Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics

Samej Wakode

doi:doi:10.11648/j.ijfbr.20200603.13

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Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics

Samej Wakode

Published in International Journal of Finance and Banking Research (Volume 6, Issue 3)

Received: 15 May 2020 Accepted: 28 May 2020 Published: 4 June 2020

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Abstract

COVID-19 (coronavirus disease 2019) pandemic has affected the length and breadth of various industries and banking is one of the most distressed sectors. The main objective of the paper was to manifest the influence of COVID-19 on the credit exposure of a bank. Conventional risk management of a bank is having its business intelligence dashboard to monitor credit exposure and make vital decisions based on it. But because of uncertainty like an epidemic, COVID-19, those visualizations/information fail to convey the impact of an epidemic on the business of a bank and create a gap, which in turn hurts the institution being not able to make accurate and strategic decisions. To bridge that gap, this study uses a statistical technique - Multivariate analysis of variance to choose and find out risk metrics for a bank which has a significant impact because of COVID-19 and developed a COVID-19 risk indicator parameter, which is the integrated measure of both COVID-19 data and credit risk metrics. The analysis uses a business intelligence tool, Tableau, to visualize geographically impact for a bank as per selected risk metrics and also displays industry-wise impact by integrated results of COVID-19 data, which extracts summarize version of most/least impacted counties and most/least impacted industries concerning bank exposure because of an epidemic. The study concluded that having this methodology and visualization of information available to risk management department or senior management of a bank, this will help them to make decisions like industry-wise relaxation on the credit products, before an asset becomes sub-standard take proactive measures such as debt restructuring, by looking at most impacted industries and banks credit exposure, appraise the provisioning factor and many more critical decisions.

Published in	International Journal of Finance and Banking Research (Volume 6, Issue 3)
DOI	10.11648/j.ijfbr.20200603.13
Page(s)	51-56
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2020. Published by Science Publishing Group

Keywords

COVID-19, Credit Risk Metrics, Risk Management of Bank, Visualization

References

[1]	Oliver Wyman Report (2020, March 5) COVID-19: How Should Risk Functions Respond? Retrieved from https://www.oliverwyman.com/our-expertise/insights/2020/ma r/covid19-risk.html.
[2]	Basel Committee on Banking Supervision Report (2020, April) Measures to reflect the impact of Covid-19. Retrieved from https://www.bis.org/bcbs/publ/d498.pdf.
[3]	Konovalova N., Kristovska I., Kudinska M. (2016, June). Credit risk management in commercial banks. Polish Journal of Management Studies, 13 (2), 90-100.
[4]	Njanike, K (2009). The Impact of Effective Credit Risk Management on Bank Survival. Annals of the University of Petrosani, Economics, 9 (2), 173-184.
[5]	Thomas Zink, Martin stiller (2020, March 26). COVID-19 and its Impact on the Banking Industry. Retrieved from https://blog-idcuk.com/covid-19-impact-banking-industry/.
[6]	Shaun Crawford (2020, April 8). How can your industry respond at the speed of COVID-19’s impact? Retrieved from https://www.ey.com/en_ae/covid-19/how-can-your-industry-respond-at-the-speed-of-covid-19s-impact.
[7]	Jim Marous (2020, March 12). How Will the Coronavirus Impact the Banking Ecosystem? Retrieved from https://thefinancialbrand.com/93679/digital-banking-fintech-fi nance-investment-coronavirus-impact-trends/.
[8]	Juliane Begenau, Monika Piazzesi, Martin Schneider (2015, June). Banks’ Risk Exposures. Retrieved from https://web.stanford.edu/~piazzesi/banks.pdf.
[9]	Basel Committee on Banking Supervision Report (n.d). Prudential treatment of problem assets – definitions of non-performing exposures and forbearance. Retrieved from https://www.bis.org/bcbs/publ/d403.pdf.
[10]	Thorsten Beck (2020, March 2). Finance in the times of coronavirus. Economics in the Time of COVID-19 (73-76).
[11]	Alicia Tuovila (2019, September 6). Loss Given Default. Retrieved from https://www.investopedia.com/terms/l/lossgivendefault.asp.
[12]	Benjamin M. Friedman (2000, May). Debt restructuring. Retrieved from https://www.nber.org/papers/w7722.pdf.
[13]	James W. Grice, Michiko Iwasaki (2007). A Truly multivariate approach to MANOVA. Applied Multivariate Research, 12 (3), 199-226.
[14]	Nick Masters (2020. Feb 13). Industry Impacts of the Coronavirus. Retrieved fromhttps://www.ibisworld.com/industry-insider/coronavirus-insig hts/industry-impacts-of-the-coronavirus/.
[15]	Chris Miller (2020, March 30). The Effect of COVID-19 on the U.S. Economy. Retrieved from https://www.fpri.org/article/2020/03/the-effect-of-covid-19-on-the-u-s-economy/.
[16]	McKinsey & Co. Report (2020, March 17). Leadership in the time of coronavirus: COVID-19 response and implications for banks. Retrieved from https://www.mckinsey.com/industries/financial-services/our-insights/leadership-in-the-time-of-coronavirus-covid-19-response-and-implications-for-banks.

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Samej Wakode. (2020). Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics. International Journal of Finance and Banking Research, 6(3), 51-56. https://doi.org/10.11648/j.ijfbr.20200603.13

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Samej Wakode. Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics. Int. J. Finance Bank. Res. 2020, 6(3), 51-56. doi: 10.11648/j.ijfbr.20200603.13

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AMA Style

Samej Wakode. Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics. Int J Finance Bank Res. 2020;6(3):51-56. doi: 10.11648/j.ijfbr.20200603.13

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@article{10.11648/j.ijfbr.20200603.13,
  author = {Samej Wakode},
  title = {Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics},
  journal = {International Journal of Finance and Banking Research},
  volume = {6},
  number = {3},
  pages = {51-56},
  doi = {10.11648/j.ijfbr.20200603.13},
  url = {https://doi.org/10.11648/j.ijfbr.20200603.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijfbr.20200603.13},
  abstract = {COVID-19 (coronavirus disease 2019) pandemic has affected the length and breadth of various industries and banking is one of the most distressed sectors. The main objective of the paper was to manifest the influence of COVID-19 on the credit exposure of a bank. Conventional risk management of a bank is having its business intelligence dashboard to monitor credit exposure and make vital decisions based on it. But because of uncertainty like an epidemic, COVID-19, those visualizations/information fail to convey the impact of an epidemic on the business of a bank and create a gap, which in turn hurts the institution being not able to make accurate and strategic decisions. To bridge that gap, this study uses a statistical technique - Multivariate analysis of variance to choose and find out risk metrics for a bank which has a significant impact because of COVID-19 and developed a COVID-19 risk indicator parameter, which is the integrated measure of both COVID-19 data and credit risk metrics. The analysis uses a business intelligence tool, Tableau, to visualize geographically impact for a bank as per selected risk metrics and also displays industry-wise impact by integrated results of COVID-19 data, which extracts summarize version of most/least impacted counties and most/least impacted industries concerning bank exposure because of an epidemic. The study concluded that having this methodology and visualization of information available to risk management department or senior management of a bank, this will help them to make decisions like industry-wise relaxation on the credit products, before an asset becomes sub-standard take proactive measures such as debt restructuring, by looking at most impacted industries and banks credit exposure, appraise the provisioning factor and many more critical decisions.},
 year = {2020}
}

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TY  - JOUR
T1  - Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics
AU  - Samej Wakode
Y1  - 2020/06/04
PY  - 2020
N1  - https://doi.org/10.11648/j.ijfbr.20200603.13
DO  - 10.11648/j.ijfbr.20200603.13
T2  - International Journal of Finance and Banking Research
JF  - International Journal of Finance and Banking Research
JO  - International Journal of Finance and Banking Research
SP  - 51
EP  - 56
PB  - Science Publishing Group
SN  - 2472-2278
UR  - https://doi.org/10.11648/j.ijfbr.20200603.13
AB  - COVID-19 (coronavirus disease 2019) pandemic has affected the length and breadth of various industries and banking is one of the most distressed sectors. The main objective of the paper was to manifest the influence of COVID-19 on the credit exposure of a bank. Conventional risk management of a bank is having its business intelligence dashboard to monitor credit exposure and make vital decisions based on it. But because of uncertainty like an epidemic, COVID-19, those visualizations/information fail to convey the impact of an epidemic on the business of a bank and create a gap, which in turn hurts the institution being not able to make accurate and strategic decisions. To bridge that gap, this study uses a statistical technique - Multivariate analysis of variance to choose and find out risk metrics for a bank which has a significant impact because of COVID-19 and developed a COVID-19 risk indicator parameter, which is the integrated measure of both COVID-19 data and credit risk metrics. The analysis uses a business intelligence tool, Tableau, to visualize geographically impact for a bank as per selected risk metrics and also displays industry-wise impact by integrated results of COVID-19 data, which extracts summarize version of most/least impacted counties and most/least impacted industries concerning bank exposure because of an epidemic. The study concluded that having this methodology and visualization of information available to risk management department or senior management of a bank, this will help them to make decisions like industry-wise relaxation on the credit products, before an asset becomes sub-standard take proactive measures such as debt restructuring, by looking at most impacted industries and banks credit exposure, appraise the provisioning factor and many more critical decisions.
VL  - 6
IS  - 3
ER  -

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Author Information

Samej Wakode

Department of Management Studies, Jamnalal Bajaj Institute of Management Studies, Mumbai, India

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Plain Text BibTeX RIS

APA Style

Samej Wakode. (2020). Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics. International Journal of Finance and Banking Research, 6(3), 51-56. https://doi.org/10.11648/j.ijfbr.20200603.13

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ACS Style

Samej Wakode. Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics. Int. J. Finance Bank. Res. 2020, 6(3), 51-56. doi: 10.11648/j.ijfbr.20200603.13

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AMA Style

Samej Wakode. Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics. Int J Finance Bank Res. 2020;6(3):51-56. doi: 10.11648/j.ijfbr.20200603.13

Copy | Download

@article{10.11648/j.ijfbr.20200603.13,
  author = {Samej Wakode},
  title = {Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics},
  journal = {International Journal of Finance and Banking Research},
  volume = {6},
  number = {3},
  pages = {51-56},
  doi = {10.11648/j.ijfbr.20200603.13},
  url = {https://doi.org/10.11648/j.ijfbr.20200603.13},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijfbr.20200603.13},
  abstract = {COVID-19 (coronavirus disease 2019) pandemic has affected the length and breadth of various industries and banking is one of the most distressed sectors. The main objective of the paper was to manifest the influence of COVID-19 on the credit exposure of a bank. Conventional risk management of a bank is having its business intelligence dashboard to monitor credit exposure and make vital decisions based on it. But because of uncertainty like an epidemic, COVID-19, those visualizations/information fail to convey the impact of an epidemic on the business of a bank and create a gap, which in turn hurts the institution being not able to make accurate and strategic decisions. To bridge that gap, this study uses a statistical technique - Multivariate analysis of variance to choose and find out risk metrics for a bank which has a significant impact because of COVID-19 and developed a COVID-19 risk indicator parameter, which is the integrated measure of both COVID-19 data and credit risk metrics. The analysis uses a business intelligence tool, Tableau, to visualize geographically impact for a bank as per selected risk metrics and also displays industry-wise impact by integrated results of COVID-19 data, which extracts summarize version of most/least impacted counties and most/least impacted industries concerning bank exposure because of an epidemic. The study concluded that having this methodology and visualization of information available to risk management department or senior management of a bank, this will help them to make decisions like industry-wise relaxation on the credit products, before an asset becomes sub-standard take proactive measures such as debt restructuring, by looking at most impacted industries and banks credit exposure, appraise the provisioning factor and many more critical decisions.},
 year = {2020}
}

Copy | Download

TY  - JOUR
T1  - Efficacious Scrutinizing of COVID-19 Impact on Banking Using Credit Risk Metrics
AU  - Samej Wakode
Y1  - 2020/06/04
PY  - 2020
N1  - https://doi.org/10.11648/j.ijfbr.20200603.13
DO  - 10.11648/j.ijfbr.20200603.13
T2  - International Journal of Finance and Banking Research
JF  - International Journal of Finance and Banking Research
JO  - International Journal of Finance and Banking Research
SP  - 51
EP  - 56
PB  - Science Publishing Group
SN  - 2472-2278
UR  - https://doi.org/10.11648/j.ijfbr.20200603.13
AB  - COVID-19 (coronavirus disease 2019) pandemic has affected the length and breadth of various industries and banking is one of the most distressed sectors. The main objective of the paper was to manifest the influence of COVID-19 on the credit exposure of a bank. Conventional risk management of a bank is having its business intelligence dashboard to monitor credit exposure and make vital decisions based on it. But because of uncertainty like an epidemic, COVID-19, those visualizations/information fail to convey the impact of an epidemic on the business of a bank and create a gap, which in turn hurts the institution being not able to make accurate and strategic decisions. To bridge that gap, this study uses a statistical technique - Multivariate analysis of variance to choose and find out risk metrics for a bank which has a significant impact because of COVID-19 and developed a COVID-19 risk indicator parameter, which is the integrated measure of both COVID-19 data and credit risk metrics. The analysis uses a business intelligence tool, Tableau, to visualize geographically impact for a bank as per selected risk metrics and also displays industry-wise impact by integrated results of COVID-19 data, which extracts summarize version of most/least impacted counties and most/least impacted industries concerning bank exposure because of an epidemic. The study concluded that having this methodology and visualization of information available to risk management department or senior management of a bank, this will help them to make decisions like industry-wise relaxation on the credit products, before an asset becomes sub-standard take proactive measures such as debt restructuring, by looking at most impacted industries and banks credit exposure, appraise the provisioning factor and many more critical decisions.
VL  - 6
IS  - 3
ER  -

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