Development of Credit Scoring Model to Determine the Creditworthiness of Borrowers

The explosive growth of data in banking sector is common phenomena. It is due to early adaptation of information system by Banks. This vast volume of historical data related to financial position of individuals and organizations compel banks to evaluate credit worthiness of clients to offers new services. Credit scoring can be defined as a technique that facilitates lenders in deciding to grant or reject credit to consumers. A credit score is a product of advanced analytical models that catch a snapshot of the consumer credit history and translate it into a numeric number that signify the amount of risks that will be generated in a specific deal by the consumer. Automated Credit scoring mechanism has replaced onerous, error-prone labour-intensive manual reviews that were less transparent and lacks statistical-soundness in almost all financial organizations. The credit scoring functionality is a type of classification problem for the new customer. There are numerous data classification algorithm proposed and each one has its pros and cons. This independent study focuses on comparing three data classification algorithms namely: Naïve Bayes, Bayesian Network and Bagging, for credit scoring task. An extensive series of experiments are performed on three standard credit scoring datasets: (i) German credit dataset, (ii) Australian credit dataset and (iii) Pakistan credit dataset. One of the main contributions of this study is to introduced Pakistan credit dataset; it is collected from local credit repository, and transformed accordingly to be used in the study. The studies compare the experimental results of different selected algorithms for classification, their standard evaluation measures, performance on the three datasets, and conclude the major findings.