Abstract
Credit scoring plays a vital role in assessing the creditworthiness of loan applicants thus speeding up the approval process. Credit score models however rely on the accuracy of classification models for their performance. This accuracy performance depends not only on the choice of data mining process; it is heavily influenced by the quality of data as well. Although no techniques can be favored over the other, it has been evidenced that logistic regression has been widely employed as an industrial technique for its comprehensive simplicity. This study proposes a SEMMA-based credit scoring model developed with an improved Logistic Regression (LR) model. Improvements are by exclusion of irrelevant features and adjusting the partition ratios. The model has been compared with the predominant models and proved to contain outstanding results with minimal credit decision errors.
Published Version
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