A Technology on Credit Score System – Assessing Public Perception in Bengaluru City
A. Senthil Kumar1, S. Ramesh2, Rahul S.3

1Dr. A. Senthil Kumar, Assistant Professor, Department of Commerce and Management, St. Joseph’s College (Autonomous), Langford Road, Bengaluru, Karnataka, India.
2Dr. S. Ramesh, Associate Professor, Department of Commerce, Jain (Deemed-to-be) University, Jayanagar 9th Block, Bengaluru, Karnataka, India.
3Mr. Rahul S. is a final year student studying in the Department of Commerce and Management (UG), St. Joseph’s College (Autonomous), Bengaluru, Karnataka, India. 

Manuscript received on September 14, 2019. | Revised Manuscript received on 24 September, 2019. | Manuscript published on October 10, 2019. | PP: 1929-1934 | Volume-8 Issue-12, October 2019. | Retrieval Number: L28901081219/2019©BEIESP | DOI: 10.35940/ijitee.L2890.1081219
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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: A credit score numerical expression based on a level analysis of a person’s credit files, to represent the solvency position of an individual. It is primarily based on a credit report, information usually sourced from credit bureaus. The present study was conducted with the aim of studying the system of credit score, assessing the level of awareness and knowledge of public on credit scoring system. Further it also examined the effect of demographic variables on awareness and knowledge level of public on credit score system. Data was collected through a well-defined questionnaire from 237 respondents in Bengaluru city of Karnataka state. The data was collected during June-July 2019 by employing convenience sampling and snowball sampling methods. Bengaluru city was chosen as the study area because there are lot of different types of individuals been setup over there like businessmen, industrialists, IT employees, salaried individuals etc. In order to achieve the objectives of the study, the statistical tools chi-square analysis and correspondence analysis were used. The statistical software SPSS version 22 was used for data analysis. The study found that that the demographic variables such as gender and number of dependents were significantly associated with level of knowledge on credit scoring system.
Keywords: Credit Score, Contingency Analysis, Correspondence Analysis, Knowledge, Loan.
Scope of the Article: Knowledge Systems and Engineering