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What is Credit Risk

Handbook of Research on Organizational Transformations through Big Data Analytics
Possibility of loss from a borrower's default.
Published in Chapter:
Default Probability Prediction of Credit Applicants Using a New Fuzzy KNN Method with Optimal Weights
Abbas Keramati (University of Tehran, Iran), Niloofar Yousefi (University of Central Florida, USA), and Amin Omidvar (Amirkabir University of Technology, Iran)
DOI: 10.4018/978-1-4666-7272-7.ch024
Abstract
Credit scoring has become a very important issue due to the recent growth of the credit industry. As the first objective, this chapter provides an academic database of literature between and proposes a classification scheme to classify the articles. The second objective of this chapter is to suggest the employing of the Optimally Weighted Fuzzy K-Nearest Neighbor (OWFKNN) algorithm for credit scoring. To show the performance of this method, two real world datasets from UCI database are used. In classification task, the empirical results demonstrate that the OWFKNN outperforms the conventional KNN and fuzzy KNN methods and also other methods. In the predictive accuracy of probability of default, the OWFKNN also show the best performance among the other methods. The results in this chapter suggest that the OWFKNN approach is mostly effective in estimating default probabilities and is a promising method to the fields of classification.
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The Effects of Political and Economic Risk on FDI: A Theoretical Survey
Is the risk that whoever you lend money to - will not repay it.
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Corporate Governance and Financial Risk Disclosure: Empirical Evidence in the Portuguese Capital Market
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