AI Shaping a New Vision: Challenges and Opportunities for Banking Leaders

1. How is AI changing the perspective and direction of banking leaders?

According to McKinsey research, AI has the potential to bring up to $1 trillion in added value per year. Interest in AI is exploding globally as the technology is widely applied in many industries, driving growing confidence from managers. Analysis from Statista predicts that AI implementation in the financial sector will witness significant growth in the period 2022-2025.

Studies show that AI applications in the industry are increasing significantly, with the rate expected to increase from 8% in 2022 to 43% in 2025. As more businesses adopt AI, the potential for innovation will expand, while application activities will gradually become standardized and customer experiences will also be significantly improved.

With the increasingly evident impact of AI in practice, businesses are increasingly concerned about the implementation time and level of AI implementation, which depends on the capabilities of each organization. Among the industries that are greatly influenced by AI, banking is one of the industries with the strongest application potential thanks to specific factors:

(1) The banking industry has large and highly authentic data sources, giving it an advantage in implementing AI compared to other sectors.

(2) Banks have reached a high level of digital maturity, thanks to many years of investment in information technology (IT).

(3) Industry personnel are familiar with the use of technology in daily operations, making it easy to access and apply breakthrough technologies such as AI.

(4) Many tasks in the industry are repetitive, requiring high connectivity and information processing capabilities, creating a great demand for AI applications in manual processes.

Leaders are well aware of the importance and potential of AI, but many still struggle to effectively implement AI initiatives within their organizations. Typically, business units and functional areas within an enterprise will implement AI projects independently. Initially, this may create positive signals, as broad participation and commitment from departments is a key factor for the organization-wide AI transformation.

However, many large enterprises reported that they have encountered difficulties in coordinating when implementing different AI initiatives and pilots. Units within the enterprise need to establish reasonable standards and processes, while avoiding duplication of research and development. Business leaders are looking for optimal ways to leverage scarce human resources while ensuring maximum productivity and operational efficiency.

As artificial intelligence (AI) becomes more widely adopted in the marketplace, senior leaders will need to create organizational structures that support large-scale AI autonomy. This will require businesses to abandon many of the traditional organizational models that are common in today’s boardrooms and develop new, more flexible organizational metrics and structures to meet the demands of the AI ​​era.

2. How should banking leaders change in the context of AI being applied throughout the bank?

Chief Executive Officer (CEO)

Vision and development strategy:

Define a clear AI vision that aligns with the bank’s short-, medium-, and long-term strategic development goals.

Develop a long-term AI capability roadmap that includes investment priorities, potential partnerships, and innovation goals.

Managing culture and change:

Foster a culture of innovation and continuous learning to apply AI in the business operations of the board and member units.

Communicate the benefits and goals of AI-related initiatives to all stakeholders to promote buy-in and reduce resistance.

Stakeholder engagement:

Engage with stakeholders, including board members, investors, customers, and regulators to communicate the strategic importance and impact of AI.

Directly lead discussions on ethical considerations and societal impacts in AI adoption.

Chief Information Officer (CIO)

Infrastructure and Technology:

Identify investment directions for flexible and easily integrated IT infrastructure to support rapid AI applications, including cloud computing, data storage and processing capabilities.

Implement information security measures to protect sensitive data as well as information related to AI models.

Technology integration:

Ensure seamless integration of AI technologies with existing enterprise systems and platforms

Work with business units to identify areas where AI can enhance operations and services.

Manage service providers and technology partners:

Evaluate and select AI technology providers and partners to work with.

Negotiate contracts and integrate service providers and technology partners to ensure value and operational efficiency.

Chief Data Officer (CDO)

Data Governance and Management:

Establish and enforce data governance policies that ensure data quality, consistency, and security.

Implement data management practices that facilitate efficient data collection, storage, and retrieval.

Building a data strategy:

Develop a comprehensive data strategy that supports AI initiatives, focusing on data accessibility, usability, and analytics.

Drive data-driven decision making across the enterprise.

Defining data privacy and compliance:

Ensure business compliance with data protection regulations, such as GDPR international standards and standards based on Decree 13/2023/ND-CP

Implement security measures to protect customer data.

Chief Financial Officer (CFO)

Investment and Budget Allocation:

Allocate resources and budgets to AI projects, aligned with the business’s phased development strategies.

Assess the financial impact and ROI of AI initiatives to justify the investment

Manage financial risks:

Assess and mitigate financial risks associated with AI, such as model risks, compliance costs, and potential disruptions.

Develop contingency plans to address unforeseen financial challenges.

Continuously measure investment performance:

Identify KPIs to measure the financial performance and impact of AI initiatives.

Regularly review and adjust budgets based on the phased results of AI initiatives

Chief Operating Officer (COO)

Performance:

Identify and prioritize specialized business areas where AI can improve efficiency, such as process automation, fraud detection, and enhanced customer service.

Deploy AI-powered solutions to automate operations and reduce business operating costs.

Process Improvement:

Continuously monitor and improve AI-powered processes to improve performance and optimize operational efficiency.

Encourage cross-functional collaboration to seamlessly integrate AI solutions into the day-to-day operations of the business.

Scalability:

Ensure that AI initiatives are scalable and flexible to adapt to evolving business strategies based on changing customer product/service requirements.

Plan for the long-term sustainability and maintenance of AI initiatives and infrastructure.

Conclusion

By focusing on specific action directions, banking industry leaders play a key role in successfully integrating AI into banking, driving innovation, efficiency and competitive advantage while managing the associated risks and challenges. In addition, banks need to consider facilitating quick and effective access for all employees in the business.

Reference source: Mckinsey

Synthesized by the author group DTSVN - Digital transformation solutions for the Finance - Banking industry.

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