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Binary Logistic Regression Analysis: The Indicators Underlying the Granting of a High Value Personal Loan

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  • Constantinescu Liliana Aurora
  • Mihai Carmina Elena

Abstract

All clients requesting loan for personal needs in credit institutions are included in certain groups with different default risk. In this respect, banks use a number of quantitative and qualitative indicators to categorize credit applicants in risk categories based on prudence, credibility and solvency. However, many institutions do not have a clear picture of the consumer's importance to these indicators, especially in terms of access to high value credits. Therefore, we present the results of a study aimed at identifying consumer opinions on the influence of the indicators underlying the granting of a high value personal credit, which was performed (n = 102) among the population in Brasov, over 18, in June 2019. The study results indicate the type of income, the amount of the customer's and beneficiary's income, the location, the length of residence, the length of service, the age and the field in which they operate are factors taken into account by banks and non-personal needs of high value.

Suggested Citation

  • Constantinescu Liliana Aurora & Mihai Carmina Elena, 2019. "Binary Logistic Regression Analysis: The Indicators Underlying the Granting of a High Value Personal Loan," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 5(2), pages 193-200, June.
    • Handle: RePEc:khe:scajes:v:5:y:2019:i:2:p:193-200
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    References listed on IDEAS

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    1. Mirela Catalina Turkes & Alexandru Olariu & Mirela Matache, 2017. "Patient Satisfaction Perceived in the Emergency Department: A Quantitative Study in a State Hospital in Romania," Academic Journal of Economic Studies, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 3(3), pages 112-117, September.
    2. Irina Raicu & Mirela Catalina (Vint) Turkes, 2014. "Organizational Performance Improvement by Implementing the Latest CRM Solutions," Knowledge Horizons - Economics, Faculty of Finance, Banking and Accountancy Bucharest,"Dimitrie Cantemir" Christian University Bucharest, vol. 6(1), pages 84-86, March.
    3. Liliana-Aurora CONSTANTINESCU & Camelia STEFANESCU, 2010. "Risk Management In Credit Institutions - New Trends," Review of General Management, Spiru Haret University, Faculty of Management Brasov, vol. 12(2), pages 51-58, October.
    4. Liliana-Aurora CONSTANTINESCU & Adrian CONSTANTINESCU, 2011. "Risk Management Strategies And Solutions For 21 Century Europe," Review of General Management, Spiru Haret University, Faculty of Management Brasov, vol. 14(2), pages 151-158, November.
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    More about this item

    Keywords

    Bank customers; loan; institution; customer income type; Amount of customer and co-payee revenue; customer's location; residence duration at the same address of the customer; customer seniority; information included in the Credit Bureau (BC) databases;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • M31 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Marketing

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