IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i15p1820-d606576.html
   My bibliography  Save this article

Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods

Author

Listed:
  • Ekaterina V. Orlova

    (Department of Economics and Management, Ufa State Aviation Technical University, 450000 Ufa, Russia)

Abstract

This research deals with the challenge of reducing banks’ credit risks associated with the insolvency of borrowing individuals. To solve this challenge, we propose a new approach, methodology and models for assessing individual creditworthiness, with additional data about borrowers’ digital footprints to implement comprehensive analysis and prediction of a borrower’s credit profile. We suggest a model for borrowers’ clustering based on the method of hierarchical clustering and the k -means method, which groups actual borrowers having similar creditworthiness and similar credit risks into homogeneous clusters. We also design the model for borrowers’ classification based on the stochastic gradient boosting (SGB) method, which reliably determines the cluster number and therefore the risk level for a new borrower. The developed models are the basis for decision making regarding the decision about lending value, interest rates and lending terms for each risk-homogeneous borrower’s group. The modified version of the methodology for assessing individual creditworthiness is presented, which is to reduce the credit risks and to increase the stability and profitability of financial organizations.

Suggested Citation

  • Ekaterina V. Orlova, 2021. "Methodology and Models for Individuals’ Creditworthiness Management Using Digital Footprint Data and Machine Learning Methods," Mathematics, MDPI, vol. 9(15), pages 1-28, August.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:15:p:1820-:d:606576
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/15/1820/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/15/1820/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fielding, David & Stracca, Livio, 2007. "Myopic loss aversion, disappointment aversion, and the equity premium puzzle," Journal of Economic Behavior & Organization, Elsevier, vol. 64(2), pages 250-268, October.
    2. Natalya Lunyakova & Oleg Lavrushin & Oleg Lunyakov, 2018. "Clustering of the Federal Subjects of the Russian Federation by Deposit Risk Level," Economy of region, Centre for Economic Security, Institute of Economics of Ural Branch of Russian Academy of Sciences, vol. 1(3), pages 1046-1060.
    3. Berger, Allen N. & Sedunov, John, 2017. "Bank liquidity creation and real economic output," Journal of Banking & Finance, Elsevier, vol. 81(C), pages 1-19.
    4. Aebi, Vincent & Sabato, Gabriele & Schmid, Markus, 2012. "Risk management, corporate governance, and bank performance in the financial crisis," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3213-3226.
    5. 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.
    6. Jordan Kjosevski & Mihail Petkovski, 2017. "Non-performing loans in Baltic States: determinants and macroeconomic effects," Baltic Journal of Economics, Baltic International Centre for Economic Policy Studies, vol. 17(1), pages 25-44.
    7. Shlomo Benartzi & Richard H. Thaler, 1995. "Myopic Loss Aversion and the Equity Premium Puzzle," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 110(1), pages 73-92.
    8. Grigori Fainstein & Igor Novikov, 2011. "The Comparative Analysis of Credit Risk Determinants In the Banking Sector of the Baltic States," Review of Economics & Finance, Better Advances Press, Canada, vol. 1, pages 20-45, June.
    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. Mehra, Rajnish & Prescott, Edward C., 1985. "The equity premium: A puzzle," Journal of Monetary Economics, Elsevier, vol. 15(2), pages 145-161, March.
    11. Khandani, Amir E. & Kim, Adlar J. & Lo, Andrew W., 2010. "Consumer credit-risk models via machine-learning algorithms," Journal of Banking & Finance, Elsevier, vol. 34(11), pages 2767-2787, November.
    12. Pestova Anna & Mamonov Mikhail, 2013. "Macroeconomic and bank?specific determinants of credit risk: evidence from Russia," EERC Working Paper Series 13/10e, EERC Research Network, Russia and CIS.
    13. Mariia Dedova, 2018. "A Comparison of Time-series Bootstrap Methods in Terms of Backtesting Risk Measurement Models of Banks," HSE Economic Journal, National Research University Higher School of Economics, vol. 22(1), pages 84-109.
    14. Giuseppe Orlando & Roberta Pelosi, 2020. "Non-Performing Loans for Italian Companies: When Time Matters. An Empirical Research on Estimating Probability to Default and Loss Given Default," IJFS, MDPI, vol. 8(4), pages 1-22, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tang, Xinyin & Feng, Chong & Zhu, Jianping & He, Minna, 2022. "How Can We Learn from Borrowers’ Online Behaviors? The Signal Effect of Borrowers’ Platform Involvement on Their Credit Risk," SocArXiv qga8j, Center for Open Science.
    2. Ekaterina V. Orlova, 2022. "Design Technology and AI-Based Decision Making Model for Digital Twin Engineering," Future Internet, MDPI, vol. 14(9), pages 1-14, August.
    3. Ekaterina V. Orlova, 2023. "Inference of Factors for Labor Productivity Growth Used Randomized Experiment and Statistical Causality," Mathematics, MDPI, vol. 11(4), pages 1-22, February.
    4. Ekaterina V. Orlova, 2022. "Methodology and Statistical Modeling of Social Capital Influence on Employees’ Individual Innovativeness in a Company," Mathematics, MDPI, vol. 10(11), pages 1-22, May.

    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. Azmat, Saad & Jalil, Muhammad Naiman & Skully, Michael & Brown, Kym, 2016. "Investor’s choice of Shariah compliant ‘replicas’ and original Islamic instruments," Journal of Economic Behavior & Organization, Elsevier, vol. 132(S), pages 4-22.
    2. Roelof Salomons, 2008. "A Theoretical And Practical Perspective On The Equity Risk Premium," Journal of Economic Surveys, Wiley Blackwell, vol. 22(2), pages 299-329, April.
    3. Pavlo Blavatskyy & Ganna Pogrebna, 2010. "Reevaluating evidence on myopic loss aversion: aggregate patterns versus individual choices," Theory and Decision, Springer, vol. 68(1), pages 159-171, February.
    4. Kim, Sei-Wan & Krausz, Joshua & Nam, Kiseok, 2013. "Revisiting asset pricing under habit formation in an overlapping-generations economy," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 132-138.
    5. Siddiqi, Hammad, 2015. "Anchoring and Adjustment Heuristic: A Unified Explanation for Equity Puzzles," MPRA Paper 68729, University Library of Munich, Germany.
    6. Lovric, M. & Kaymak, U. & Spronk, J., 2008. "A Conceptual Model of Investor Behavior," ERIM Report Series Research in Management ERS-2008-030-F&A, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.
    7. Uri Gneezy & Jan Potters, 1997. "An Experiment on Risk Taking and Evaluation Periods," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 112(2), pages 631-645.
    8. Stefano DellaVigna, 2009. "Psychology and Economics: Evidence from the Field," Journal of Economic Literature, American Economic Association, vol. 47(2), pages 315-372, June.
    9. Iñigo Iturbe-Ormaetxe & Giovanni Ponti & Josefa Tomás, 2016. "Myopic Loss Aversion under Ambiguity and Gender Effects," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-11, December.
    10. Zeisberger, Stefan & Langer, Thomas & Trede, Mark, 2007. "A note on myopic loss aversion and the equity premium puzzle," Finance Research Letters, Elsevier, vol. 4(2), pages 127-136, June.
    11. Eriksen, Kristoffer W. & Kvaløy, Ola, 2014. "Myopic risk-taking in tournaments," Journal of Economic Behavior & Organization, Elsevier, vol. 97(C), pages 37-46.
    12. Enrico G. De Giorgi & Thierry Post, 2011. "Loss Aversion with a State-Dependent Reference Point," Management Science, INFORMS, vol. 57(6), pages 1094-1110, June.
    13. Jakusch, Sven Thorsten, 2017. "On the applicability of maximum likelihood methods: From experimental to financial data," SAFE Working Paper Series 148, Leibniz Institute for Financial Research SAFE, revised 2017.
    14. Prat, Georges, 2013. "Equity risk premium and time horizon: What do the U.S. secular data say?," Economic Modelling, Elsevier, vol. 34(C), pages 76-88.
    15. Abootaleb Shirvani & Stoyan V. Stoyanov & Frank J. Fabozzi & Svetlozar T. Rachev, 2019. "Equity Premium Puzzle or Faulty Economic Modelling?," Papers 1909.13019, arXiv.org, revised Jan 2020.
    16. Rieger, Marc Oliver & Wang, Mei, 2012. "Can ambiguity aversion solve the equity premium puzzle? Survey evidence from international data," Finance Research Letters, Elsevier, vol. 9(2), pages 63-72.
    17. Uri Gneezy & Arie Kapteyn & Jan Potters, 2003. "Evaluation Periods and Asset Prices in a Market Experiment," Journal of Finance, American Finance Association, vol. 58(2), pages 821-837, April.
    18. Marc Oliver Rieger & Thorsten Hens & Mei Wang, 2013. "International Evidence on the Equity Premium Puzzle and Time Discounting," Multinational Finance Journal, Multinational Finance Journal, vol. 17(3-4), pages 149-163, September.
    19. Jonathan Chapman & Erik Snowberg & Stephanie Wang & Colin Camerer, 2018. "Loss Attitudes in the U.S. Population: Evidence from Dynamically Optimized Sequential Experimentation (DOSE)," NBER Working Papers 25072, National Bureau of Economic Research, Inc.
    20. Alexander Ludwig & Alexander Zimper, 2013. "A decision-theoretic model of asset-price underreaction and overreaction to dividend news," Annals of Finance, Springer, vol. 9(4), pages 625-665, November.

    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:gam:jmathe:v:9:y:2021:i:15:p:1820-:d:606576. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.