Banks and financial institutions are making strides with artificial intelligence – but they’ve been slow to scale it. Here are four steps to realize AI’s full potential throughout the enterprise.
Generating ROI from AI is a slow-burning process. With an average payback period of 17 months across industries, it clearly takes time to identify the appropriate business case, acquire and prepare the right data, and then build, test, refine and deploy working models.
In research that we concluded in September 2020, we found FIs to be making strides in AI, even making up 31% of AI leaders. However, much more progress must be made before AI delivers greater value across the banking space.
It requires approaching AI as a business play rather than a technology challenge. To ensure your organization develops successful AI strategies that scale, we recommend the following steps:
- Identify use cases that are universal yet well bounded. For FIs, developing AI expertise starts with deploying the technology on common business problems that provide broad benefits across the organization. Spotting AI’s potential for universal uses requires the formation of an enterprise-wide group tasked with identifying use cases. So far, few FIs have established such a group.
- Beef up data management. FIs’ IT systems aren’t equipped to quickly deploy and provision the data and platforms that AI applications depend on. Typically, FIs lack the data management capabilities to gather and analyze the data in conjunction with performance metrics from other systems. Solving the data management challenge requires developing an intelligent tagging strategy and implementing a multi-cloud platform strategy.
- Move beyond experimentation. AI’s adoption path is different from other digital technologies; its success hinges on a steady stream of data. How can FIs overcome the fragmented ownership and control of data sources? The answer must include expanding the data supply chain and inviting the organization at large to ideate for AI.
- Mitigate unintended consequences by creating responsible applications. An important step is to build a solid ethical foundation up front instead of as an afterthought. This includes establishing processes throughout application design and management to identify, expose and overcome bias in analytics. Also important is overlaying a code of ethics on ML systems. Another critical step is monitoring AI applications over time for hidden biases.
By approaching AI as a business play, banks can create successful AI strategies that lead to production and scale. Learn more in the full report Close the AI Action Gap in Financial Services.