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How Banks Can Leverage Credit Risk Evaluation to Improve Financial Performance

Author

Listed:
  • Daniul Thomas ISENBERG

    (Hofstra University, New York, USA)

  • Mesbaul Haque SAZU

    (Case Western Reserve University, Cleveland, USA)

  • Sakila Akter JAHAN

    (Illinois State University, Normal, USA)

Abstract

The research aims to examine the impact of credit risk evaluation on the financial performance of American and European commercial banks during the period 2017-2021. A set of 37 commercial banks were selected to represent the entire banking industry of those two continents. To measure this relationship, two mathematical models were created. Research has revealed that credit risk evaluation influences the financial performance of the American and European commercial banks as represented by ROE and ROA. The study also concludes that the credit risk evaluation indicators analyzed in this study have a substantial effect on the financial performance of American and European commercial banks. The study suggests banks enhance their credit risk evaluation to generate more profits. It also cites the indicators of non-performing loans or gross loans, provision for facilities loss/net facilities, as well as the leverage ratio as significant in determining credit risk evaluation. Banks must put together strategies that will not only limit the banks’ exposure to credit risk, but also enhance the banks’ performance, as well as competitiveness. Further research can be conducted in developing nations to understand the impact of credit risk evaluation in such economies.

Suggested Citation

  • Daniul Thomas ISENBERG & Mesbaul Haque SAZU & Sakila Akter JAHAN, 2022. "How Banks Can Leverage Credit Risk Evaluation to Improve Financial Performance," CECCAR Business Review, Body of Expert and Licensed Accountants of Romania (CECCAR), vol. 3(9), pages 62-72, September.
  • Handle: RePEc:ahd:journl:v:3:y:2022:i:9:p:62-72
    DOI: 10.37945/cbr.2022.09.07
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    References listed on IDEAS

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    1. Alice Francesca Sproviero, 2020. "Integrated reporting and the epistemic authority of Big Data: An exploratory study from the banking industry," FINANCIAL REPORTING, FrancoAngeli Editore, vol. 2020(2), pages 99-124.
    2. T. Robert Yu & Xuehu Song, 2020. "Big Data and Artificial Intelligence in the Banking Industry," World Scientific Book Chapters, in: Cheng Few Lee & John C Lee (ed.), HANDBOOK OF FINANCIAL ECONOMETRICS, MATHEMATICS, STATISTICS, AND MACHINE LEARNING, chapter 117, pages 4025-4041, World Scientific Publishing Co. Pte. Ltd..
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    Cited by:

    1. Akhter Salahuddin MOHAMMED, 2022. "How Analytics Creates Strategic Business Value: Perspectives from French MNCs," CECCAR Business Review, Body of Expert and Licensed Accountants of Romania (CECCAR), vol. 3(11), pages 65-72, November.
    2. Mohammad BIN HAMMAM & Salatun-Nisa KHAYEB & Abdullah Abu MUHTABIYEN, 2023. "Role of Analytics in Shaping the Supply Chain Industry in the Middle East," CECCAR Business Review, Body of Expert and Licensed Accountants of Romania (CECCAR), vol. 3(12), pages 66-72, December .

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    More about this item

    Keywords

    credit risk; financial performance; commercial banks;
    All these keywords.

    JEL classification:

    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • G32 - Financial Economics - - Corporate Finance and Governance - - - Financing Policy; Financial Risk and Risk Management; Capital and Ownership Structure; Value of Firms; Goodwill

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