Exploring the Machine Learning Algorithm for Prediction the Loan Sanctioning Process
E. Chandra Blessie1, R. Rekha2
1Dr.E.ChandraBlessie, Department of MCA, Nehru College of Management, Coimbatore, Tamil Nadu, India
2R.Rekha*, Department of MCA, Nehru College of Management, Coimbatore, Tamil Nadu, India.
Manuscript received on October 14, 2019. | Revised Manuscript received on 24 October, 2019. | Manuscript published on November 10, 2019. | PP: 2714-2719 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4881119119/2019©BEIESP | DOI: 10.35940/ijitee.A4881.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Extending credits to corporates and individuals for the smooth functioning of growing economies like India is inevitable. As increasing number of customers apply for loans in the banks and non- banking financial companies (NBFC), it is really challenging for banks and NBFCs with limited capital to device a standard resolution and safe procedure to lend money to its borrowers for their financial needs. In addition, in recent times NBFC inventories have suffered a significant downfall in terms of the stock price. It has contributed to a contagion that has also spread to other financial stocks, adversely affecting the benchmark in recent times. In this paper, an attempt is made to condense the risk involved in selecting the suitable person who could repay the loan on time thereby keeping the bank’s non-performing assets (NPA) on the hold. This is achieved by feeding the past records of the customer who acquired loans from the bank into a trained machine learning model which could yield an accurate result. The prime focus of the paper is to determine whether or not it will be safe to allocate the loan to a particular person. This paper has the following sections (i) Collection of Data, (ii) Data Cleaning and (iii) Performance Evaluation. Experimental tests found that the Naïve Bayes model has better performance than other models in terms of loan forecasting.
Keywords: Loan Prediction, Big data, Machine Learning, Logistic Regression, SVM, Decision Tree, Naïve Bayes.
Scope of the Article: Machine Learning,