Banking Trends & Figures 2024

Establish responsible AI policies by implementing stringent security measures such as data encryption, strict access controls, and comprehensive incident response plans. However, these initiatives should not end here, banks must institute monitoring systems that keep these measures up-to-date. By adopting a responsible AI toolkit, banks can pursue AI’s value while mitigating its risks, providing the foundation for trust-building practices. It is also essential that such responsible AI measures do not limit GenAI initiatives but rather, accelerate them.

Conduct a detailed assessment of the bank’s value chain to identify areas where GenAI can create the most value. A clear understanding of these priority areas will determine how each use case contributes to the overarching goals and vision of the bank. This will help align the bank’s resources and efforts to initiatives that have the most meaningful impact on the bottom line.

Develop detailed implementation plans for the top use cases identified. This entails assembling a dedicated core team to enhance speed, repeatability, and oversight. From this, it is important to select pilot projects that can be executed with relative ease and demonstrate clear, tangible benefits. These pilots must focus on core business processes, organisational readiness and repeatability.

Scale use cases by building GenAI into workflows and applications and adapting its patterns across the business. Scaling will typically imply new tooling and integration services with enterprise data and applications. This in part, requires robust data integration services that can seamlessly connect GenAI tools with existing enterprise systems, ensuring smooth data flow and interoperability.

Effective GenAI governance involves establishing clear policies and frameworks that define how AI systems should be developed, deployed, and monitored to ensure they align with regulatory standards and ethical considerations. Establishing robust feedback mechanisms will advance the cause of AI governance, as it would allow for the collection of insights from various stakeholders, ensuring that AI implementations are aligned with evolving user needs and regulations.