The Development of Credit Channels in the transition to the Digital Economy: Demand Modelling

The article substantiates and formalizes, in analytical form, the probabilistic model of demand for alternative lending channels, taking into account the common and distinctive characteristics of traditional and new ways to take a credit. To develop this model, the advantages and disadvantages of lending channels have emphasized. The possible exclusive scenarios of the credit market development in conditions of digitalization of the economy have been identifed. Taking into account the trends and scenarios for the development of credit channels, a descriptive model of the institutional structure of the demand and supply of credit has been proposed. It is supposed that traditional lending institutions will be able to adapt the business to innovative technologies, offering customers fundamentally new business models, which will perfectly correspond to the sphere of FinTech. According to the descriptive model, the authors proposed to estimate the market share of lending channels based on the application of utility theory and discrete choice models. It is assumed that potential borrowers make a choice of one / another lending channel from available alternatives, maximizing the utility, under the influence of personal and consumer characteristics of the loan. The authors formalized a multidimensional logit model (nested logit model — NLM) for describing the discrete choice of an alternative lending channel and the corresponding subgroups of lenders (traditional, FinTech and BigTech companies). In this case, the distinctive feature of NLM is a possibility of taking into account the correlations in borrowers’ preferences. The conditions for the application of the developed model have determined. Due to the lack of relevant statistical data as to the volume of lending by the digital channels, the authors modelled changes in the market share of the traditional lending channel based on hypothetical data (characteristics of credit). In the process of modelling, the authors showed nonlinear changes in the demand for an alternative lending channel owing to the existence of individual preferences of potential borrowers. The proposed approach can be used to model and forecast the changes in the credit market conditions

1. Шваб К. Четвертая промышленная революция. Пер. с англ. М.: Эксмо; 2016. 208 с. Schwab K. The fourth industrial revolution. Transl. from Engl. Moscow: Eksmo; 2016. 208 p. (In Russ.).

2. Jagtiani J., Lemieux C. Fintech lending: Financial inclusion, risk pricing, and alternative information. SSRN Electronic Journal. 2017;(Jan.). DOI: 10.2139/ssrn.3096098

3. Mills K. G., McCarthy B. The state of small business lending: Credit access during the recovery and how technology may change the game. Harvard Business School. Working Paper. 2014;(15–004). URL: https://www.hbs.edu/faculty/Publication%20Files/15–004_09b1bf8b­eb2a­4e63–9c4e­0374f770856f.pdf (accessed 04.09.2018).

4. Schweitzer M., Barkley B. Is “Fintech” good for small business borrowers? Impacts on frm growth and customer satisfaction. Federal Reserve Bank of Cleveland. Working Paper. 2017;(17–01). URL: fle:///C:/Users/User/Downloads/wp%201701%20Is%20Fintech%20Good%20for%20Small%20Business%20Borrowers.pdf (accessed 04.09.2018).

5. Freedman S., Jin G.Z. Learning by doing with asymmetric information: Evidence from Prosper.com. NBER Working Paper. 2011;(16855). URL: https://www.nber.org/papers/w16855 (accessed 16.12.2017).

6. Jagtiani J., Lemieux C. Small business lending after the fnancial crisis: A new competitive landscape for community banks. Economic Perspectives. 2016;40(3). URL: fle:///C:/Users/User/Downloads/ep2016–3­pdf. (accessed 04.09.2018).

7. Moldow C. A trillion dollar market by the people, for the people: How marketplace lending will remake banking as we know it. Menlo Park, CA: Foundation Capital; 2015. 35 p. URL: https://foundationcapital.com/wp­content/uploads/2016/08/TDMFinTech_whitepaper.pdf (accessed 04.09.2018).

8. Mach T., Carter C., Slattery C. Peer­to­peer lending to small businesses. The Federal Reserve Board. FEDS Working Papers. 2014;(10). URL: https://www.federalreserve.gov/pubs/feds/2014/201410/201410pap.pdf (accessed 04.09.2018).

9. De Roure C., Pelizzon L., Tasca P. How does P2P lending ft into the consumer credit market? Deutsche Bundesbank. Discussion Paper. 2016;(30). URL: https://www.bundesbank.de/Redaktion/EN/Downloads/Publications/Discussion_Paper_1/2016/2016_08_12_dkp_30.pdf%3F__blob%3DpublicationFile (accessed 04.09.2018).

10. Buchak G., Matvos G., Piskorski T., Seru A. Fintech, regulatory arbitrage, and the rise of shadow banks. NBER Working Paper. 2017;(23288). URL: http://www.nber.org/papers/w23288.pdf (accessed 24.12.2017). DOI: 10.3386/w23288

11. Emekter R., Tu Y., Jirasakuldech B., Lu M. Evaluating credit risk and loan performance in Online Peer­toPeer (P2P) lending. Applied Economics. 2015;47(1):54–70. DOI: 10.1080/00036846.2014.962222

12. Berry S., Levinsohn J., Pakes A. Automobile prices in market equilibrium. Econometrica. 1995;63(4):841–890.

13. Wong T., Brownstone D., Bunch D. Aggregation biases in discrete choice models. Journal of Choice Modelling. 2018;(Feb.). DOI: 10.1016/j.jocm.2018.02.001

14. DeYoung R., Rice T. How do banks make money? The fallacies of fee income. Economic Perspectives. 2004;28(4):34–51. URL: fle:///C:/Users/User/Downloads/ep­4qtr2004­part3­deyoung­rice­pdf.pdf

15. Мамонов М. Влияние кризиса на прибыльность российского банковского сектора. Банковское дело. 2011;(12):15–27. Mamonov M. Impact of the crisis on the profitability of the Russian banking sector. Bankovskoe delo = Banking. 2011;(12):15–27. (In Russ.).

16. Котлер Ф., Вонг В., Сондерс Дж., Армстронг Г. Основы маркетинга. Пер. с англ. М.: Вильямс; 2007. 1199 с. Kotler Ph., Wong V., Saunders J., Armstrong G. Principles of marketing. Transl. from Engl. Moscow: Williams; 2007. 1199 p. (In Russ.).

17. Шандор З. Мультиномиальные модели дискретного выбора. Квантиль. 2009;(7):9–20. Sandor Z. Multinomial models of discrete choice. Kvantil’ = Quantile. 2009;(7):9–20. (In Russ.).

18. McFadden D. Modeling the choice of residential location. Transportation Research Record. 1978;(673). URL: http://onlinepubs.trb.org/Onlinepubs/trr/1978/673/673–012.pdf (accessed 20.12.2017).

19. McFadden D., Train K. Mixed MNL models for discrete response. Journal of Applied Econometrics. 2000;15(5):447–470. DOI: 10.1002/1099–1255(200009/10)15:5<447:AID­JAE 570>3.0.CO;2–1

20. Hausman J., Wise D. A conditional probit model for qualitative choice: Discrete decisions recognizing interdependence and heterogeneous preferences. Econometrica. 1978;46(2):403–426. DOI: 10.2307/1913909

21. Berry S. Estimating discrete­choice models of product differentiation. The RAND Journal of Economics. 1994;25(2):242–262. DOI: 10.2307/2555829.