Lenders experience positive net revenue impacts from lending if they increase the classification power of their credit scoring systems. If loan officers’ subjective assessments of otherwise intangible borrower characteristics contain additional information about a borrower, a lender may improve the default forecast quality of his internal credit scoring systems by utilizing this subjective info...

Credit scoring is the assessment of the risk associated with a consumer (an organization or an individual) that apply for the credit. Therefore, the problem of credit scoring can be stated as a discrimination between those applicants whom the lender is confident will repay credit and those applicants who are considered by the lender as insufficiently reliable. In this work we propose a novel me...

In the practice of credit-risk management, models for receiver operating characteristic (ROC) curves are helpful in describing shape an ROC curve, estimating discriminatory power a scorecard, and generating without underlying data. The primary purpose this study is to review curve proposed literature, primarily biostatistics, fit them actual credit-scoring data order determine which could be us...

Credit risk scorecards are logistic regression models, fitted to large and complex data sets, employed by the financial industry model probability of default potential customers. In order ensure that a scorecard remains representative population, one tests hypothesis population stability; specifying distribution customers’ attributes constant over time. Simulating realistic sets for this purpos...

Adaboost is a machine learning algorithm that builds a series of small decision trees, adapting each tree to predict difficult cases missed by the previous trees and combining all trees into a single model. We will discuss the AdaBoost methodology and introduce the extension called Real AdaBoost. Real AdaBoost comes from a strong academic pedigree: its authors are pioneers of machine learning a...

In developing a predictive model, the complexity of the population used to build the model can lead to very weak scorecards when a traditional technique such as logistic regression or an MLP neural network is used. For these cases some nontraditional methodologies like boosting could help improve the predictive power of any learning algorithm. The idea behind this technique is to combine severa...

ABSTRAK Penggunaan Balanced Scorecard sangat membantu perusahaan untuk memberikan pandangan mengenai kinerja sebuah perusahaan. Penelitian ini bertujuan mengidentifikasi sifat dan karakteristik risiko yang dihadapi oleh koperasi credit union agar dapat menetukan keputusan apa akan diambil union, penelitian menggunakan metode digunakan adalah dengan menetapkan probabilitas munculnya menilai pelu...