What financial institutions need to build a strong AI strategy
Three considerations in building a strong AI strategy
FINOVATEFALL SPECIAL: INDUSTRY INSIGHTS FROM FLYBITS
The buzz is rampant as advances in artificial intelligence are leading to more and more meaningful results. As artificial intelligence (AI) has shifted in recent years from theory to the realm of possibility, financial institutions are shifting their strategic priorities to incorporate AI capabilities into their tech stacks. This is unsurprising as AI holds vast promises for banks and credit unions to not only reach and segment customers better, but to provide experiences far superior than we’ve been accustomed to before. However, what many institutions fail to recognize is that AI doesn’t simply come as a piece of technology to be integrated, but rather requires thorough planning to ensure organizations can leverage its full impact.
Banks and credit unions today are in possession of an incredible amount of resources, data, and incentives to create effective AI implementations. However, organizations that acquire AI solutions often fail to put enough effort into learning which AI strategies are most valuable for their business and what challenges they pose. And, perhaps most importantly, financial institutions must know that without a comprehensive, enterprise-wide data strategy, they won’t be in a position to reap the competitive benefits of AI. To avoid such a fate, banks must consider the following:
1. Reimagine your company’s data
Data is central to feeding the predictive power of AI. So it’s important to shift your perspective on data to look at it as a source for clues rather than outcomes. For example, instead of looking to data to tell you how many 25-30 year olds with annual incomes of at least $60,000 and savings of $20,000 are applying for mortgages, banks can leverage AI to determine which customers have the greatest propensity to apply for a mortgage and proactively reach out with mortgage rates or even pre-approve them. Recognizing the insights AI can derive from data will help you see more clearly how your organization can use AI to achieve its goals.
2. Understand the value of microservices
It’s unrealistic to expect AI to work with any system you wish to integrate it into. Instead, when creating your AI strategy, consider adopting a microservices architecture. This containerized approach helps companies implement take on a system-agnostic approach, which will give your organization room to scale its AI needs.
3. Get organizational buy-in
Building an enterprise-wide data strategy to support, sustain, and drive AI initiatives requires involvement of the entire organization. Most traditional financial institutions have data siloed within departments and branches. This is why, when designing a data strategy, cross-functional teams must be involved. Doing so ensures all departments are aware of what is needed from them and are equally committed to ensuring success.
While there might be a lot of talk in the news about what organizations are doing, there’s no reason to rush your AI pursuits. Creating the right foundation is more valuable in the long run than implementing a tool that can’t be fully utilized. It can be critical here to partner with an AI vendor that can help you develop the infrastructure to successfully launch your AI efforts.
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