alternative way to the problems of Discriminant Analysis, an alternative way of separation of the groups of 'bad' and 'good', appears instead of Discriminant Analysis, in [8]. A recent paper, in which Neural Networks (NN) are compared to linear regression Credit Rating Modeling if the distribution of the dependent variable is 'skew', is [9].…”

Can Sentiment Analysis Help Mimic Decision-Making Process of Loan Granting? A Novel Credit Risk Evaluation Approach Using GMKL Model

“…Meanwhile, the operational research (OR) models also enjoy great popularity in credit scoring such as linear programming (LP) and integer programming. Hardy and Adrian [5] found the linear programming classifiers could perform as well as statistic methods while Kolesar and Showers [6] developed AT&T scorecard applying the integer programming.…”

Applications of artificial intelligence technologies in credit scoring: A survey of literature

“…There are many statistical and optimization approaches have been proposed to build the credit scoring models. Such as logistics regression [1], linear discriminate [2], decision tree [3], linear programming [4,5], k-nearest neighbor [6] and their hybridization. These methods have been proven to be a relatively simple and excusable for building the credit scoring models.…”