FinTech and the COVID-19 Pandemic: Evidence from Electronic Payment Systems

Tut, Daniel (2020): FinTech and the COVID-19 Pandemic: Evidence from Electronic Payment Systems.

Abstract

ArticlePDF Available FinTech and the COVID-19 Pandemic: Evidence from Electronic Payment Systems: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3660987 January 2020SSRN Electronic Journal Authors: Daniel Tut York University

Download full-text PDF Read full-text Download full-text PDF Read full-text Download citation Copy link References (30) Figures (2)

Abstract and Figures This paper investigates the effects of the Covid-19 pandemic on financial institutions and consumers’ adoption of FinTech in payments. We find [1] The covid-19 pandemic has accelerated the adoption of FinTech platforms in payments. We document an approximate increase of 54% in mobile banking transactions, an increase of 19.56% in Mobile agents and an increase of 14.56% in Mobile banking accounts [2] The use of all electronic payment cards has significantly declined during the pandemic. [3] The pandemic has magnified interbank contagion and liquidity risks and has reduced both domestic and international electronic fund transfers via both the Real-Gross Settlement system (RTGS) and via Clearing house (ACH).Overall, our results shows that FinTech not only alleviated the negative impact of theCovid-19 pandemic during Q1 but that the pandemic has also accelerated consumers’ adoption of FinTech platforms and digital onboarding specially in Q3 and Q4.

• Item Type: MPRA Paper
• Language: English
• Keywords: Covid-19, Coronavirus, Fintech, Mobile Payment, Central Banks, Financial Intermediaries, Financial Technologies, Banks, Interbank transfers, Diaspora Remittances, Settlement and Liquidity risks, clearing houses, financial stability, Pandemic, M-PESA, Digital Banking.
• Subjects: E - Macroeconomics and Monetary Economics > E3 - Prices, Business Fluctuations, and Cycles > E32 - Business Fluctuations ; Cycles; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E52 - Monetary Policy; E - Macroeconomics and Monetary Economics > E5 - Monetary Policy, Central Banking, and the Supply of Money and Credit > E58 - Central Banks and Their Policies; G - Financial Economics > G2 - Financial Institutions and Services > G21 - Banks ; Depository Institutions ; Micro Finance Institutions ; Mortgages; G - Financial Economics > G2 - Financial Institutions and Services > G28 - Government Policy and Regulation; G - Financial Economics > G3 - Corporate Finance and Governance > G32 - Financing Policy ; Financial Risk and Risk Management ; Capital and Ownership Structure ; Value of Firms ; Goodwill; O - Economic Development, Innovation, Technological Change, and Growth > O1 - Economic Development > O16 - Financial Markets ; Saving and Capital Investment ; Corporate Finance and Governance; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O31 - Innovation and Invention: Processes and Incentives; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O32 - Management of Technological Innovation and R&D; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O33 - Technological Change: Choices and Consequences ; Diffusion Processes; O - Economic Development, Innovation, Technological Change, and Growth > O3 - Innovation ; Research and Development ; Technological Change ; Intellectual Property Rights > O38 - Government Policy; O - Economic Development, Innovation, Technological Change, and Growth > O5 - Economywide Country Studies > O55 - Africa

References

Aditya, A., S. Aron-Dine, W. Dunn, L. Feiverson, P. Lengermann and C. Sahm (2019). From transaction data to economic statistics: constructing real-time, high-frequency, geographical measures of consumers spending. Finance and Economics Discussion Series 2019-057. Board of Governors of the Federal Reserve..

Aker, J., and I. Mbiti (2010). Mobile Phones and Economic Development in Africa, Journal of Economic Perspectives, 24(3), 207-232

Allen, F. and D. Gale (2000). Financial Contagion. Journal of Political Economy, 108(1), 1-33

Aprigliano, A., Ardizzi G., and L. Monterforte (2019). Using payment system data to forecast the Italian GDP. International Journal of Central Banking. v. 15, p. 55-80.

Bae, K., Karolyi G. and R.M Stulz (2003). A new approach to measuring financial contagion. The Review of Financial Studies, 16, 717-76

Baker, Scott (2018). Debt and the response to household income shocks: validation and application of linked financial account data. Journal of Political Economy, 126(4): 1504-1557

Bhagwati, J., (1998).The Capital Myth,Foreign Affairs, 77(3), 7--12.

Bech, M.L, Shimizu, Y. and P. Wong (2017). The quest for speed in payments, BIS Quaterly Review

Borio, C. and P Van den Bergh (1993). The nature and management of payment system risk: an international perspective, BIS Economic Papers, no. 36.

Cabrales, A., D. Gale and P. Gattardi (2016). Financial contagion in networks. Oxford handbook of the Economics of Networks, edited by Y. Bramoulle, A. Galeotti and Brian Rogers, 543-68. Oxford and New York: Oxford University Press.

Carlsen, M. and Storgaard, P.E. (2010). Dankort payments as a timely indicator of retail sales in Denmark. Technical report

Central Bank of Kenya (2020) Implementation of the emergency measures to mitigate the adverse impact of the Corona virus (Covid-19) pandemic on loans and advances- Banking Circular No. 3 of 2020.

Central Bank of Ireland (2020), The potential economic impact of Covid-19 pandemic based on baseline and severe scenarios. Quarterly Bulletin No. 3/QB3- July 2020

Central Bank of Kenya(2020). Emergency measures to facilitate mobile money transactions. Press Release, March 2020.

Central Bank of Kenya(2020). Key monetary and financial indicators- CBK, weekly Bulletin, 2020. Central Bank of Kenya(2020). Monetary policy committee meeting, CBK, March-23-2020.

Cytonn Report (2020) Cytonn note on the monetary policy committee (MPC) meeting- July, 2020.

Crockett, A. (1998), Managing change in payment systems, BIS policy papers no.4.. item Crowe, M. Tabila E. and B. Mcguire (2017) Mobile banking and payment practices of US financial institutions: 2016 Mobile financial Services survey results in Seven Federal Reserve Districts. Federal Reserve Bulletin of Boston, Decmeber, p.14

Degryse, H. and G. Nguyenn (2007). Interbank exposures: An empirical examination of contagion risk in the Belgian Banking System. Internationa Journal of Central Banking 3(2): 123-71

Demirgüç-Kunt, A., L. Klapper, D. Singer, S. Ansar and J.R. Hess (2018).The Global Findex Database 2017: Measuring Financial Inclusion and the Fintech Revolution, World Bank, Washington D.C., April.

Diamond, D. and P. Dybvig (1983). Bank runs, deposit insurance and liquidity. Journal of Political Economy, 91, 401-19.

Esteves, P.S (2007) Are ATMs/POS Data relevant when forecasting Private Consumption. Technical Report.

Furfine, C. Interbank Exposures: Quantifying the risk of Contagion. Journal of Money, Credit and Banking, 35, 2003.

Frost, Gambacorta and Gambacorta (2020). The Matthew effect and modern finance: on the nexus between wealth and inequality, financial development anf financial technology. Working Paper No. 871

FSB (2016) Fintech: Describing the landscape and a framework for analysis, March; and FSB(2017a).

Galbraith, J. and G. Tkacz (2007). Electronic transactions as high-frequency indicators of economic activity. Bank of Canada, Staff Working paper.

Gambacorta, L. and H. Shin (2018). Why bank capital matters for monetary policy. Journal of Financial Intermediation, v.35, pp. 17-29

Galbraith, J. and G. Tkacz (2015). Nowcasting GDP with electronic payment data. ECB Statistics paper Series.

Galbraith, J. and Tkacz (2009). A note on monitoring dail economic activity via electronic transaction data. CIRANO Working Papers 2009-23 Hopkins, A. and M. Sherman (2020). How has the Covid-19 pandemic affected daily spending patterns. Central bank of Ireland, QB2

Jack, W., and T. Suri (2011), The Economics of M-PESA, NBER Working Paper.

Jack, W., T. Suri and R. Townsend (2010), Monetary Theory and Electronic Money: Lessons from the Kenyan Experience, Economic Quarterly, 96(1), First Quarter.

Jagannathan, M., Stephen, C. and M. Weisbach (2000). Financial Flexibility and The Choice Between Dividends and Stock Repurchases. Journal of Financial Economics,57, 355-384. Reprinted in Recent Developments in Corporate Finance, edited by Jay Ritter, Edward Elgar publishing, 2005.

Kaminsky, G. L., and C. Reinhart (2000). On Crises, Contagion and Confusion, Journal of International Economics, 51, 145--168.

Mas, Ignacio, and Olga Morawczynski (2009). Designing Mobile Money Services: Lessons from M-PESA,Innovations, 4(2), 77-92, MIT Press

Muller, J. (2003). Two approaches to assess contagion in the interbank market. Swiss national Bank (Decemeber)

Philippon, T. (2016). The FinTech Opportunity, NBER Working Paper, 22476, August.

Rao, Madanmohan (2011). Mobile Africa Report 2011: Regional Hubs of Excellence and Innovation.

Rysman, M. and Schuh, S. (2017). New innovations in payments.NBER Innovation Policy and the Economy, 17(1), 27-48.

Van Lelyveld I. and Lierdorp, F.R (2004). Interbank Contagion in the Dutch Sector. A Sensitive Analysis

Wells, S.J. (2004). Financial interlinkages in the United Kingdon's Interbank market and the risk of contagion.