Rationalization of Banking Ayush Gundawar

History of Banking

Banking practices have evolved from manual record-keeping to digitalization. Early efforts at mechanization in the 1940s laid the groundwork for technological advancements that define banking today. The 1970s saw a technological revolution and globalization that reshaped financial markets. In the 2000s, banks faced challenges and adapted by focusing on digital transformation. Today, electronic banking services are integral, and banks require expertise in cybersecurity, data analysis, and user experience design.


In the 1940s, a drive for efficiency led to the mechanization of routine tasks within commercial banking. This mechanization, aimed at tasks like check clearing and ledger maintenance, reduced manual labor and accelerated operational speed.

Automated Teller Machines (ATMs) represent another significant step towards banking efficiency. ATMs streamline everyday transactions such as deposits, withdrawals, and balance inquiries, minimizing the need for teller assistance. This shift allows tellers to dedicate their time and expertise to higher-value activities, including personalized customer service and resolving complex banking issues. Research suggests that ATMs can significantly reduce processing costs compared to traditional tellers. For instance, studies show a potential cost reduction of 65% for automated deposits [Boe, 2018]. This focus on automation reflects a broader effort within the banking industry to optimize processes and resource allocation.


Credit scoring algorithms exemplify calculability in banking. These algorithms analyze various financial data points to generate a numerical credit score, essentially transforming an applicant's financial history into a quantifiable risk assessment. While bankers retain final approval authority, credit scores streamline the loan evaluation process, allowing for faster decisions. This approach may limit individual discretion, but it promotes efficiency and consistency in lending practices. As the FDIC highlights, scoring models "summarize available information" into objective risk profiles, fostering consistent application of lending standards throughout the banking system.


Standardized operating procedures and regulations are hallmarks of predictability in banking. This predictability reduces uncertainty and stress for bankers, especially regarding regulatory compliance and audits [Hashmall et al., 2022]. It minimizes disruptions to daily operations and fosters stability within the profession. Banking regulations establish clear guidelines, ensuring consistent application of lending practices and consumer protection measures [Hashmall et al., 2022]. In essence, predictability, achieved through rationalization, benefits both bankers and the overall stability of the banking system.


Centralized decision-making in banking offers greater control for managers, streamlining oversight but potentially limiting employee autonomy and skill requirements. This approach consolidates authority within a hierarchy, enabling control over crucial areas like risk management and resource allocation. While it enhances managerial control, it may also restrict decision-making at lower levels and potentially deskill certain tasks. In essence, centralized decision-making exemplifies control, a key aspect of rationalization, and helps banks achieve operational efficiency through standardized practices.


Rationalization, while aiming for efficiency, can produce unintended consequences. Sociologist George Ritzer calls these "irrationalities" - situations where seemingly efficient practices create problems. Here are two examples:

  • Unpaid Customer Labor: Self-service banking (ATMs, mobile apps) reduces bank costs but burdens customers with unpaid work (managing accounts, depositing checks). This contradicts efficiency by shifting labor and negates the human touch valued by some customers (Ho & Ko, 2008; Parasuraman et al., 2005).
  • Standardization and Loan Problems: Standardized loan approvals aim for predictability but may overlook individual circumstances. Rigid criteria can lead to unjustified rejections or approvals, missing good borrowers or perpetuating biases (Hovsepyan, 2023; Martinez & Kirchner, 2021). This creates an irrational outcome as efficiency trumps borrower potential.

Future of Banking

The relentless pursuit of efficiency, fueled by advancements like AI and blockchain, will significantly reshape the banking industry (Czímer et al., 2022). AI chatbots and blockchain ledgers promise streamlined operations, reduced costs, and enhanced security (Czímer et al., 2022; McKinsey & Company, 2023). However, these advancements are a double-edged sword. AI-powered tools can personalize customer experiences but may displace jobs traditionally done by tellers and customer service representatives (NVIDIA).

The future workforce will require a significant shift. Banks must invest in upskilling and reskilling initiatives to equip employees with data analysis, cybersecurity, and expertise in AI and blockchain technology (World Economic Forum, 2020). These skills will be crucial for navigating the evolving landscape shaped by automation (World Economic Forum, 2019; Wakefield, 2020). By investing in its workforce, the banking industry can ensure a smooth transition into this new era of technological advancement.


Boe, Heidi. “The Role of the ATM in the Transformation of Branch Banking.” ABA Banking Journal, 1 Feb. 2018, bankingjournal.aba.com/2018/02/the-role-of-the-atm-in-the-transformation-of-branch-banking/.

Culbertson, Daniel. “US Business-To-Business Labor Market Update - 2023 Q1.” Indeed Hiring Lab, 25 Apr. 2023, www.hiringlab.org/2023/04/25/us-business-to-business-labor-market-update-2023-q1/.

Czímer, Balázs, et al. “The Future of Banks: A $20 Trillion Breakup Opportunity | McKinsey.” Www.mckinsey.com, 20 Dec. 2022, www.mckinsey.com/industries/financial-services/our-insights/the-future-of-banks-a-20-trillion-dollar-breakup-opportunity.

Hashmall, A. M., Zhang, A. A., Xu, C., Lyons, G. J., & Dikkers, J. (2022, March 16). In brief: Banking regulatory framework in USA. Lexology. https://www.lexology.com/library/detail.aspx?g=d29fb947-7c36-4c11-9958-758dd8a8d534.

Ho, Shu‐Hsun, and Ying‐Yin Ko. “Effects of Self‐Service Technology on Customer Value and Customer Readiness.” Internet Research, vol. 18, no. 4, 15 Aug. 2008, pp. 427–446, https://doi.org/10.1108/10662240810897826.

Hovsepyan, Tigran. “AI in Lending and Loan Management: Impact & Challenges.” Plat.AI, 13 Nov. 2023, plat.ai/blog/ai-in-loan-processing/.

Kreger, Alex, and Linda Zaikovska-Daukste. “Banks Are Cutting Jobs due to Digitalization and AI.” Theuxda.com, theuxda.com/blog/banks-will-cut-millions-of-jobs-in-the-next-decade.

Martinez, Emmanuel, and Lauren Kirchner. “The Secret Bias Hidden in Mortgage-Approval Algorithms – the Markup.” Themarkup.org, 25 Aug. 2021, themarkup.org/denied/2021/08/25/the-secret-bias-hidden-in-mortgage-approval-algorithms.

McKinsey & Company. Jobs Lost, Jobs Gained: Workforce Transitions in a Time of Automation. Dec. 2017, www.mckinsey.com/~/media/BAB489A30B724BECB5DEDC41E9BB9FAC.ashx.

McKinsey & Company. “What Is the Future of Work?” Www.mckinsey.com, 23 Jan. 2023, www.mckinsey.com/featured-insights/mckinsey-explainers/what-is-the-future-of-work.

NVIDIA. “Smart Banking Powered by AI.” NVIDIA, www.nvidia.com/en-us/industries/finance/ai-powered-bank/.

Federal Deposit Insurance Corporation. FDIC. (n.d.). https://www.fdic.gov/regulations/examinations/credit_card/pdf_version/

Parasuraman, A., et al. “E-S-QUAL: A Multiple-Item Scale for Assessing Electronic Service Quality.” Journal of Service Research, vol. 7, no. 3, Feb. 2005, pp. 213–233, journals.sagepub.com/doi/abs/10.1177/1094670504271156, https://doi.org/10.1177/1094670504271156.

Ritzer, George. The McDonaldization of Society: Into the Digital Age. 10th ed., Los Angeles, Sage. Copyright, Dec. 2020.