IDEAS home Printed from https://ideas.repec.org/a/ers/ijfirm/v5y2015i1p851.html
   My bibliography  Save this article

Loan Products and Credit Scoring by Commercial Banks (India)

Author

Listed:
  • Rais Ahmad Itoo
  • A. Selvarasu
  • José António Filipe

Abstract

This study describes the loan products offered by commercial banks and credit scoring techniques used for classifying risks and granting credit to applicants in India. Loan products offered by commercial banks are from several kinds, since housing loans, personal loans, business loans until education loans or vehicle loans, among many other types. All loan products are categorized either as secured or unsecured loans. Credit scoring techniques used for both secured and unsecured loans are broadly divided into two categories: Advanced Statistical Methods and Traditional Statistical Methods. Some methodologies are presented to discuss Indian situation and understand the different kind on retail loans offered by banks and the different credit scoring methods for personal finance used by commercial banks in India.

Suggested Citation

  • Rais Ahmad Itoo & A. Selvarasu & José António Filipe, 2015. "Loan Products and Credit Scoring by Commercial Banks (India)," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 5(1), pages 851-851.
    • Handle: RePEc:ers:ijfirm:v:5:y:2015:i:1:p:851
    as

    Download full text from publisher

    File URL: https://journalfirm.com/journal/115/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anderson, Raymond, 2007. "The Credit Scoring Toolkit: Theory and Practice for Retail Credit Risk Management and Decision Automation," OUP Catalogue, Oxford University Press, number 9780199226405.
    2. McKee, Thomas E. & Lensberg, Terje, 2002. "Genetic programming and rough sets: A hybrid approach to bankruptcy classification," European Journal of Operational Research, Elsevier, vol. 138(2), pages 436-451, April.
    3. Azam, Rehan & Muhammad, Danish & Syed Akbar, Suleman, 2012. "The significance of socioeconomic factors on personal loan decision a study of consumer banking local private banks in Pakistan," MPRA Paper 42322, University Library of Munich, Germany.
    4. B Baesens & T Van Gestel & S Viaene & M Stepanova & J Suykens & J Vanthienen, 2003. "Benchmarking state-of-the-art classification algorithms for credit scoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 54(6), pages 627-635, June.
    5. D. J. Hand & W. E. Henley, 1997. "Statistical Classification Methods in Consumer Credit Scoring: a Review," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 160(3), pages 523-541, September.
    6. Paleologo, Giuseppe & Elisseeff, André & Antonini, Gianluca, 2010. "Subagging for credit scoring models," European Journal of Operational Research, Elsevier, vol. 201(2), pages 490-499, March.
    7. Eric Rosenberg & Alan Gleit, 1994. "Quantitative Methods in Credit Management: A Survey," Operations Research, INFORMS, vol. 42(4), pages 589-613, August.
    8. Thomas, Lyn C., 2000. "A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers," International Journal of Forecasting, Elsevier, vol. 16(2), pages 149-172.
    9. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    10. Lensberg, Terje & Eilifsen, Aasmund & McKee, Thomas E., 2006. "Bankruptcy theory development and classification via genetic programming," European Journal of Operational Research, Elsevier, vol. 169(2), pages 677-697, March.
    11. Blochlinger, Andreas & Leippold, Markus, 2006. "Economic benefit of powerful credit scoring," Journal of Banking & Finance, Elsevier, vol. 30(3), pages 851-873, March.
    12. Malhotra, Rashmi & Malhotra, D. K., 2003. "Evaluating consumer loans using neural networks," Omega, Elsevier, vol. 31(2), pages 83-96, April.
    13. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    14. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    15. T Bellotti & J Crook, 2009. "Credit scoring with macroeconomic variables using survival analysis," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 60(12), pages 1699-1707, December.
    16. David Durand, 1941. "Risk Elements in Consumer Instalment Financing, Technical Edition," NBER Books, National Bureau of Economic Research, Inc, number dura41-2, May.
    17. Hussein A. Abdou & John Pointon, 2009. "Credit scoring and decision making in Egyptian public sector banks," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 5(4), pages 391-406, September.
    18. Seow, Hsin-Vonn & Thomas, Lyn C., 2006. "Using adaptive learning in credit scoring to estimate take-up probability distribution," European Journal of Operational Research, Elsevier, vol. 173(3), pages 880-892, September.
    19. Jerzy Stefanowski & Szymon Wilk, 2001. "Evaluating business credit risk by means of approach‐integrating decision rules and case‐based learning," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 10(2), pages 97-114, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hussein A. Abdou & John Pointon, 2011. "Credit Scoring, Statistical Techniques And Evaluation Criteria: A Review Of The Literature," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 18(2-3), pages 59-88, April.
    2. Rais Ahmad Itoo & A. Selvarasu, 2017. "Loan products and Credit Scoring Methods by Commercial Banks," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 7(1), pages 1297-1297.
    3. Elena Ivona DUMITRESCU & Sullivan HUE & Christophe HURLIN & Sessi TOKPAVI, 2020. "Machine Learning or Econometrics for Credit Scoring: Let’s Get the Best of Both Worlds," LEO Working Papers / DR LEO 2839, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.
    4. Dinh, Thi Huyen Thanh & Kleimeier, Stefanie, 2007. "A credit scoring model for Vietnam's retail banking market," International Review of Financial Analysis, Elsevier, vol. 16(5), pages 471-495.
    5. Akkoç, Soner, 2012. "An empirical comparison of conventional techniques, neural networks and the three stage hybrid Adaptive Neuro Fuzzy Inference System (ANFIS) model for credit scoring analysis: The case of Turkish cred," European Journal of Operational Research, Elsevier, vol. 222(1), pages 168-178.
    6. Neuberg Richard & Hannah Lauren, 2017. "Loan pricing under estimation risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 69-87, June.
    7. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    8. Doruk Şen & Cem Çağrı Dönmez & Umman Mahir Yıldırım, 2020. "A Hybrid Bi-level Metaheuristic for Credit Scoring," Information Systems Frontiers, Springer, vol. 22(5), pages 1009-1019, October.
    9. Crook, Jonathan N. & Edelman, David B. & Thomas, Lyn C., 2007. "Recent developments in consumer credit risk assessment," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1447-1465, December.
    10. Andreea Costea, 2017. "A Quantitative Approach to Credit Risk Management in the Underwriting Process for the Retail Portfolio," Romanian Economic Journal, Department of International Business and Economics from the Academy of Economic Studies Bucharest, vol. 20(63), pages 157-186, March.
    11. Maria Rocha Sousa & João Gama & Elísio Brandão, 2013. "Introducing time-changing economics into credit scoring," FEP Working Papers 513, Universidade do Porto, Faculdade de Economia do Porto.
    12. Yu Zhao & Huaming Du & Qing Li & Fuzhen Zhuang & Ji Liu & Gang Kou, 2022. "A Comprehensive Survey on Enterprise Financial Risk Analysis from Big Data Perspective," Papers 2211.14997, arXiv.org, revised May 2023.
    13. Doruk Şen & Cem Çağrı Dönmez & Umman Mahir Yıldırım, 0. "A Hybrid Bi-level Metaheuristic for Credit Scoring," Information Systems Frontiers, Springer, vol. 0, pages 1-11.
    14. Ha Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," Working Papers hal-04133309, HAL.
    15. K.K. Jain & P.K. Gupta & Sanjiv Mittal, 2011. "Logistic Predictive Model for SMEs Financing in India," Vision, , vol. 15(4), pages 331-346, December.
    16. Dinh, K. & Kleimeier, S., 2006. "Credit scoring for Vietnam's retail banking market : implementation and implications for transactional versus relationship lending," Research Memorandum 012, Maastricht University, Maastricht Research School of Economics of Technology and Organization (METEOR).
    17. Thomas Wainwright, 2011. "Elite Knowledges: Framing Risk and the Geographies of Credit," Environment and Planning A, , vol. 43(3), pages 650-665, March.
    18. Richard Chamboko & Jorge M. Bravo, 2016. "On the modelling of prognosis from delinquency to normal performance on retail consumer loans," Risk Management, Palgrave Macmillan, vol. 18(4), pages 264-287, December.
    19. Crone, Sven F. & Finlay, Steven, 2012. "Instance sampling in credit scoring: An empirical study of sample size and balancing," International Journal of Forecasting, Elsevier, vol. 28(1), pages 224-238.
    20. Ha-Thu Nguyen, 2015. "How is credit scoring used to predict default in China?," EconomiX Working Papers 2015-1, University of Paris Nanterre, EconomiX.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ers:ijfirm:v:5:y:2015:i:1:p:851. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marios Agiomavritis (email available below). General contact details of provider: https://journalfirm.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.