Why banks must accelerate their adoption of advanced analytics and AI

Now is the time for financial institutions (FIs) to accelerate their adoption of advanced analytics and AI, or risk being left behind. It's that simple.

FIs that are ahead of the curve in AI adoption are able to leverage their data to meet customer expectations, innovate, offer new products and generate new revenue streams. By some estimates, the potential annual value of advanced analytics & AI adoption in global banking is $1 trillion.??

With advanced analytics, FIs gain a holistic view of their customers, making them customer-centric rather than product-centric. After analyzing customer data, such as their existing financial products, purchase history and household members, institutions can build more accurate views of who their customers are and what motivates their choices. In turn, this data provides more accurate insights into what customers really need. Banks can make informed product suggestions rather than broad recommendations based on demographics or customer segmentation, as has been the case without AI.?

With AI, personalization decisions become embedded

Analytics can mean the difference between receiving investment account option recommendations that a particular customer is really interested in versus home loan offers that are not relevant to them at that point in time. When data is aggregated from multiple banking channels in this way, it can help institutions be more effective with their sales and marketing strategies. Personalization decisions become embedded in the customer journey, ensuring that the right service or product is offered to the right customer at the right time and on the right channel, ultimately delivering more value.? These hyper-personalized customer journeys can become embedded in partner ecosystems allowing them to take advantage of a partner’s data and channels to increase engagement and usage.?

AI can identify risks and vulnerabilities

Another avenue that advanced analytics can be applied in the customer journey is through churn analytics. Using this data, it’s possible to identify weak spots in the system and institution that cause customer churn. Churn analytics provide a basis to test theories on where there’s room for improvement and which customers are at high risk for loss. This information can then be used strategically to develop counteractive strategies that ensure long-term retention.?

There is also the matter of risk mitigation - banks use data analytics to protect not only their customers’ interests but also themselves from potential risk. Here, analytics can be applied to segment groups based on creditworthiness for improved credit risk management. In turn, institutions can narrow down suitable credit products for individuals, reducing default risk. Similarly, analyzing behavioral patterns can raise red flags to fraud. Any suspect deviations from regular banking practices such as irregular log-ins can notify customers to take steps to protect their accounts.?

AI is the competitive edge banks need

AI-first banks are customer-centric and offer experiences that are insightful, hyper-personalized and omni-channel. With advanced analytics and AI, banks can collaborate with partners to deliver new value propositions that are embedded within their customer journeys.

They can become more than a bank.