Evaluation of K-NN and Decision Tree Classifiers for Classification of Home Loan Customers Using Data Mining Classification Technique

  • Mrs.Yogita Bhapkar Research Student, Dept. of Computer Science, BVDU, Yashwantrao Mohite College, Pune- 411038
  • Dr. A. D. More MCA Director, Research Guide BVDU, IMED, Pune-411038


Data mining has variety of applications in Banking and finance sectors like fraud detection, marketing, customer relationship management, customer acquisition and retention and credit risk analysis. For any financial organization which provides loan to the customer. To acquire more number of customers and to retain existing customers is very essential for the bank. Credit risk analysis is the core application for Banking sectors.  A large number of approaches and methodologies have been tried out for analysis of customers database requesting for home loan . Bank maintains customers data in their database repositories. Different types of loans are available .This paper highlights study of home loan applications. Traditional method is bank loan officers study the loan applications ,personal details and financial history of the customers and then take decisions of defaulters and non defaulters but in today’s world information is increasing due  to electronic transactions and number of loan applications are also increasing. Hence traditional approach will not work accurately. So we need a strong tool and techniques to store this huge amount of data do some analysis on it and find different patterns for future use. Hence there is need of data mining technique to research in this area. Depending on patterns home loan customers are classifying into mainly ’Risky’ and ‘Safe’. But in this research work we have further classify customers into ‘Safe‘,’More Safe’,’Risk’,’More Risky’ .Data mining techniques plays important role in the process of extracting previously unknown information typically in the form of patterns from large databases. This study mainly focuses on study of home loan applications by using data mining techniques.This is an overview of modeling behavior of bank customers using predictive data mining techniques. An accurate estimation of credit risk analysis could be transformed into a more efficient use of economic capital. Key Words: data mining, banking, credit risk analysis, classification etc.

Author Biography

Mrs.Yogita Bhapkar, Research Student, Dept. of Computer Science, BVDU, Yashwantrao Mohite College, Pune- 411038


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