Commercial Banking – AI in Banking – Grantland Case

An analysis of Grantland National Bank and Sridharan et al. (2025)

“Al in Asia: Reimagining Banking Operations through Agentic Al.” [Sources: Hempel, Simonson and Coleman 1994:735 — 745 and McKinsey & Company (2025)]

General Requirements

Students are provided (1) the Grantland National Bank (GNB) case detailing the bank’s financial struggles, asset mix, credit risk management issues, and operational constraints; and (2) the McKinsey (2025) “Al in Asia: Reimagining Banking Operations through Agentic Al” report outlining modern Al, GenAI, and agentic Al approaches to banking operations, risk management, customer journeys, and productivity enhancement. Students must integrate the historical bank-management problem with contemporary Al capabilities.

Working in groups of 3 to 4, develop logical and coherent responses to the questions raised by the case. The opinions or decisions should be supported by references to appropriate texts, articles, websites, and current finance and banking practices.

Although all students are expected to play an important role in developing the paper, the final submission should be presented as a comprehensive group project.

Format Requirements

The group paper should be typed and double-spaced; flow as a well-documented, coherent committee paper; cite all sources; have correct formats for the bibliography, footnotes, and references; have on the first page of the paper the title of the paper, the authors’ names, and respective tutorial groups; and have an executive summary.

Part A: Grantland National Bank

SPECIFIC REQUIREMENTS / QUESTIONS:

Part B: Sridharan Et Al. (2025) “Al In Asia: Reimagining Banking Operations Through Agentic Al.”

Using insights from Sridharan et al. (2025), explain which Al capabilities are most relevant to GNB’s specific challenges. These may include lending and credit operations, asset management, margin improvement, and operational efficiency. Students need to be specific and justify their claims.

Students must cite at least 4—6 external references beyond the two provided documents. Examples include:

Academic literature on Al in banking risk management.

BIS or IMF reports on Al supervision in banking.

Industry research (e.g., Deloitte, BCG, IBM Institute for Business Value) on Al credit scoring or agentic Al.

Expert Answers on the Above Questions on AI in Banking

Gartland National Bank – Identification of key problems

The major problems identified are in respect to poor credit risk management such as poor loan assessment, rising default cases etc, asset mix is also inappropriate directly impacting profitability, operational efficiency was also inefficient because of manual processes and high cost of operations along with poor decision making contribute adversely towards margin performance.

AI capabilities for GNB

The most significant capabilities on the basis of insights from McKinsey & Company includes AI in credit risk and lending where by it can be utilised to calculate the credit scoring and allows for assessing the risk of the borrower on a real time basis. The impact would be the better quality of loan, reduction in the overall defaulter rate and better efficiency in approving loans. Along with the credit risk and lending, AI can also be utilised in asset management such as in performing predictive analysis for portfolio optimisation and performing asset allocation using AI.

It would contribute towards improved return on asset performance and also reduce the exposure to the high risk sector. Another application of AI is with respect to margin improvement and achieving operational efficiency. AI can be utilised to achieve cost efficiency through automation, utilising AI chat bots for customer service and for workflow optimisation. The overall impact is reduction in the operational cost and improved profitability performance.

Justification

The above AI capabilities as discussed are quite effective in addressing the weaknesses faced by GNB such as poor credit risk through achieving assessment accuracy using AI, inefficient operations through automation and weak profitability by achieving operational efficiency using AI.

This model answer is reviewed by Rui Shi, financial accounting expert good in performing AI impact on financial businesses.
Disclaimer: This answer is a model for study and reference purposes only. Please do not submit it as your own work.

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