The future of AI in banking has been saved
The future of AI in banking
From experimentation to full-scale deployment
To reap the full benefits of new artificial intelligence and machine learning technologies, banks must move beyond the hype and consider the practical applications of AI. Discover use cases for mainstream deployment of AI in banking and how to enable successful implementation.
The importance of AI in banking
It would be an understatement to suggest that artificial intelligence (AI) and machine learning (ML) in banking are transformative technologies. According to a recent Deloitte survey of IT and line-of-business executives, 86% of financial services AI adopters say that AI will be very or critically important to their business’s success in the next two years. So, what should banks do to keep current with AI marketplace trends and build with confidence into the future?
While the banking sector has long been technology-dependent and data-intensive, new data-enabled AI technology has the capability to drive innovation further and faster than ever before. AI can help improve efficiency, enable a growth agenda, boost differentiation, manage risk and regulatory needs, and positively influence customer experience. Building sophisticated AI systems was once expensive, restricting deployment to key use cases (e.g., high-frequency trading). Deloitte’s recent AI survey of IT and line-of-business executives of companies that have adopted AI technologies found that, from a technology perspective, cost and other barriers to adoption are falling, and it is becoming easier to implement and integrate AI technologies.
Organizations are making targeted investments in areas such as cloud, big data platforms, and data applications that use updated architecture (e.g., microservices and event hubs), eliminating up-front capital investment needed specifically to develop, deploy, and scale AI solutions. However, multiple operational and organizational challenges remain, notably skills gaps and the integration of AI into the wider organization, to name two examples.
86% of financial services AI adopters say that AI will be very or critically important to their business’s success in the next two years.
– Deloitte survey of IT and line-of-business executives
Banking reimagined with AI
As banks consider the pros and cons of a broader enterprise AI strategy, use cases can be instructive in decision-making. By focusing on use cases like the ones that follow, executives can make informed decisions that can help tailor deployments to their circumstances, yielding a better return on investment. While these examples are by no means exhaustive, they demonstrate that data-driven AI can be used in many ways to generate additional value across a banking organization—from front-office revenue growth to back-office operational efficiencies.
Shifting to full-scale AI implementation in banking
Much like the evolution of cloud platforms in recent years, banks must move beyond the hype and consider the practical applications of AI. While there are proven examples of effective applications, many banks still consider AI to be experimental, with many of their pilot programs never moving into full-scale implementation. Banks must consider their artificial intelligence and machine learning approach and invest in an AI implementation journey for successful outcomes. Here are critical focus areas, across six steps, where banks may need to evolve their processes to be successful on their journey:
An AI-enabled future
The growing adoption of AI promises to have a lasting impact on the banking industry. Even though banks must still overcome significant operational and organizational challenges, they are making great strides forward in implementation and adoption. To realize the full benefits of AI, banks must stay the course today and continue to build the technological foundations and processes necessary to move forward into the future.