A Predictive Analysis of Credit Risk Evaluation and the Quality Decision Making using Different Predictive Models
U Bhuvaneswari1, Sharon Sophia2
1U Bhuvaneswari, VITBS, VIT University, Chennai, India.
2Dr Sharon Sophia, VITBS, VIT University, Chennai, India.
Manuscript received on 05 April 2019 | Revised Manuscript received on 10 May 2019 | Manuscript published on 30 May 2019 | PP: 1965-1969 | Volume-8 Issue-1, May 2019 | Retrieval Number: A3316058119/19©BEIESP
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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: In the fast growing economy, the role of credit plays a very significant role. Most of the financial institutions offer credit to their customers who are in need to meet their personal requirements and as to pay it back within specific period. Any institution which provides the credit service will predict the credit risk towards an individual which highlights the capability of the person to pay back the amount along with their previous available pay back records. Many predictive models were developed to predict the credit risk with many different variables. In the present work, the different credit risk predictive models were evaluated and compared based on the quality of decision making. The primary data were collected from 151 respondents through various online sources with the structured questionnaire and the secondary data from the previous records. The metrics derived from the predictions reveal high accuracy and precision. From the analysis, the prediction accuracy and time for the linear SVM technique was better than all other methods..
Index Terms: Credit Risk; Predictive Model; Quality Decision; Accuracy; Linear Svm
Scope of the Article: Predictive Analysis