TY - JOUR

T1 - Graphical models in credit scoring.

AU - Sewart, P.

AU - Whittaker, J.

PY - 1998

Y1 - 1998

N2 - Graphical models simplify the analysis of multivariate observations by summarizing conditional independences in the data. Variables are represented by nodes, and the absence of an edge between two nodes signifies their conditional independence. While graphical modelling has been used in several applications of statistics, credit scoring has only recently been suggested as a suitable candidate. This paper suggests the following potential uses for graphical models: to display and interpret the associations between variables taken from a credit-card application form; to compare the credit scoring of subpopulations; to give a description of the credit-scoring selection process in terms of influence diagrams; and to assess the effect of selection bias and stratification on the interdependency of variables. These methods are discussed in relation to the analysis of a subset of variables from a stratified sample of credit-card applicants. The large number of variables measured in an application form requires the statistical analysis of large sparse contingency tables. It is shown here that tractable graphical models can be extracted from fitting the relatively simple all-two-way interaction model.

AB - Graphical models simplify the analysis of multivariate observations by summarizing conditional independences in the data. Variables are represented by nodes, and the absence of an edge between two nodes signifies their conditional independence. While graphical modelling has been used in several applications of statistics, credit scoring has only recently been suggested as a suitable candidate. This paper suggests the following potential uses for graphical models: to display and interpret the associations between variables taken from a credit-card application form; to compare the credit scoring of subpopulations; to give a description of the credit-scoring selection process in terms of influence diagrams; and to assess the effect of selection bias and stratification on the interdependency of variables. These methods are discussed in relation to the analysis of a subset of variables from a stratified sample of credit-card applicants. The large number of variables measured in an application form requires the statistical analysis of large sparse contingency tables. It is shown here that tractable graphical models can be extracted from fitting the relatively simple all-two-way interaction model.

U2 - 10.1093/imaman/9.3.241

DO - 10.1093/imaman/9.3.241

M3 - Journal article

VL - 9

SP - 241

EP - 266

JO - IMA Journal of Management Mathematics

JF - IMA Journal of Management Mathematics

SN - 1471-678X

IS - 3

ER -