Fraud identification and prevention is becoming the decisive challenge for any financial institutions and financial service industries. Majority of the banks use a credit score, a numerical or statistical value to estimate the customer's creditworthiness based on their credit history. A credit score model will be built by employing training dataset and further, an analytical process will be conceded to estimate the credit score of each customer. Thus, for any credit score model, the lenders acquire various data about the customer from various external agencies. The collected data may encompass irrelevant parameters which will not help in making any decisions and also decelerates the global performance of the model. Accordingly, feature selection is an imperative process to eliminate less relevant attributes for any dataset, especially in credit scoring. This paper emphasizes on enlightening the performance of the attribute selection process using multiple rank score model. The proposed method accumulates the results using the optimized threshold algorithm and outperforms well in selecting the quality attribute for the underlying credit score model. The experimental evaluation has been carried out and it is proved that the accuracy and performance of the suggested method are comparatively better than the existing single rank techniques.