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AI in Banking [10 Case Studies] [2024]

In the rapidly altering finance landscape, AI has emerged as a pivotal significance, extending banks’ abilities and reshaping traditional financial patterns. From enhancing customer experiences to mitigating financial risks, AI’s role in banking is pivotal and transformative. This exploration delves into ten distinct case studies where leading banks have successfully implemented AI to address complex challenges in the industry. These examples showcase AI’s innovative applications and highlight its potential to revolutionize banking operations, improve customer service, and bolster financial security. As we navigate through these case studies, we gain insights into the strategic advantages and practical impacts of AI in the banking sector, underscoring its importance in shaping the future of finance.

Related: High-Paying Banking Jobs & Career Paths

Case Study 1: JP Morgan Chase: Streamlining Loan Approvals

The traditional loan approval process is notoriously cumbersome and slow, heavily reliant on manual data handling. This results in prolonged wait times, leading to significant customer dissatisfaction and increasing operational costs due to the extensive need for human oversight and intervention.

To address these inefficiencies, JP Morgan Chase has implemented an advanced AI system that automates key aspects of the loan approval process. This system utilizes machine learning to swiftly and accurately analyze various data points, including applicants’ credit history, recent transaction data, and current financial behaviors. Doing so enhances the speed and accuracy of creditworthiness assessments, reduces reliance on manual processes, and improves overall customer experience by expediting loan approvals.

Overall Impact:

• Increased Speed:  Loan processing times have dramatically reduced from days to minutes and hours.
• Enhanced Customer Satisfaction:  Faster loan approvals increase customer satisfaction and loyalty.
• Cost Efficiency:  Reduced reliance on manual processes decreases operation expenses and improves profitability.
• Scalable Operations:  The bank can handle more loan applications without significantly increasing staff or resources.

Key Learnings:

• Process Efficiency:  AI drastically cuts down the time required for loan approvals.
• Operational Cost Reduction:  Automation reduces the labor-intensive elements of loan processing.
• Enhanced Risk Management:  AI provides a more accurate and comprehensive loan risk assessment.
• Customer Retention:  Improved process speeds and accuracy improve customer retention rates.

Future Prospects:

AI algorithms could be enhanced for faster processing, achieving near-instant approval times. Future iterations may further integrate broader economic indicators to refine credit risk assessments, enhancing personalized lending strategies.

Case Study 2: Bank of America: Erica, the AI-Powered Financial Assistant

As digital banking gains traction, customer expectations are also evolving. Users now demand personalized services on-demand and easily accessible through their digital devices. This shift has pushed banks to find innovative solutions to meet these new customer demands without compromising service quality.

Bank of America responded to this digital shift by launching Erica, an AI-driven virtual assistant designed to enhance the mobile banking experience. Accessible via mobile apps, Erica offers a wide range of functionalities that cater to the modern banking customer’s needs. These include handling transaction queries, updating credit reports, and providing proactive financial advice. Erica’s capabilities are powered by sophisticated algorithms that analyze user behavior and large datasets, enabling customized and efficient service that meets the high expectations of today’s bank customers.

• Personalized Customer Interaction:  Erica offers tailored banking advice, enhancing user engagement.
• Increased Accessibility:  Round-the-clock availability allows customers to receive instant assistance without waiting for human help.
• Data-Driven Insights:  Erica provides insights based on a deep analysis of user transactions and behaviors, helping customers manage their finances better.
• Operational Efficiency:  The AI assistant handles regular inquiries, leaving humans to deal with more complex issues.
• Enhanced User Experience:  AI-driven tools like Erica improve customer experience by providing quick, personalized service.
• Operational Scalability:  AI can manage increasing volumes of consumer interactions without additional human resources.
• Proactive Service:  AI enables proactive engagement, offering financial advice and alerts that can prevent issues before they arise.
• Customer Data Utilization:  Using AI to analyze customer data effectively can lead to more accurate and useful financial advice.

Erica could develop more sophisticated natural language processing capabilities to manage increasingly complex inquiries and transactions. Integration with IoT devices and other platforms may offer holistic financial management solutions, extending personalized services beyond traditional banking.

Case Study 3: HSBC: Enhancing Anti-Money Laundering Efforts

Money laundering remains a formidable challenge for financial institutions worldwide. Traditional systems designed to detect such activities often struggle under modern financial transactions’ heavy volume and complex nature. These systems can be overwhelmed, resulting in undetected fraudulent activities and significant regulatory penalties for banks.

In response, HSBC has integrated an AI-driven system to bolster its anti-money laundering (AML) efforts. This advanced system employs sophisticated machine learning algorithms to analyze many real-time transactions. By detecting unusual patterns and potential illegal activities, the system can far more effectively differentiate between normal and suspicious activities than traditional methods. This AI-enhanced approach allows HSBC to address the complexities of modern financial crime while improving compliance and reducing the risk of oversight.

• Improved Detection Rates:  The AI system has significantly increased the detection of suspicious transactions, reducing the risk of financial crimes.
• Reduced False Positives:  Enhanced accuracy in distinguishing legitimate from suspicious activities, minimizing disruptions to innocent customers.
• Compliance Efficiency:  AI assists in maintaining compliance with evolving regulatory requirements, adapting more quickly to new rules.
• Cost Reduction:  Automating surveillance reduces the need for extensive manual review teams, lowering operational costs.
• Accuracy in Surveillance:  AI technologies improve the accuracy and efficiency of financial monitoring systems.
• Adaptive Compliance:  AI can adapt quickly to new regulatory changes, aiding compliance efforts.
• Resource Optimization:  Implementing AI reduces the need for large human oversight teams, optimizing resource use.

Future developments may incorporate predictive analytics to detect and predict laundering schemes before they are fully enacted. Integration with international finance monitoring systems could enhance global compliance and tracking capabilities.

Related: Is Banking a stressful job?

Case Study 4: Citibank: Optimizing Customer Service with AI Chatbots

In the fast-paced banking world, high demand for customer service can lead to long wait times and inconsistent service experiences. Such delays and variability often detract from customer satisfaction and can negatively impact customer retention rates. As digital interactions become the norm, banks face the challenge of maintaining high service standards while managing large volumes of customer inquiries efficiently.

Citibank has implemented AI-powered chatbots across its digital platforms to address this challenge. These chatbots are arranged to address a spectrum of consumer inquiries, offer real-time support, and efficiently settle typical issues. By deploying these AI chatbots, Citibank ensures a uniform and agile consumer service experience. The chatbots are equipped to understand and process user queries quickly, offering solutions and guidance instantaneously. This technology reduces the burden on human customer service representatives and enhances overall customer satisfaction by providing timely and reliable support.

• Enhanced Customer Service:  Immediate response to inquiries improves customer satisfaction.
• 24/7 Availability:  Customers receive help anytime without needing human agent availability.
• Consistent Experience:  AI ensures that every customer interaction is handled uniformly, enhancing service reliability.
• Operational Savings:  The chatbots handle routine inquiries, decreasing the workload on human client service agents and decreasing operational costs.
• Service Accessibility:  AI tools can provide constant and consistent consumer service.
• Cost Efficiency:  Automating routine interactions can significantly reduce customer service costs.
• Customer Engagement:  Real-time interactions facilitated by AI can boost customer engagement and loyalty.

AI chatbots could evolve to handle more sophisticated negotiations and problem-solving tasks, further reducing the need for human intervention. Future versions might seamlessly integrate into omnichannel customer service strategies, providing a unified interface across all banking platforms.

Case Study 5: Santander: Predictive Analytics for Loan Default Prevention

Loan defaults pose a great financial risk to banks, affecting their profits and stability. Traditional risk assessment models often fall short in accurately predicting defaults before they occur, primarily because they may not account for dynamic changes in customers’ financial situations or broader economic trends. This limitation leads to unexpected financial losses and inefficient allocation of resources for risk management.

Santander has adopted a proactive approach to this challenge by integrating predictive analytics models powered by AI into its risk management strategy. These models use a combination of historical data analysis and real-time monitoring of account behaviors to detect early warning signs of potential loan defaults. By identifying at-risk customers before defaults occur, Santander can engage with them to offer tailored financial advice, restructuring options, or other support measures. This early intervention helps mitigate risks associated with loan defaults and improves the bank’s and its customers’ overall financial health.

• Reduced Default Rates:  Early identification and intervention have led to a decrease in loan defaults.
• Enhanced Customer Support:  At-risk customers receive tailored advice and restructuring options, improving financial outcomes.
• Operational Efficiency:  The bank optimizes resource allocation by focusing efforts where they are needed the most.
• Improved Risk Management:  Better predictive capabilities allow for more accurate risk pricing and reserve allocation.
• Proactive Risk Management:  Early detection of potential defaults enables more effective mitigation strategies.
• Customer Retention:  Proactive engagement helps maintain customer relationships and loyalty.
• Financial Health:  Improved risk assessment contributes to the bank’s overall financial health and stability.
• Resource Allocation:  AI enables more targeted and efficient use of resources in risk management activities.

Integrating wider socio-economic data could improve predictive models, offering even more precise forecasts of potential defaults. These enhancements allow customized intervention strategies tailored to individual customer profiles and economic conditions.

Case Study 6: Wells Fargo: Fraud Detection Enhancement

Real-time fraud detection in financial transactions presents a major challenge, as traditional methods often lag behind fraudsters’ sophisticated techniques. Wells Fargo faced significant challenges in effectively identifying and preventing fraudulent activities. Their traditional systems struggled to keep up without mistakenly flagging legitimate transactions as fraudulent, leading to customer dissatisfaction and operational inefficiencies.

To address this issue, Wells Fargo implemented an AI-based fraud detection system employing deep learning algorithms to scrutinize real-time transaction patterns. This advanced system is designed to compare each transaction against an extensive database of known fraudulent behaviors, enhancing its ability to make accurate assessments instantly. By doing so, the system significantly improves fraud detection accuracy, minimizing false positives and ensuring that legitimate customer transactions are not disrupted. This method boosts security and enhances the overall customer experience by minimizing delays and errors in transaction processing.

• Improved Fraud Detection: The AI system has a higher accuracy rate in identifying fraudulent transactions, reducing the incidence of fraud.
• Minimized Customer Disruption: Accurate fraud detection means fewer legitimate transactions are flagged incorrectly, ensuring smoother customer experiences.
• Enhanced Security: The system enhances overall transaction security, giving customers greater confidence in using Wells Fargo’s services.
• Cost Efficiency: Decreased fraud incidence reduces financial losses and related costs for the bank.
• Real-Time Processing: AI can process and analyze real-time transactions, offering immediate fraud alerts.
• Data Utilization: Leveraging large datasets enhances the system’s ability to identify and learn from emerging fraud patterns.
• Customer Trust: Improved security measures boost customer trust and satisfaction.

Wells Fargo plans to integrate further enhancements into the AI system, such as adaptive learning capabilities that can evolve with changing fraud tactics. This will allow for even more dynamic and robust fraud prevention mechanisms.

Case Study 7: Barclays: Streamlining Wealth Management

Barclays faced challenges in meeting the high expectations of its high net-worth clients who demand personalized, efficient wealth management services. Traditional methods were slow and often ineffective in providing the customization and rapid service these clients expected, leading to dissatisfaction and operational inefficiencies.

Barclays introduced an AI-driven platform to transform its wealth management services. This platform uses advanced analytics to deeply understand individual client preferences and performance, enabling tailored investment advice and automated portfolio adjustments. This automation enhances service speed and accuracy, improving client satisfaction and streamlining operations.

• Personalized Service: Clients receive highly customized investment advice, improving satisfaction and engagement.
• Increased Efficiency: The AI platform automates routine portfolio management tasks, freeing up advisors to focus on client relationships.
• Better Investment Performance: AI-enhanced analytics provide deeper insights into market trends, aiding better investment decisions.
• Scalability: The platform can efficiently manage many portfolios, scaling as the client base grows.
• Enhanced Customization: AI enables a high degree of personalization in delivering services. This technology tailors interactions to meet individual user needs effectively.
• Advisor Efficiency: Automating routine tasks allows wealth managers to focus more on strategic client interaction.
• Data-Driven Decisions: Utilizing AI for data analysis improves the accuracy and timeliness of investment decisions.

Barclays intends to refine its AI capabilities further, incorporating more comprehensive data sources, including global economic indicators and social trends, to enhance investment strategy recommendations.

Related: Banking Cybersecurity Case Studies

Case Study 8: Deutsche Bank: Optimizing Credit Card Fraud Detection

Credit card fraud poses a major problem for banks, resulting in annual losses amounting to millions and eroding customer trust. This persistent issue challenges financial institutions to enhance their security measures and maintain client confidence. Deutsche Bank faced the challenge of rapidly identifying and mitigating fraudulent credit card activities without affecting genuine transactions.

Deutsche Bank implemented an AI-based solution specifically designed to improve credit card fraud detection. This solution uses advanced machine learning models to monitor and analyze real-time credit card transactions. The system can quickly identify anomalies that suggest fraudulent activity by learning from historical transaction data and continuously adapting to new fraud patterns.

• Increased Detection Accuracy: The AI system significantly enhances the ability to spot fraudulent transactions, reducing financial losses.
• Enhanced Customer Trust: Customers feel more secure using their credit cards, knowing that advanced measures are in place to protect them.
• Operational Efficiency: The automated system allows for faster response times and reduces the workload on manual review teams.
• Reduced False Positives: The system effectively minimizes disruptions to innocent customers by accurately distinguishing between legitimate and fraudulent activities.
• Adaptive Learning: Machine learning models adapting to new data and evolving fraud tactics are more effective than static models.
• Customer Experience: Maintaining a balance between aggressive fraud detection and customer convenience is crucial for customer satisfaction.
• Security as a Priority: Investing in advanced security measures like AI protects the bank’s assets and builds customer loyalty.

Deutsche Bank plans to integrate more granular behavioral analytics to refine the system’s accuracy further. Additionally, collaborating with global financial networks to share fraud intelligence could enhance the system’s predictive capabilities, setting a new standard for fraud prevention in the banking industry.

Case Study 9: Credit Suisse: Enhancing Mortgage Underwriting with AI

Credit Suisse encountered significant challenges in its mortgage underwriting process, which relied heavily on manual input, making it both time-consuming and prone to creating backlogs of applications. This inefficient process delayed loan disbursals and negatively impacted customer satisfaction, as clients experienced lengthy wait times and unpredictable service levels. Streamlining this process was crucial to improving operational efficiency and maintaining customer trust and loyalty.

Credit Suisse adopted an AI-driven approach to transform its mortgage underwriting process. The AI system uses machine learning to assess applicant data such as income, credit score, employment history, market trends, and property evaluations more quickly and accurately than manual methods. This automation allows for faster decision-making and more precise risk assessment.

• Faster Processing Times: The time taken to approve mortgages has been significantly reduced, enhancing customer satisfaction.
• Increased Accuracy: AI provides more accurate assessments of applicant risk profiles, reducing the likelihood of loan defaults.
• Operational Efficiency: Automating routine tasks allows human underwriters to concentrate on handling more complex cases. This shift frees up valuable resources for more critical and detailed work.
• Scalable Underwriting Capacity: The system can handle more applications without additional staff.
• Automation in Risk Assessment: The use of AI for processing and analyzing complex applicant data streamlines risk assessment.
• Improved Customer Experience: Reducing wait times for loan approvals directly impacts customer satisfaction positively.
• Enhanced Decision Making: AI tools provide a deeper insight into potential risks and applicant credibility, aiding better decision-making.

Credit Suisse plans to further enhance the capabilities of its AI system by integrating it with real-time economic indicators and more detailed applicant lifestyle data to predict future financial stability more accurately. This advancement aims to streamline the process and tailor mortgage products more specifically to individual needs, setting a new standard in personalized banking services.

Case Study 10: Standard Chartered: Streamlining Trade Finance Operations

Standard Chartered faced complexities in managing trade finance operations, which involve extensive documentation and verification processes that are traditionally manual and error-prone. These challenges resulted in slow transaction times and higher operational costs, affecting client satisfaction and competitiveness in the global market.

Standard Chartered introduced an AI-driven platform designed to automate and enhance the efficiency of its trade finance operations. Utilizing sophisticated machine learning algorithms, the platform efficiently verifies documents, authenticates data, and streamlines the entire approval process for trade transactions. This integration of advanced technology ensures faster, more accurate handling of the complex documentation and regulatory requirements inherent in trade finance, improving overall transaction speed and reliability. By automating these key steps, the bank has significantly reduced manual errors and sped up the processing of trade finance operations.

• Reduced Processing Time: Transaction times for trade finance operations have been drastically reduced, increasing client satisfaction and transaction volumes.
• Decreased Operational Costs: Automation has minimized the need for extensive manual intervention, significantly cutting operational costs.
• Enhanced Accuracy: The AI system provides a higher level of precision in document verification and data authentication, decreasing the risk of fraud and errors.
• Improved Compliance: The system ensures better adherence to international trade regulations through accurate and automated compliance checks.
• Efficiency through Automation: Automating complex, repetitive tasks can significantly enhance efficiency and accuracy in high-stakes financial operations.
• Client Satisfaction: Quicker processing times and fewer errors directly enhance client relationships and contribute to business expansion.
• Regulatory Compliance: AI tools are vital in ensuring compliance with the continuously changing international trade laws. They help organizations adapt quickly to regulatory updates, maintaining legal integrity across global operations.

Standard Chartered is looking to expand its AI capabilities to include predictive analytics for assessing the potential risks and opportunities in trade finance. Further integration with blockchain technology could enhance security and transparency in international trade transactions, setting new industry standards for efficiency and trust.

Related: Will Banking jobs be Automated?

The integration of AI in banking, as demonstrated through these ten case studies, marks a significant leap toward a more efficient, secure, and customer-centric future in finance. Banks like JP Morgan Chase, Bank of America, HSBC, Citibank, and Santander are at the forefront, harnessing AI to enhance decision-making, streamline operations, and enrich customer interactions. These cases vividly illustrate how AI can effectively address traditional banking challenges, driving significant service delivery and risk management improvements. As the banking industry continues to evolve, the strategic deployment of AI will not only be a competitive advantage but a necessity, paving the way for innovative solutions that meet the complex demands of modern finance.

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Case Study: How Aggressively Should a Bank Pursue AI?

• Thomas H. Davenport
• George Westerman

A Malaysia-based CEO weighs the risks and potential benefits of turning a traditional bank into an AI-first institution.

Siti Rahman, the CEO of Malaysia-based NVF Bank, faces a pivotal decision. Her head of AI innovation, a recent recruit from Google, has a bold plan. It requires a substantial investment but aims to transform the traditional bank into an AI-first institution, substantially reducing head count and the number of branches. The bank’s CFO worries they are chasing the next hype cycle and cautions against valuing efficiency above all else. Siti must weigh the bank’s mixed history with AI, the resistance to losing the human touch in banking services, and the risks of falling behind in technology against the need for a prudent, incremental approach to innovation.

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Siti Rahman, the CEO of Malaysia-headquartered NVF Bank, hurried through the corridors of the university’s computer engineering department. She had directed her driver to the wrong building—thinking of her usual talent-recruitment appearances in the finance department—and now she was running late. As she approached the room, she could hear her head of AI innovation, Michael Lim, who had joined NVF from Google 18 months earlier, breaking the ice with the students. “You know, NVF used to stand for Never Very Fast,” he said to a few giggles. “But the bank is crawling into the 21st century.”

• Thomas H. Davenport is the President’s Distinguished Professor of Information Technology and Management at Babson College, a visiting scholar at the MIT Initiative on the Digital Economy, and a senior adviser to Deloitte’s AI practice. He is a coauthor of All-in on AI: How Smart Companies Win Big with Artificial Intelligence (Harvard Business Review Press, 2023).
• George Westerman is a senior lecturer at MIT Sloan School of Management and a coauthor of Leading Digital (HBR Press, 2014).

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Case Study 7: The Digital Transformation of Banking—An Industry Changing Beyond Recognition

• First Online: 06 February 2020

Cite this chapter

• Hubert Tardieu 6 ,
• David Daly 7 ,
• José Esteban-Lauzán 8 ,
• John Hall 9 &
• George Miller 10  

Part of the book series: Future of Business and Finance ((FBF))

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Partly as a result of the rise of FinTechs, banking is a sector that is facing significant disruption. In this case study, we identify some of the innovations that are being made both by young start-ups and long-established banks. We explore emerging opportunities in terms of business models, as well as how new operating models will boost customer-centricity and optimize costs through intelligent automation. The challenges of strategy, leadership, and attracting and retaining digital talent are analyzed. Finally, we conclude with a discussion of how platforms will enable new ecosystems of partners to work together to create and capture customer value.

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Accenture. (2018). Beyond north Star gazing . https://www.accenture.com/_acnmedia/pdf-85/accenture-banking-beyond-north-star-gazing.pdf . Accessed October 26, 2019.

Bain. New bank strategies require new operating models . https://www.bain.com/contentassets/a97b9014afc84a76ae9fb723d3e94ead/bain_brief_new_bank_strategies_require_new_operating_models.pdf . Accessed October 26, 2019.

The Financial Brand. Is the banking industry prepared for a world without bankers ? https://thefinancialbrand.com/86253/banking-future-of-work-training-digital-trends/ . Accessed October 26, 2019.

Capgemini. (2017, October). The digital talent gap . https://www.capgemini.com/wp-content/uploads/2017/10/report_the-digital-talent-gap_final.pdf . Accessed October 26, 2019.

Efma. (2018, September). World retail banking report 2018 . https://www.efma.com/study/detail/28603 . Accessed October 26, 2019.

EY. (2018, June). How convergence in banking could be an opportunity for growth . https://consulting.ey.com/convergence-banking-opportunity-growth/ . Accessed October 26, 2019.

EY. (2016). Global consumer banking survey . https://eyfinancialservicesthoughtgallery.ie/wp-content/uploads/2016/10/ey-the-relevance-challenge-2016.pdf . Accessed October 26, 2019.

IDC. (2018, March). The business value of the stripe payments platform . https://stripe.com/files/payments/IDC_Business_Value_of_Stripe_Platform_Full%20Study.pdf

KPMG. (2019, July). The future of digital banking: Banking in 2030. https://home.kpmg/au/en/home/insights/2019/07/future-of-digital-banking-in-2030.html . Accessed October 26, 2019.

McKinsey. (2018, August). The lending revolution: How digital credit is changing banks from the inside . https://www.mckinsey.com/business-functions/risk/our-insights/the-lending-revolution-how-digital-credit-is-changing-banks-from-the-inside . Accessed October 26, 2019.

OnDeck. (2019). https://www.ondeck.com/home5-lendstart . Accessed October 26, 2019.

Quartz. (2019, August). Digital banks are racking up users, but will they ever make money ? https://qz.com/1679197/when-will-digital-banks-like-n26-and-revolut-start-making-money/ . Accessed october 26, 2019.

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Tardieu, H., Daly, D., Esteban-Lauzán, J., Hall, J., Miller, G. (2020). Case Study 7: The Digital Transformation of Banking—An Industry Changing Beyond Recognition. In: Deliberately Digital. Future of Business and Finance. Springer, Cham. https://doi.org/10.1007/978-3-030-37955-1_28

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Minna Bank: Japan's first digital bank

Japan’s digital native consumers don't need a brick-and-mortar banking experience, so Minna Bank built a different bank for them—in the cloud.

Call for change

First came the digital natives. Then, the financial technology companies flexed their muscles. Next, we saw a variety of non-banking companies entering the banking field. With all of these rule changes and paradigm shifts affecting banking on a global level, Bank of Fukuoka, the core bank of the Fukuoka Financial Group (FFG), based in Kyushu, Japan, knew they needed to transform. “The number of customers visiting traditional branches of the FFG decreased by 40% over the past 10 years, while the number of customers using internet banking increased by 2.4 times over the same period,” said Koji Yokota, President, Minna Bank. To create a bank for everyone—including digital natives—FFG would have to change. But how?

FFG began by establishing iBank Marketing Corporation, a platform company to explore potential business models for the bank of the future by connecting the financial and non-financial sectors with local communities. Kenichi Nagayoshi, the founder of iBank Marketing and Director and Vice President of Minna Bank, explained:

"Our mission was to create innovative financial services, which is why we launched iBank Marketing to develop simple financial functions and digital marketing, with data and analytics at its core. Our core product app, Wallet+, has been downloaded more than 1.6 million times. We thought it was time to create a new platform for financial services now that the game is changing."

We chose Accenture as our partner largely because of their global digital expertise in technology, in design, and in data analytics. This, combined with their ability to execute, enabled us to launch our service on time, even in the midst of the COVID-19 pandemic. Accenture is an excellent company and our best partner.

Koji Yokota / President, Minna Bank

Minna Bank has won the "Brand of the Year" award in the brand category of the Red Dot Design Award 2021, one of the world's three major design awards. They are the first Japanese company to win this award, and the first financial institution in the world to win it. The company also won "Best of the Best" (the highest award of the year) in the Communication Design category (Applications) and "Red Dot" in the Communication Design category (Brand Design & Identity), winning three awards simultaneously.

When tech meets human ingenuity

Under these circumstances, FFG is implementing a "two-way approach" in digital transformation. While FFG, which has a traditional bank, is steadily implementing digital transformation, the approach is to establish Japan's first digital bank, Minna Bank, as an organization to implement digital transformation in a single step without the constraints of the existing business. This bank was the first bank in the world to build a full cloud banking system, and the system was built in the midst of a pandemic, with overwhelming speed.

Minna Bank was designed as a digital technology company that provides financial services to digital native customers. “We looked all over the world for a suitable platform for a digital bank, but there was no banking system built in the public cloud. So we decided to create a full cloud bank ourselves,“ said Nagayoshi.

Accenture is providing support in the adoption of Agile development and in multiple areas such as automation, strategy and talent development. Its Banking, Strategy & Consulting, Technology, and Interactive teams have come together from Fukuoka, Osaka, Tokyo, Aizu, Hokkaido and two overseas locations, transcending national and organizational boundaries to partner with Minna Bank.

In addition to its own resources, Accenture has drawn on its vast ecosystem of technology partners—in this case, industry leaders such as Google, Microsoft, AWS, Salesforce and Oracle—to take advantage of their solutions and best practices.

Specifically, in the "Zero Bank Core Solution" jointly developed by Minna Bank and Accenture, the core system will be implemented on Google Cloud using Accenture's Digital Experience and cloud-first approach, connected technology and cloud-native core solution. For contact center operations, Amazon Web Services (AWS), Amazon Connect and Salesforce's Service Cloud have been combined. Microsoft's Azure is being used for the virtual desktop infrastructure for employee and system operations, and Oracle Cloud is being used for the accounting system. Collaboration with these solution providers has allowed Minna Bank to build its foundation as a cloud-first business with the latest technology available worldwide.

In 2020, in the midst of the COVID-19 pandemic, the Minna Bank project team continued to press forward. It took no more than 18 months to invent and launch a transformational bank in a country with strict regulations governing financial institutions—an unprecedented achievement.

"If it wasn't for cloud, we would have been six months late in opening. Cloud's scalability, speed of deployment and efficiency in fixing bugs are the reasons for the agility of our banking services," said Yokota.

A valuable difference

Minna Bank differs from traditional banks not only by virtue of its operating model, but also its marketing and promotion. Instead of using mass media, it actively employs social media and develops promotions by observing mentions among users. This approach is made possible by a user interface and experience that perfectly matches the preferences of the bank’s target market. To target digital natives, Accenture's team of designers pursued a simple and appealing graphical presentation with minimal descriptive information.

The planning and design process started with a thorough understanding of the thinking and behavior of digital natives, and a commitment to develop services from the customer's perspective: when and how do they want to use financial services? This approach enabled Minna Bank to become a frictionless app that people want to use every day. It is also a portal for non-financial services, providing great value to customers by turning data-based marketing into a service. "We are the first bank in Japan to truly integrate financial and non-financial data into a single service," said Nagayoshi.

Minna Bank has three core business concepts:

• Give shape to everyone's voice—provide new financial services in line with changes in customer behavior.
• Deliver the best for everyone—become a comprehensive financial concierge based on an understanding of customers.
• Integrate into people's daily lives—realize the concept of a BaaS (Banking as a Service) business.

BaaS is a new banking system offering based on the Accenture Cloud Native Core Solution. It helps business partners to create new value in the banking industry.

Minna Bank, a unique digital entity, is a bank for the age of a data-driven society. It will continue to be a bank that explores the potential of hyper-personalization and makes customers say "Wow!”

"As Japan's first digital bank, Minna Bank will be the epicenter of innovation in the Japanese financial industry. Accenture is committed to continuing to be an engine of innovation for Minna Bank," said Masashi Nakano, Senior Managing Director, Financial Services, Accenture.

Japan's first digital bank

Launched the business in 18 months

of employees are engineers

As Japan's first digital bank, Minna Bank will be the epicenter of innovation in the Japanese financial industry. Accenture is committed to continuing to be an engine of innovation for Minna Bank

Masashi Nakano / Senior Managing Director, Financial Services, Accenture Japan Ltd

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Scaling gen AI in banking: Choosing the best operating model

Generative AI (gen AI) is revolutionizing the banking industry as financial institutions use the technology to supercharge customer-facing chatbots , prevent fraud, and speed up time-consuming tasks such as developing code, preparing drafts of pitch books, and summarizing regulatory reports.

About the authors

This article is a collaborative effort by Kevin Buehler , Alison Corsi, Mina Jurisic, Larry Lerner , Andrea Siani, and Brian Weintraub , representing views from McKinsey’s Banking Practice and Risk & Resilience Practice.

The McKinsey Global Institute (MGI) estimates that across the global banking sector, gen AI could add between $200 billion and $340 billion in value annually, or 2.8 to 4.7 percent of total industry revenues, largely through increased productivity . 1 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. However, as banks and other financial institutions move to quickly implement the technology, challenges are emerging. Getting gen AI right can potentially unlock tremendous value; getting it wrong can lead to complications . Companies across industries face gen AI risks , including the generation of false or illogical information, intellectual property infringement, limited transparency in how the systems function, issues of bias and fairness, security concerns, and more.

In a previous article, we explored a series of strategies that banks could use to capture the full value of gen AI . Achieving sustained value, beyond initial proofs of concept, requires strong capabilities across seven dimensions:

• strategic road map
• operating model
• risk and controls
• adoption and change management

These dimensions are interconnected and require alignment  across the enterprise. A great operating model on its own, for instance, won’t bring results without the right talent or data in place.

This article takes a closer look at one of these seven dimensions: the operating model, which is essentially a blueprint for how a business puts strategy into action. Subsequent articles will examine some of the other dimensions. In this article, we explain what an operating model is and why it is important, then delve into the operating-model archetypes that have emerged for gen AI in banking—including the one with the best record of success. Finally, we go over important decisions financial institutions need to make as they set up a gen AI operating model.

We have found that across industries, a high degree of centralization works best for gen AI operating models. Without central oversight, pilot use cases can get stuck in silos and scaling becomes much more difficult. Looking at the financial-services industry specifically, we have observed that financial institutions using a centrally led gen AI operating model are reaping the biggest rewards. As the technology matures, the pendulum will likely swing toward a more federated approach, but so far, centralization has brought the best results.

A centrally led gen AI operating model is beneficial for several reasons:

• Given the scarcity of top gen AI talent, centralization allows the enterprise to allocate talent in a way that is more likely to benefit the entire organization. A centrally led operating model can also help the organization build a world-class, cohesive gen AI team that fosters a sense of camaraderie, helping attract and retain talent.
• In a rapidly changing environment where new large language models and gen AI features are regularly being introduced, a central team can stay on top of the evolving gen AI landscape better than several teams dispersed across an organization.
• A centrally led operating model is useful early on in an enterprise’s gen AI push, when it is necessary to make frequent and important decisions on matters such as funding, tech architecture, cloud providers, large language model providers, and partnerships.
• Risk management and keeping up with regulatory developments are easier with a centrally led approach.

Choosing an operating model isn’t a simple binary approach, however. A financial institution can draw insights from the details explored in this article, decide how much to centralize the various components of its gen AI operating model, and tailor its approach to its own structure and culture. An organization, for instance, could use a centralized approach for risk, technology architecture, and partnership choices, while going with a more federated design for strategic decision making and execution.

The importance of the operating model

An operating model is a representation of how a company runs, including its structure (roles and responsibilities, governance, and decision making), processes (performance management, systems, and technology), and people (skills, culture, and informal networks).

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Financial institutions that successfully use gen AI have made a concerted push to come up with a fitting, tailored operating model that accounts for the new technology’s nuances and risks, rather than trying to incorporate gen AI into an existing operating model. We have observed that the majority of financial institutions making the most of gen AI are using a more centrally led operating model for the technology, even if other parts of the enterprise are more decentralized. This is likely to evolve as the technology matures.

The right operating model for a financial-services company’s gen AI push should both enable scaling and align with the firm’s organizational structure and culture; there is no one-size-fits-all answer. An effectively designed operating model, which can change as the institution matures, is a necessary foundation for scaling gen AI effectively.

In essence, a suitable operating model enables the financial institution to efficiently carry out three types of activities:

• Strategic steering . Identify clusters, or domains, of gen AI use cases that align with the enterprise’s strategic objectives; sort them by priority into a road map that maximizes value while managing risk; and monitor value creation in order to ensure efficient resource allocation.
• Standard setting . Define common standards (such as those concerning technology architecture choices, data practices, and risk frameworks and controls) to increase efficiency and use insights learned from completed projects on new ones.
• Execution . Design and test use cases’ technical solutions, put the use cases that meet the appropriate performance and safety criteria into production, and scale them if there is a business case for doing so, ensuring that their impact is tracked and delivered.

Operating-model archetypes for gen AI in banking

Banks and other financial institutions can take different approaches to how they set up their gen AI operating models, ranging from the highly centralized to the highly decentralized.

We recently conducted a review of gen AI use by 16 of the largest financial institutions across Europe and the United States, collectively representing nearly $26 trillion in assets. Our review showed that more than 50 percent of the businesses studied have adopted a more centrally led organization for gen AI, even in cases where their usual setup for data and analytics is relatively decentralized. This centralization is likely to be temporary, with the structure becoming more decentralized as use of the new technology matures. Eventually, businesses might find it beneficial to let individual functions prioritize gen AI activities according to their needs.

Among the financial institutions we studied, four organizational archetypes have emerged, each with its own potential benefits and challenges (exhibit).

Highly centralized

Potential benefits. This structure—where a central team is in charge of gen AI solutions, from design to execution, with independence from the rest of the enterprise—can allow for the fastest skill and capability building for the gen AI team.

Potential challenges. The gen AI team can be siloed from the decision-making process. It can also be distant from the business units and other functions, creating a possible barrier to influencing decisions.

Centrally led, business unit executed

Potential benefits. This archetype has more integration between the business units and the gen AI team, reducing friction and easing support for enterprise-wide use of the technology.

Potential challenges. It can slow execution of the gen AI team’s use of the technology because input and sign-off from the business units is required before going ahead.

Business unit led, centrally supported

Potential benefits. With this archetype, it is easy to get buy-in from the business units and functions, as gen AI strategies bubble from the bottom up.

Potential challenges. It can be difficult to implement uses of gen AI across various business units, and different units can have varying levels of functional development on gen AI.

Highly decentralized

Potential benefits. It is easy to get buy-in from the business units and functions, and specialized resources can produce relevant insights quickly, with better integration within the unit or function.

Potential challenges. Business units that do their own thing on gen AI run the risk of lacking the knowledge and best practices that can come from a more centralized approach. They can also have difficulty going deep enough on a single gen AI project to achieve a significant breakthrough.

The operating model with the best results

At this very early stage of the gen AI journey, financial institutions that have  centralized their operating models appear to be ahead. About 70 percent of banks and other institutions with highly centralized gen AI operating models have progressed to putting gen AI use cases into production, 2 Live use cases at minimal-viable-product stage or beyond. compared with only about 30 percent of those with a fully decentralized approach. Centralized steering allows enterprises to focus resources on a handful of use cases, rapidly moving through initial experimentation to tackle the harder challenges of putting use cases into production and scaling them. Financial institutions using more dispersed approaches, on the other hand, struggle to move use cases past the pilot stage.

The nascent nature of gen AI has led financial-services companies to rethink their operating models to address the technology’s rapidly evolving capabilities, uncharted risks, and far-reaching organizational implications. More than 90 percent of the institutions represented at a recent McKinsey forum on gen AI in banking reported having set up a centralized gen AI function to some degree, in a bid to effectively allocate resources and manage operational risk.

Our surveys also show that about 20 percent of the financial institutions studied use the highly centralized operating-model archetype, centralizing gen AI strategic steering, standard setting, and execution. About 30 percent use the centrally led, business unit–executed approach, centralizing decision making but delegating execution. Roughly 30 percent use the business unit–led, centrally supported approach, centralizing only standard setting and allowing each unit to set and execute its strategic priorities. The remaining institutions, approximately 20 percent, fall under the highly decentralized archetype. These are mainly large institutions whose business units can muster sufficient resources for an autonomous gen AI approach.

Centralization isn’t friction free. The main obstacles to implementing a centralized operating model have so far stemmed from disagreements over the strategic road map, funding mechanisms, and talent pooling as units fear losing out on crucial resources or having their operational priorities overlooked.

The financial-services companies that have best managed the transition to gen AI already had a high level of organizational agility, allowing them to quickly rework processes and flexibly pool resources, either by locating them in a central hub or by creating ad hoc, centrally coordinated, agile squads to execute use cases. Compared with a traditional AI squad, gen AI teams tend to feature more significant involvement from cloud engineers, business domain experts, and risk and compliance professionals from the beginning of a use case. This is because of two factors: the highly iterative nature of the gen AI development process and the need to consider, even in the early development stage, unforeseen or speculative implications of scaling the applications.

As gen AI technology and organizations’ grasp of its implications mature, the operating model might swing toward a more federated design in both strategic decision making and execution, while standard setting is the likeliest candidate for continued centralization (for example, in risk management, tech architecture, and partnership choices).

A checklist of essential decisions to consider

Choosing and implementing a gen AI operating model requires leaders at financial institutions to make decisions in various areas, including both those directly implicated in the operating model and those that fall into other areas but affect how the model works. Here is a checklist executives can keep in mind as they come up with the best operating model for their organizations:

• Strategy and vision . First, the financial institution needs to decide which leaders will define its gen AI strategy and whether that will be done on an enterprise-wide or business unit level. This should include a vision for the potential value at stake and an assessment of which functions or processes are likely to be affected the most by gen AI.
• Domains and use cases . Next, the institution should ascertain who will determine the enterprise domains, or clusters, of gen AI use cases and the specific use cases within those domains.
• Deployment model . Regarding the implementation of the domains and use cases, the institution should decide whether it will be a “taker” (procuring targeted solutions from vendors), a “shaper” (integrating broader solutions from vendors), or a “maker” (developing in-house solutions that reshape the core business).
• Funding . The institution will need to set out how gen AI use cases will be funded, which will depend on how centralized or decentralized its gen AI approach is. Banks typically fund use cases through a combination of individual business units and a foundation-building central team dedicated to gen AI.
• Talent . The enterprise should define which skills will be needed for gen AI initiatives, then put in place the necessary talent through hiring, upskilling, strategic outsourcing, or a combination of all these strategies. Another step will be to determine the role of “translators” who understand both the business needs and technical requirements of implementing gen AI use cases and domains.
• Risk . The financial institution should determine who defines risk guardrails (such as those related to data privacy and intellectual property infringement) and mitigation strategies. It should also decide to what extent existing frameworks should be adjusted to account for risks specific to gen AI, including whether additional governance is required for particular use cases (such as customer-facing ones).
• Change management . A committee will need to lead the execution of a change management plan to ensure evolutions in mindsets and behaviors as required for the successful adoption of gen AI across the enterprise.

Without the right gen AI operating model in place, it is tough to incorporate enough structure and move quickly enough to generate enterprise-wide impact. To choose the operating model that works best, financial institutions need to address some important points, such as setting expectations for the gen AI team’s role and embedding flexibility into the model so it can adapt over time. That flexibility pertains to not only high-level organizational aspects of the operating model but also specific components such as funding.

The dynamic landscape of gen AI in banking demands a strategic approach to operating models. Banks and other financial institutions should balance speed and innovation with risk, adapting their structures to harness the technology’s full potential. As financial-services companies navigate this journey, the strategies outlined in this article can serve as a guide to aligning their gen AI initiatives with strategic goals for maximum impact. Scaling isn’t easy, and institutions should make a push to bring gen AI solutions to market with the appropriate operating model before they can reap the nascent technology’s full benefits.

Kevin Buehler is a senior partner in McKinsey’s New York office, where Alison Corsi is a consultant, and Brian Weintraub is a partner; Mina Jurisic is a partner in the Paris office, where Andrea Siani is a consultant; and Larry Lerner is a partner in the Washington, DC, office.

The authors wish to thank Antonio Castro for his contributions to this article.

This article was edited by Jana Zabkova, a senior editor in the New York office.

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HDFC Bank Case Study 2021 – Industry, SWOT, Financials & Shareholding

by Jitendra Singh | Mar 4, 2021 | Case Study , Stocks | 1 comment

HDFC Bank Case Study and analysis 2021: In this article, we will look into the fundamentals of HDFC Bank, focusing on both qualitative and quantitative aspects. Here, we will perform the SWOT Analysis of HDFC Bank, Michael Porter’s 5 Force Analysis, followed by looking into HDFC Bank’s key financials. We hope you will find the HDFC Bank case study helpful.

Disclaimer: This article is only for informational purposes and should not be considered any kind of advisory/advice.  Please perform your independent analysis before investing in stocks, or take the help of your investment advisor. The data is collected from  Trade Brains Portal .

Table of Contents

About HDFC Bank and its Business Model

Incorporated in 1994, HDFC Bank is one of the earliest private sector banks to get approval from RBI in this segment. HDFC Bank has a pan India presence with over 5400+ banking outlets in 2800+ cities, having a wide base of more than 56 million customers and all its branches interlinked on an online real-time basis.

HDFC Limited is the promoter of the company, which was established in 1977. HDFC Bank came up with its 50 crore-IPO in March 1996, receiving 55 times subscription. Currently, HDFC Bank is the largest bank in India in terms of market capitalization (Nearly Rs 8.8 Lac Cr.). HDFC Securities and HDB Financial Services are the subsidiary companies of the bank.

HDFC Bank primarily provides the following services:

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• Retail Banking (Loan Products, Deposits, Insurance, Cards, Demat services, etc.)
• Wholesale Banking (Commercial Banking. Investment Banking, etc.)
• Treasury (Forex, Debt Securities, Asset Liability Management)

HDFC Bank Case Study – Industry Analysis

There are 12 PSU banks, 22 Private sector banks, 1485 urban cooperative banks, 56 regional rural banks, 46 foreign banks and 96,000 rural cooperative banks in India. The total number of ATMs in India has constantly seen a rise and there are 209,110 ATMs in India as of August 2020, which are expected to further grow to 407,000 by the end of 2021.

In the last four years, bank credit recorded a growth of 3.57% CAGR, surging to $1698.97 billion as of FY20. At the same time, deposits rose with a CAGR of 13.93% reaching $1.93trillion by FY20. However, the growth in total deposits to GDB has fallen to 7.9% in FY20 owing to pandemic crises, which was above 9% before it.

Due to strong economic activity and growth, rising salaries, and easier access to credit, the credit demand has surged resulting in the Credit to GDP ratio advancing to 56%. However, it is still far less than the developed economies of the world. Even in China, it is revolving around 150 to 200%.

As of FY20, India’s Retail lending to GDP ratio is 18% , whereas in developed economies (US, UK) it varies between 70% – 80%).

Michael Porter’s 5 Force Analysis of HDFC Bank

1. rivalry amongst competitors.

• The banking sector has evolved very rapidly in the past few years with technology coming in, and now it is not only limited to depositing and lending but various categories of loans and advances, digital services, insurance schemes, cards, broking services, etc.; hence, the banks face stiff competition from its rivals.

2. A Threat by Substitutes

• For services like mutual funds, investments, insurances, categorized loans, etc., banks are not the only option these days because a lot of niche players have put their foot in the specialized category, surging the threat by substitutes for the banks.
• Another threat for the traditional banks is NEO Banks. The  Neo Banks  are virtual banks that operate online, are completely digital, and have a minimum physical presence.

3. Barriers to Entry

• Banks run in a highly regulated sector. Strict regulatory norms, huge initial capital requirements and winning the trust of people make it very tough for new players to come out as a national level bank in India. However, if a company enters as a niche player, there are relatively fewer entry barriers.
• With RBI approving the functioning of new small finance banks, payment banks and entry of foreign banks, the competition has further intensified in the Indian banking sector.

4. Bargaining Power of Suppliers

• The only supply which banks need is capital and they have four sources for the capital supply viz. deposits from customers, mortgage securities, loans, and loans from financial institutions. Customer deposits enjoy higher bargaining power as it is totally dependent on income and availability of options.
• Financial Institutions need to hedge inflation, and banks are liable to the rules and regulations of the RBI which makes them a safer bet; hence, they have less bargaining power.

5. Bargaining Power of Customers

• In modern days, customers not only expect proper banking but also the quality and faster services. With the advent of digitalization and the entry of new private banks and foreign banks, the bargaining power of customers has increased a lot.
• In terms of lending, creditworthy borrowers enjoy a high level of bargaining power as there is a large availability of banks and NBFCs which are ready to offer attractive loans and services at low switching and other costs.

HDFC Bank Case Study – SWOT Analysis

Now, moving forward in our HDFC Bank case study, we will perform the SWOT analysis.

1. Strengths

• Currently, HDFC Bank is the leader in the retail loan segment (personal, car and home loans) and credit card business, increasing its market share each year
• The HDFC tag has become a sign of trust in the people as HDFC has come out as a pioneer not only in banking, but loans, insurances, mutual funds, AMC and brokerage.
• HDFC Bank has always been an institution of its words as it has, without fail, delivered its guidance and this has created a strong brand loyalty in the market for them.
• HDFC Bank has very well leveraged the technology to help its profitability, only 34% transaction via Internet Banking in 2010 to 95% transaction in 2020.

2. Weaknesses

• HDFC bank doesn’t have a significant rural presence as compared to its peers. Since its inception, it has focused mainly on high-end clients. However, the focus is shifting in the recent period as nearly 50% of its branches are now in semi-urban and rural areas.

3. Opportunities

• The average age of the Indian population is around 28 years and more than 65% of the population is below 35, with increasing disposable income and rising urbanization, the demand for retail loans is expected to increase. HDFC Bank, being a leader in retail lending, can make the best out of this opportunity.
• With modernization in farming and a rise in rural and semi-urban disposable income, consumer spending is expected to rise. HDFC Bank can increase its market share in these segments by grabbing this opportunity. Currently, the bank has only 21% of the branches in rural areas.
• A lot of niche players have set up their strong branches in respective segments, which has shown stiff competition and has shrined the market share and profit margin for the company. Example – Gold Loans, Mutual Funds , Brokerage, etc.
• In-Vehicle Financing (which is HDFC Bank’s major source of lending income), most of the leading vehicle companies are providing the same service, which is a threat to the bank’s business.

Asian Paints Case Study 2021 – Industry, SWOT, Financials & Shareholding

HDFC Bank’s Management

HDFC Bank has set high standards in corporate governance since its inception.

Right from sticking to their words to proper book writing, HDFC has never compromised with the banking standards, and all the credit goes to Mr. Aditya Puri, the man behind HDFC Bank, who took the bank to such great heights that today its market capitalization is more than that of Goldman Sachs and Morgan Stanley of the US.

In 2020, after 26 years of service, he retired from his position in the bank and passed on the baton of Managing Director to Mr. Shasidhar Jagadishan. He joined the bank as a Manager in the finance function in 1996 and with an experience of over 29 years in banking, Jagadishan has led various segments of the sector in the past.

Financial Analysis of HDFC Bank

• 48% of the total revenue for HDFC bank comes from Retail Banking, followed by Wholesale Banking (27%), Treasury (12%), and 13% of the total comes from other sources.
• Industries receive a maximum share of loans issued by HDFC bank, which is 31.7%, followed by Personal Loans and Services both at a 28.7% share of the total. Only 10.9% of the total loans are issued to Agricultural and allied activities.
• HDFC Bank has a 31.3% market share in credit card transactions, showing a growth of 0.23% from the previous fiscal year, which makes it the market leader, followed by SBI.
• HDFC Bank is the market leader in large corporate Banking and Mid-Size Corporate Banking with 75% and 60% share respectively.
• In Mobile Banking Transaction, the market share of HDFC bank is 11.8%, which has seen a degrowth of 0.66% in the current fiscal year.
• With each year, HDFC Bank has shown increasing net profit, which makes the 1-year profit growth (24.57%) greater than both 3-year CAGR (21.75%) and 5-year CAGR (20.78%).

• Capital Adequacy Ratio, which is a very important figure for any bank stands at 18.52% for HDFC Bank.
• As of Sept 2020 HDFC, is at the second position in bank advances with a 10.1% market share, which has shown a rise from 9.25% a year ago. SBI tops this list with a 22.8% market share, Bank of Baroda is at the third spot with a 6.68% share, followed by Kotak Mahindra Bank (6.35%).
• HDFC Bank is again at the second spot in the market share of Bank deposits with 8.6%. SBI leads with a nearly 24.57% market share. PNB holds 7.5% of the market share in this category, coming out as the third followed by Bank of Baroda with 6.89%.

HDFC Bank Financial Ratios

1. profitability ratios.

• As of FY20, the net profit margin for the bank stands at 22.87%, which has seen a continuous rise for the last 4 fiscal years. This a very positive sign for the bank’s profitability.
• The Net Interest Margin (NIM) has been fluctuating from the range of 3.85% to 4.05% in the last 5 fiscal years. Currently, it stands at 3.82% as of FY20.
• Since FY16, there has been a constant fall in RoE, right from the high of 18.26% to 16.4% as of FY20.

• RoA has been more or less constant for the company, currently at 1.89%, which is a very positive sign.

2. Operational Ratios

• Gross NPA for the bank has fallen from FY19 (1.36) to 1.26, which a positive sign for the company. A similar improvement is also visible in the Net NPA, currently standing at 0.36.
• The CASA ratio for the bank is 42.23%, which has been seeing a continuous fall since FY17 (48.03%). However, there has been a spike rise in FY17 as in FY16, it was 43.25 and in FY18, again came to the almost same level of 43.5.
• In FY19, Advance Growth witnessed a massive spike from 18.71 level in FY18 rising to 24.47%. However, in FY20, it again fell nearly 4 points, coming down to 21.27%.

HDFC Bank Case Study – Shareholding Pattern

• Promoters hold 26% shares in the bank, which has been almost at the same level for the last many quarters. In the December quarter a years ago, the promoter holding was 26.18%. The marginal fall is only due to Aditya Puri retiring and selling few shares for his post-retirement finance, which he stated.
• FIIs own 39.95% shareholding in the bank, which has been increasing for years in every quarter. HDFC bank has been a darling share in the investor community.
• 21.70% of shares are owned by DIIs as of December Quarter 2020. Although it is less than the SeptQ2020(22.90%), it is still far above the year-ago quarter (21.07).
• Public holding in HDFC bank is 12.95% as of Dec Q2020, which has tanked from the year-ago quarter (14.83%) as FIIs increasing their share, which is evident from the rising share prices.

Closing Thoughts

In this article, we tried to perform a quick HDFC Bank   case study. Although there are still many other prospects to look into, however, this guide would have given you a basic idea about HDFC Bank.

What do you think about HDFC Bank fundamentals from the long-term investment point of view? Do let us know in the comment section below. Take care and happy investing!

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The Wi-Fi 6 upgrade at the SBI's GITC resulted in an impressive 2x reduction in employee complaints about network slowness, fostering a more efficient and productive work environment. The improved network capabilities and stability empowered the bank's IT team and tech partners to explore innovative applications and services. With Wi-Fi 6 Access Points supporting a vast number of connected devices, the adoption of IoT devices in the banking environment was facilitated. Additionally, the deployed solution enhanced security with advanced features, ensuring robust network and customer data protection against potential cyber threats.

Solution Deployed

The deployed solution comprised both Hardware and Software components, ensuring a ubiquitous, secure, and scalable Wi-Fi Access solution across the GITC Campus.

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Risk Management in Banking: Case Studies

These case studies involving risk management in banking demonstrate how to handle complex situations successfully. Learn more today.

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Risk Management in banking comes with a significant number of challenges, as banks must stay compliant with endlessly changing rules while making transactions seamless for customers. Eliassen Group is known for our considerable risk and compliance experience in the financial services industry, and we can support teams that must respond to Matters Requiring Attention (MRAs) and other regulatory actions. For proof, look no further than these recent case studies for two top 25 global banks.

Case Study #1: Deployment of Enterprise-Wide Risk Management Framework and Supporting Capabilities

To respond to regulatory actions and MRAs, this global bank needed to deploy an enhanced enterprise-level Risk Management Framework and supporting capabilities across all Front-Line Units with requirements that impacted all lines of business. This would be a daunting task for any company. Luckily, the client had worked with Eliassen Group in the past, and they knew that we could help them embed sustainable, repeatable controls into the client's processes.

We led the deployment of key Risk Management Framework process, system, and policy components across one of the lines of business. Not only did we meet immediate deadlines and go beyond expectations, but aspects of our approach were also adopted by all business groups across the enterprise. During the engagement, we successfully transitioned the program to new executive and workstream leadership as the client made broad organizational changes.

"We focus on helping our clients implement and execute their risk management program, which is why they continue to reach out to us when they need help," said Bill Gienke, Managing Director at Eliassen Group. "I am especially proud of how we collaborated with this client to prioritize and deliver a complex program that met evolving regulatory and internal requirements."

Case Study #2: End-to-End High Risk Client Review via the Enhanced Due Diligence Process To Meet Regulatory Requirements

A second global bank asked Eliassen Group for support with a different but equally difficult scenario – regulators required their wealth management division to improve its Enhanced Due Diligence (EDD) reviews of high-risk customers. After working with Eliassen Group on key risk and compliance initiatives, the client knew that Eliassen Group had financial crimes experience and could help stand up a team to work on the backlog of reviews, train team members, handle quality assurance of risk assessments, make decisions to retain or exit customers, and build a sustainable business as usual process.

Eliassen Group made a powerful impact – the client upgraded their internal audit rating of the Anti-Money Laundering (AML) within their Investment Division for the first time in several years. In addition, we achieved a 99% Quality Control pass rate, and we were recognized as a role model for other teams.

"We are in the business of becoming that trusted strategic partner building client relationships to stand the test of time because when our clients win, we all win," said Jay Gentile, Principal, Client Solutions, at Eliassen Group. "Our progressive delivery models are designed to ensure consistent, repeatable, and sustainable results across processes and teams."

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Journal of Enterprise Information Management

ISSN : 1741-0398

Article publication date: 23 November 2022

Issue publication date: 7 March 2023

The paper aims to help enterprises gain valuable knowledge about big data implementation in practice and improve their information management ability, as they accumulate experience, to reuse or adapt the proposed method to achieve a sustainable competitive advantage.

Design/methodology/approach

Guided by the theory of technological frames of reference (TFR) and transaction cost theory (TCT), this paper describes a real-world case study in the banking industry to explain how to help enterprises leverage big data analytics for changes. Through close integration with bank's daily operations and strategic planning, the case study shows how the analytics team frame the challenge and analyze the data with two analytic models – customer segmentation (unsupervised) and product affinity prediction (supervised), to initiate the adoption of big data analytics in precise marketing.

The study reported relevant findings from a longitudinal data analysis and identified some key success factors. First, non-technical factors, for example intuitive analytics results, appropriate evaluation baseline, multiple-wave implementation and selection of marketing channels critically influence big data implementation progress in organizations. Second, a successful campaign also relies on technical factors. For example, the clustering analytics could promote customers' response rates, and the product affinity prediction model could boost efficient transaction and lower time costs.

Originality/value

For theoretical contribution, this paper verified that the outstanding characteristics of online mutual fund platforms brought up by Nagle, Seamans and Tadelis (2010) could not guarantee organizations' competitive advantages from the aspect of TCT.

• Transaction cost theory
• Big data analytics
• Enterprise information management
• Banking industry
• Precise marketing

He, W. , Hung, J.-L. and Liu, L. (2023), "Impact of big data analytics on banking: a case study", Journal of Enterprise Information Management , Vol. 36 No. 2, pp. 459-479. https://doi.org/10.1108/JEIM-05-2020-0176

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The impact of sandbars on bank protection structures in low-land reaches: case of ganges and brahmaputra-jamuna.

1. Introduction

2. study area, 3. methodology, 3.1. satellite image analysis, 3.2. time series hydraulic data analysis, 3.3. 2d numerical simulation, 3.3.1. governing equations, 3.3.2. model schematization and boundary conditions, 3.3.3. 2d numerical model validation, 4.1. channel-bar dynamics and bank erosion, 4.1.1. meandering river: ganges, 4.1.2. braided river: brahmaputra-jamuna, 4.2. hydraulic impact of sandbar, 4.2.1. hydraulic impact of sandbar in ganges reach, 4.2.2. hydraulic impact of sandbar in brahmaputra-jamuna reach, 5. discussion, 6. conclusions, supplementary materials, author contributions, data availability statement, acknowledgments, conflicts of interest.

• Thorne, C.R.; Tovey, N.K. Stability of composite river banks. Earth Surf. Process. Landf. 1981 , 6 , 469–484. [ Google Scholar ] [ CrossRef ]
• Yu, M.-H.; Wei, H.-Y.; Wu, S.-B. Experimental study on the bank erosion and interaction with near-bank bed evolution due to fluvial hydraulic force. Int. J. Sediment Res. 2015 , 30 , 81–89. [ Google Scholar ] [ CrossRef ]
• Redolfi, M. Free Alternate Bars in Rivers: Key Physical Mechanisms and Simple Formation Criterion. Water Resour. Res. 2021 , 57 , e2021WR030617. [ Google Scholar ] [ CrossRef ]
• Claude, N.; Rodrigues, S.; Bustillo, V.; Bréhéret, J.G.; Tassi, P.; Jugé, P. Interactions between flow structure and morphodynamic of bars in a channel expansion/contraction, Loire River, France. Water Resour. Res. 2014 , 50 , 2850–2873. [ Google Scholar ] [ CrossRef ]
• Crosato, A.; Mosselman, E. An Integrated Review of River Bars for Engineering, Management and Transdisciplinary Research. Water 2020 , 12 , 596. [ Google Scholar ] [ CrossRef ]
• Fredsøe, J. Meandering and braiding of rivers. J. Fluid Mech. 1978 , 84 , 609–624. [ Google Scholar ] [ CrossRef ]
• Tubino, M.; Seminara, G. Free–forced interactions in developing meanders and suppression of free bars. J. Fluid Mech. 1990 , 214 , 131–159. [ Google Scholar ] [ CrossRef ]
• Zhang, K.; Wu, S.; Feng, W.; Zhang, J.; Wen, S. Bar dynamics in a sandy braided river: Insights from sediment numerical simulations. Sediment. Geol. 2020 , 396 , 105557. [ Google Scholar ] [ CrossRef ]
• BWDB. An Investigation on the Causes of Embankment Failure and Recommendations for Sustainable Solutions ; Final Report, Bangladesh Water Development Board (BWDB); Government of the People’s Republic of Bangladesh: Dhaka, Bangladesh, 2023.
• WBIWD. Guidelines for Riverbank Protections & Anti-Sea Erosion Work in West Bengal. lst Amendment; Government of West Bengal Irrigation & Waterways Department (WBIWD), 2019. Available online: https://wbxpress.com/files/2022/05/501-IFC.pdf (accessed on 7 June 2024).
• Parker, G. On the cause and characteristic scales of meandering and braiding in rivers. J. Fluid Mech. 1976 , 76 , 457–480. [ Google Scholar ] [ CrossRef ]
• Ashworth, P.J. Mid-channel bar growth and its relationship to local flow strength and direction. Earth Surf. Process. Landf. 1996 , 21 , 103–123. [ Google Scholar ] [ CrossRef ]
• Best, J.L.; Ashworth, P.J.; Bristow, C.S.; Roden, J. Three-Dimensional Sedimentary Architecture of a Large, Mid-Channel Sand Braid Bar, Jamuna River, Bangladesh. J. Sediment. Res. 2003 , 73 , 516–530. [ Google Scholar ] [ CrossRef ]
• Ashmore, P.E. How do gravel-bed rivers braid? Can. J. Earth Sci. 1991 , 28 , 326–341. [ Google Scholar ] [ CrossRef ]
• Egozi, R.; Ashmore, P. Experimental analysis of braided channel pattern response to increased discharge. J. Geophys. Res. Earth Surf. 2009 , 114 , 1–15. [ Google Scholar ] [ CrossRef ]
• Ashmore, P. Morphology and Dynamics of Braided Rivers ; Elsevier Ltd.: Amsterdam, The Netherlands, 2013; Volume 9. [ Google Scholar ] [ CrossRef ]
• Fujita, Y.; Muramoto, Y. Experimental Study on Stream Channel in Alluvial Rivers. Bull. Disaster Prev. Res. Inst. 1982 , 32 , 49–96. [ Google Scholar ]
• Ikeda, S.; Parker, G.; Sawai, K. Bend theory of river meanders. Part 1. Linear development. J. Fluid Mech. 1981 , 112 , 363–377. [ Google Scholar ] [ CrossRef ]
• Blondeaux, P.; Seminara, G. A unified bar-bend theory of river meanders. J. Fluid Mech. 2016 , 157 , 44–70. [ Google Scholar ] [ CrossRef ]
• Kleinhans, M.G.; van den Berg, J.H. River channel and bar patterns explained and predicted by an empirical and a physics-based method. Earth Surf. Process. Landf. 2011 , 36 , 721–738. [ Google Scholar ] [ CrossRef ]
• Qian, H.; Cao, Z.; Liu, H.; Pender, G. Numerical modelling of alternate bar formation, development and sediment sorting in straight channels. Earth Surf. Process. Landf. 2017 , 42 , 555–574. [ Google Scholar ] [ CrossRef ]
• Duró, G.; Crosato, A.; Tassi, P. Numerical study on river bar response to spatial variations of channel width. Adv. Water Resour. 2016 , 93 , 21–38. [ Google Scholar ] [ CrossRef ]
• Cordier, F.; Tassi, P.; Claude, N.; Crosato, A.; Rodrigues, S.; Van Bang, D.P. Numerical Study of Alternate Bars in Alluvial Channels With Nonuniform Sediment. Water Resour. Res. 2019 , 55 , 2976–3003. [ Google Scholar ] [ CrossRef ]
• Schuurman, F.; Kleinhans, M.G. Bar dynamics and bifurcation evolution in a modelled braided sand-bed river. Earth Surf. Process. Landf. 2015 , 40 , 1318–1333. [ Google Scholar ] [ CrossRef ]
• Rice, S.P.; Church, M.; Wooldridge, C.L.; Hickin, E.J. Morphology and evolution of bars in a wandering gravel-bed river; lower Fraser river, British Columbia, Canada. Sedimentology 2009 , 56 , 709–736. [ Google Scholar ] [ CrossRef ]
• Hooke, J.M.; Yorke, L. Channel bar dynamics on multi-decadal timescales in an active meandering river. Earth Surf. Process. Landf. 2011 , 36 , 1910–1928. [ Google Scholar ] [ CrossRef ]
• Fujita, Y. Bar and channel formation. In River Meandering ; Ikeda, S., Parker, G., Eds.; American Geophysical Union: Washington, DC, USA, 1989; pp. 417–462. [ Google Scholar ]
• Colombini, M.; Seminara, G.; Tubino, M. Finite-amplitude alternate bars. J. Fluid Mech. 1987 , 181 , 213. [ Google Scholar ] [ CrossRef ]
• Luchi, R.; Zolezzi, G.; Tubino, M. Modelling mid-channel bars in meandering channels. Earth Surf. Process Landf. 2010 , 35 , 902–917. [ Google Scholar ] [ CrossRef ]
• Ikeda, S. Prediction of Alternate Bar Wavelength and Height. J. Hydraul. Eng. 1984 , 110 , 371–386. [ Google Scholar ] [ CrossRef ]
• Struiksma, N.; Olesen, K.W.; Flokstra, C.; De Vriend, H.J. Bed deformation in curved alluvial channels. J. Hydraul. Res. 1985 , 23 , 57–79. [ Google Scholar ] [ CrossRef ]
• Gonzalo, D.; Crosato, A.; Tassi, P. Numerical experiments to explore bar management by channel width variations. In Proceedings of the 36th IAHR World Congress, The Hague, The Netherlands, 28 June–3 July 2015; pp. 28–31. [ Google Scholar ]
• Rohde, S.; Schütz, M.; Kienast, F.; Englmaier, P. River widening: An approach to restoring riparian habitats and plant species. River Res. Appl. 2005 , 21 , 1075–1094. [ Google Scholar ] [ CrossRef ]
• Inoue, T.; Ushiyama, T.; Asahi, K.; Yonemoto, M. An Experimental Study on the Bedrock Re-Exposure by Sandbar Formation and Channel Curvature. In Proceedings of the 36th IAHR World Congress, The Hague, The Netherlands, 28 June 2015; pp. 1–8. [ Google Scholar ]
• Yamaguchi, S.; Kyuka, T. A Hydraulic Model Experiment on the Relationship between Sediment Low-Water Revetment or Spur Dikes. In Proceedings of the 38th IAHR World Congress, Panama City, Panama, 1–6 September 2019; pp. 315–320. [ Google Scholar ] [ CrossRef ]
• Nakagawa, H.; Zhang, H.; Baba, Y.; Kawaike, K.; Teraguchi, H. Hydraulic characteristics of typical bank-protection works along the Brahmaputra/Jamuna River, Bangladesh. J. Flood Risk Manag. 2013 , 6 , 345–359. [ Google Scholar ] [ CrossRef ]
• Bishwakarma, K.; Wang, G.-X.; Zhang, F.; Adhikari, S.; Karki, K.; Ghimire, A. Hydrochemical characterization and irrigation suitability of the Ganges Brahmaputra River System: Review and assessment. J. Mt. Sci. 2022 , 19 , 388–402. [ Google Scholar ] [ CrossRef ]
• Allison, M.A. Geologic framework and environmental status of the Ganges-Brahmaputra delta. J. Coast. Res. 1998 , 14 , 826–836. [ Google Scholar ]
• Gupta, A. Large Rivers: Geomorphology and Management ; John Wiley & Sons: Hoboken, NJ, USA, 2007. [ Google Scholar ]
• Islam, M.M. Numerical Simulation of Bed Level Changes of the Ganges. Master’s Thesis, Department of Water Resources Engineering, Bangladesh University of Engineering & Technology, Dhaka, Bangladesh, 1993. [ Google Scholar ]
• BWDB. Hydroinformatics & Flood Forecasting Circle Bangladesh Water Development Board. Available online: http://www.hydrology.bwdb.gov.bd/index.php?pagetitle=home&id=75 (accessed on 12 July 2024).
• BWDB. Morphological Changes of River Ganges over the Last Seven Years (2013 to 2019) ; River Morphology Processing Branch, Bangladesh Water Development Board (BWDB): Dhaka, Bangladesh, 2020. Available online: http://www.hydrology.bwdb.gov.bd/img_upload/ongoing_project/782.pdf (accessed on 7 June 2024).
• FAP 24. Flood Action Plan (FAP) ; Special Report 9, River Survey Project; Flood Plan Coordination Organization, Government of Bangladesh: Dhaka, Bangladesh, 1996.
• Shampa; Roy, B.; Hussain, M.M.; Islam, A.S.; Rahman, M.A.; Mohammed, K. Assessment of Flood Hazard in Climatic Extreme Considering Fluvio-Morphic Responses of the Contributing River: Indications from the Brahmaputra-Jamuna’ s Braided-Plain. GeoHazards 2022 , 3 , 465–491. [ Google Scholar ] [ CrossRef ]
• Gao, B.-C. NDWI A Normalized Difference Water Index for Remote Sensing of Vegetation Liquid Water from Space. Remote Sens. Environ. 1996 , 58 , 257–266. [ Google Scholar ] [ CrossRef ]
• Jana, S. An automated approach in estimation and prediction of riverbank shifting for flood-prone middle-lower course of the Subarnarekha river, India. Int. J. River Basin Manag. 2021 , 19 , 359–377. [ Google Scholar ] [ CrossRef ]
• Yang, C.; Cai, X.; Wang, X.; Yan, R.; Zhang, T.; Zhang, Q.; Lu, X. Remotely sensed trajectory analysis of channel migration in Lower Jingjiang Reach during the period of 1983-2013. Remote Sens. 2015 , 7 , 16241–16256. [ Google Scholar ] [ CrossRef ]
• Hossain, A.; Gan, T.Y.; Baki, A.B.M. Assessing morphological changes of the Ganges River using satellite images. Quat. Int. 2013 , 304 , 142–155. [ Google Scholar ] [ CrossRef ]
• Lesser, G.; Roelvink, J.; van Kester, J.; Stelling, G. Development and validation of a three-dimensional morphological model. Coast. Eng. 2004 , 51 , 883–915. [ Google Scholar ] [ CrossRef ]
• Van Rijn, L.C. Principles of Sediment Transport in Rivers, Estuaries and Coastal Seas ; Aqua Publications: Amsterdam, The Netherlands, 1993. [ Google Scholar ]
• Exner, F.M. On the interaction between water and sediment in rivers. Akad. Wiss. Wien. Math. Naturwiss. Klasse 1925 , 134 , 165–204. [ Google Scholar ]
• Roelvink, D.; Lesser, G.; van der Wegen, M. Evaluation of the Long Term Impacts of an Infiltration BMP Drexel E-Repository and Archive (iDEA). In Proceedings of the 7th International Conference on HydroScience and Engineering College of Engineering, Philadelphia, PA, USA, 10–13 September 2006; pp. 1–6. [ Google Scholar ]
• Moriasi, D.N.; Arnold, J.G.; van Liew, M.W.; Bingner, R.L.; Harmel, R.D.; Veith, T.L. Model Evaluation Guidelines for Systematic Quantification of Accuracy in Watershed Simulations. Trans. ASABE 2007 , 50 , 885–900. [ Google Scholar ] [ CrossRef ]
• Knoben, W.J.M.; Freer, J.E.; Woods, R.A. Technical note: Inherent benchmark or not? Comparing Nash-Sutcliffe and Kling-Gupta efficiency scores. Hydrol. Earth Syst. Sci. 2019 , 23 , 4323–4331. [ Google Scholar ] [ CrossRef ]
• Vijai, H.; Sorooshian, S.; Yapo, P.O. Status of a Utomatic Calibration For Hydrologic Models: Comparison With Multilevel Expert Calibration. J. Hydrol. Eng. 1999 , 4 , 135–143. [ Google Scholar ]
• Oberhagemann, K.; Haque, A.M.A.; Thompson, A. A century of riverbank protection and river training in bangladesh. Water 2020 , 12 , 3018. [ Google Scholar ] [ CrossRef ]
• Taniguchi, J.; Watanabe, K.; Saito, N. Study on the Behavior of Sandbar in a River Channel at the Babamegawa River. Int. J. Geomate 2021 , 20 , 115–120. [ Google Scholar ] [ CrossRef ]
• Nagata, T.; Watanabe, Y.; Yasuda, H.; Ito, A. Development of a meandering channel caused by the planform shape of the river bank. Earth Surf. Dyn. 2014 , 2 , 255–270. [ Google Scholar ] [ CrossRef ]
• Almeida, R.P.; Galeazzi, C.P.; Best, J.; Ianniruberto, M.; Prado, A.H.D.; Janikian, L.; Mazoca, C.E.M.; Tamura, L.N.; Nicholas, A. Morphodynamics and depositional architecture of mid-channel bars in large Amazonian rivers. Sedimentology 2024 , 71 , 1591–1614. [ Google Scholar ] [ CrossRef ]
• Trigg, M.A.; Carr, A.B.; Smith, M.W.; Tshimanga, R.M. Measuring Geomorphological Change on the Congo River Using Century-Old Navigation Charts. In Congo Basin Hydrology, Climate, and Biogeochemistry: A Foundation for the Future ; American Geophysical Union: Washington, DC, USA, 2022; pp. 413–426. [ Google Scholar ] [ CrossRef ]
• ASCE. River Width Adjustment I: Processes and Mechanisms. J. Hydraul. Eng. 1998 , 124 , 881–902. [ Google Scholar ] [ CrossRef ]
• ASCE. River Width Adjustment II: By the ASCE Task Committee on Hydraulics, Bank Mechanics. J. Hydraul. Eng. 1998 , 124 , 903–917. [ Google Scholar ]
• Li, J.; Xia, J.; Kong, L.; Ji, Q.; Li, L.; Chen, F. Geomorphic adjustments of channel bars in the braided reach of the lower Yellow river from 1986 to 2018. Catena 2024 , 236 , 107735. [ Google Scholar ] [ CrossRef ]
• Sarker, M.H.; Thorne, C.R.; Aktar, M.N.; Ferdous, R. Morpho-dynamics of the Brahmaputra-Jamuna River, Bangladesh. Geomorphology 2014 , 215 , 45–59. [ Google Scholar ] [ CrossRef ]
• Zhou, Y.; Xia, J.; Deng, S.; Han, Z. Bank erosion under the impacts of hydraulic erosion, river stage change and revetment protection in the Middle Yangtze River. Geomorphology 2024 , 448 , 109043. [ Google Scholar ] [ CrossRef ]
• Tockner, K.; Paetzold, A.; Karaus, U.; Claret, C.; Zettel, J. Ecology of Braided Rivers ; Special Publication-International Association of Sedimentologists 36; 2006; pp. 339–359. Available online: https://onlinelibrary.wiley.com/doi/10.1002/9781444304374.ch17 (accessed on 7 June 2024). [ CrossRef ]
• Rahman, R.; Rahman, M.M.; Islam, T.G.M.; Sarker, M.H.; Choudhury, S.M.; Ahmed, M. Hydraulic Profiling of Important Fish. Habitats of the Jamuna River ; Final Report; Bangladesh University of Engineering and Technology (BUET): Dhaka, Bangladesh, 2011. [ Google Scholar ]
• Ock, G.; Gaeuman, D.; McSloy, J.; Kondolf, G.M. Ecological functions of restored gravel bars, the Trinity River, California. Ecol. Eng. 2015 , 83 , 49–60. [ Google Scholar ] [ CrossRef ]
• BWDB. Standard Design Manual ; Bangladesh Water Development Board (BWDB): Dhaka, Bangladesh, 1993; Volume 1.
• BWDB. Design Guidelines for River Bank Protection Work ; Bangladesh Water Development Board (BWDB): Dhaka, Bangladesh, 2021.
• Pilarczyk, K.W. Coastal Protection: Design of Seawalls and Dikes incl. Overview of Revetments ; Rijkswaterstaat, Road and Hydraulic Engineering Division Van der Burghweg 1: Delft, The Netherlands, 1991; Volume 1. [ Google Scholar ]

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Shampa; Muktadir, H.M.; Nejhum, I.J.; Islam, A.K.M.S.; Rahman, M.M.; Islam, G.M.T. The Impact of Sandbars on Bank Protection Structures in Low-Land Reaches: Case of Ganges and Brahmaputra-Jamuna. Water 2024 , 16 , 2523. https://doi.org/10.3390/w16172523

Shampa, Muktadir HM, Nejhum IJ, Islam AKMS, Rahman MM, Islam GMT. The Impact of Sandbars on Bank Protection Structures in Low-Land Reaches: Case of Ganges and Brahmaputra-Jamuna. Water . 2024; 16(17):2523. https://doi.org/10.3390/w16172523

Shampa, Hussain Muhammad Muktadir, Israt Jahan Nejhum, A. K. M. Saiful Islam, Md. Munsur Rahman, and G. M. Tarekul Islam. 2024. "The Impact of Sandbars on Bank Protection Structures in Low-Land Reaches: Case of Ganges and Brahmaputra-Jamuna" Water 16, no. 17: 2523. https://doi.org/10.3390/w16172523

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Employee claims against Byju's top Rs 300 crore in ongoing insolvency case

But these claims still need to be reconciled with the complete and accurate books of accounts and records from the corporate debtor, in this case, the former management of byju's.

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First Published: Sep 07 2024 | 10:49 AM IST

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