The financial organizations such as banks assess credit worthiness of borrowers before providing new loans. Certain assessments are done on the basis of ‘probability of default’ for the potential borrower which is based on credit scoring of customer. Banks are having customer`s portfolios that are likely to go through current crisis without much difficulty, but at the same time it is difficult to determine the risks arising due to various factors. Most of the features in customer databases have little predictive effect on the credit worthiness of the customer. So, determining features that affects the credit worthiness is another important task before knowledge discovery. The predictive models, based on Ensemble Methods (EM): a ML paradigm under Data Mining (DM), have the capability to determine the relevant features and customer’s credit worthiness in efficient manner. In this paper, a good number of prediction models proposed in the literature are surveyed. To deal with major challenges like data incompleteness, noise and its vastness while building predictive models, this work proposes a predictive framework (classifier) for detection of Credit Worthiness. It employs a Supervised Attribute (Feature) selection method to select the worth of subset, filters and an Enhanced Ensemble Method based on Boosting for precise convergence. The proposed Enhanced Ensemble based classifier is implemented in Weka 3.8.2. Later in this work, to know the performance of proposed model, evaluation and comparison against several model is presented. The proposed framework selects minimum number of attributes and is good in terms of False Positive, Recall, True Positive and Prediction Rate than original base classifiers. Also, the model is good for applying on large datasets due the benefit of ensembles.

Artificial Intelligence, Boosting, Classification, Credit Risk, Credit Worthiness, Ensemble Methods, Machine Learning, Predictive Models, Weka

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