The most commonly used techniques for credit scoring is logistic regression, and more recent research has proposed that the support vector machine is a more effective method. However, both logistic regression and support vector machine suffers from curse of dimension. In this paper, we introduce a new way to address this problem which is defined as orthogonal dimension reduction. We discuss the...

A requirement of a credit scoring decision support system for small business loans is that the embedded scoring model can be easily altered in accord with the change of business environment. To satisfy such a requirement, this study proposes an N-tier architecture integrated with the idea of Model-View-Controller. With this design, the system engineers can avoid frequently investing considerabl...

The paper considers a general approach for classifying objects using mathematical programming algorithms. The approach is based on optimizing a utility function, which is quadratic in indicator parameters and is linear in control parameters (which need to be identified). Qualitative characteristics of the utility function, such as monotonicity in some variables, are included using additional co...

Lenders use rating and scoring models to rank credit applicants on their expected performance. The models and approaches are numerous. We explore the possibility that estimates generated by models developed with data drawn solely from extended loans are less valuable than they should be because of selectivity bias. We investigate the value of “reject inference” – methods that use a rejected app...

The goal of this paper is to propose an ensemble classification method for the credit assignment problem. The idea of the proposed method is based on switching class labels techniques. An application of such techniques allows solving two typical data mining problems: a predicament of imbalanced dataset, and an issue of asymmetric cost matrix. The performance of the proposed solution is evaluate...

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 th...

Credit risk assessment for consumers has been a cornerstone of risk management in financial institutions and constitutes a component of the three pillars of Basel II. Traditionally, the concept of 5 ‘C’s was widely adopted by financial institutions as the key basis for credit risk assessment for loan applications by prospective borrowers. With the evolution of the credit risk management practic...

Assume we are given a large collection of objects, each with several hundred attributes, and we wish to assign scores and take appropriate actions for each object in such a way as to maximize a given objective function defined on the entire collection. In this paper, we describe a methodology that uses data mining to divide the objects into different clusters on the basis of their attributes so...

Recognizing and foreseeing which credit clients will be "good or bad payers" is an important and di cult task for bank institutions and credit protection services. Using data from approximately 10,000 clients obtained from a large private Brazilian bank, we present a methodology to perform the credit scoring analysis. The methodology proposed is divided into 2 stages: statistical data analysis ...