How Can Banks Embrace an AI-First Approach

Artificial intelligence is helping banks address the industry’s changing landscape in several ways, including risk management, fraud detection and prevention, process automation, and more. Still, 7 of 10 senior executives reported little or no gains from AI efforts. So, the question is, how can you turn AI into ROI?

AI technologies are becoming integral to the world, and banks need to deploy these technologies to remain relevant. McKinsey estimates that AI technologies could deliver up to $1 trillion in additional value for global banking annually.

Banks must take a holistic approach to ensure the success of AI projects. This article aims to guide US banks and other financial institutions on the path to becoming AI-first by outlining key steps and strategies to navigate through this transformative journey.

Establish a Clear Vision and Strategy

Define your business objectives – identify specific problem areas where AI can provide value. Align AI initiatives with the bank’s goals and focus on use cases with a high potential for impacts, such as fraud detection, customer service enhancement, or risk management.

Build a Strong Data Foundation

Data forms the cornerstone of every AI project. It is essential to ensure the presence of high-quality, pertinent, and varied datasets. Invest in data cleansing, aggregation, and integration processes to guarantee data accuracy and consistency. Establish robust data governance practices and data infrastructure to support AI initiatives effectively.

Cross-Functional Collaboration

Successful AI projects require collaboration between various teams within the bank, including IT, data science, risk management, compliance, and business units. Effective communication, knowledge sharing, and implementation can be facilitated by promoting a culture of collaboration and creating multidisciplinary teams.

Partnering With Experts

Consider partnering with external AI solution providers, fintech companies, or consultants with expertise in the banking sector and AI implementation. Collaborating with experienced partners can accelerate project timelines, provide access to specialized knowledge, and mitigate risks associated with AI implementations.

Take an Iterative Approach

Take an iterative approach when implementing AI projects. Begin with small-scale pilots or proofs-of-concept to assess the feasibility and value of AI solutions. Gather feedback and insights from these initial implementations and use them to refine and improve the projects before scaling them up.

Continuous Monitoring and Evaluation

Implement mechanisms to regularly assess AI solutions’ accuracy, reliability, and effectiveness. Make necessary adjustments and improvements to ensure ongoing success.

Final Thoughts

Becoming an AI-first bank is no longer a choice but an imperative for US banks. Embracing an AI-first approach will enable banks to drive tangible benefits across their operations, customer experiences, and competitive positioning.

Posted on Oct-05-2021