Data representativeness problem in credit scoring

Credit Scoring and Data Mining

Credit scoring is the use of predictive modelling techniques to support decision making in lending. It is a field of immense practical value that also supports a modest amount of academic research. Interestingly, the academic research tends not to be put into practice. This is not a result of insularity and arrogance on the part of the practitioners, but rather, of the practitioners having a be...

Credit scoring - Case study in data analytics

Using DEA for Classification in Credit Scoring

Credit scoring is a kind of binary classification problem that contains important information for manager to make a decision in particularly in banking authorities. Obtained scores provide a practical credit decision for a loan officer to classify clients to reject or accept for payment loan. For this sake, in this paper a data envelopment analysis- discriminant analysis (DEA-DA) approach is us...

Credit scoring in banks and financial institutions via data mining techniques: A literature review

This paper presents a comprehensive review of the works done, during the 2000–2012, in the application of data mining techniques in Credit scoring. Yet there isn’t any literature in the field of data mining applications in credit scoring. Using a novel research approach, this paper investigates academic and systematic literature review and includes all of the journals in the Science direct onli...

Optimization driven data mining and credit scoring

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...