Speech recognition. Image recognition. Gesture recognition. Recognize these terms?
Along with others such as machine learning and robotic process automation—once little more than alien jargon—these concepts have entered the traditional banking business lexicon. Taken together, they’re more or less subsets of one overarching term: artificial intelligence (AI).
And with AI, the banking industry is now pressured to dive into the data pile it’s sitting on to harvest meaningful insights about existing and prospective customers. Yet the dive has its rewards. For banks—especially retail banks—AI can analyze customer data sets to find out the following:
How they are interrelated
How to consider different data point combinations
How to interpret data to make meaningful inferences that can serve the customer better; and
How to reduce anomalies, improve operational efficiency and increase business by adding new customers.
In retail banking, AI adoption is reaching a more mature state as it moves to banks worldwide. Royal Bank of Scotland, Swedbank and Bank of America are some of the early adopters of AI-based technology to serve retail banking customers. But from the industry standpoint, this marks but the starting point. The reason: Adoption of such technology is essentially confined to the tier-1 banks. The greater transformation will occur over the next few years as more banks take up AI-based transformations. While retail banks experience the transformation journey, it is prudent to highlight some crucial questions to consider:
Adopt to what extent? AI adoption, of course, requires a lot of investment. Retail banks need to carefully evaluate the extent and areas of adoption versus the possible benefits.
What is the data quality? Banks must determine whether their data is worth applying to AI analysis methods. If not, they need to first work towards cleansing the data and making it more structured. This way, they can apply AI methods to reap the full benefits of AI.
How do the solution offerings feel? It can prove challenging to get existing customers to adopt AI-based service offerings; providing only the technology may not suffice. Customers should find the interaction enjoyable and satisfactory—and not feel as though they’re only talking to a machine that fails to acknowledge their expressions. Hence, integration of human emotions to machine intelligence represents a hurdle.
Can you personalize the offerings? One important goal of implementing AI in retail banking is to personalize product or solution offerings for customers. Yet different customers may react to the very same situation in different ways. The challenge rests with how granular the personalization can be.
Is there a perceived internal threat from automation? Automation of processes may make some human effort redundant. It will help to undertake careful planning, on behalf of potentially affected employees, to upgrade their skills to some other function. Otherwise your workforce will consider the AI adoption process employee unfriendly—creating a negative impact.
AI promises to provide a very powerful tool to retail banks. It optimizes efficiency, reduces fraud and serves customers more efficiently. It solves anomalies sooner, reduces human errors and improves customer relationships and loyalty. But that said, banks must identify and implement the best AI technologies to leverage their maximum transformative capabilities—while advancing the institution’s strategy.
If adopted and implemented properly, AI can change the retail banking scenario dramatically. It comes down to banks’ able leaders and their strategic decisions to absorb the shock of any changes customers and employees might face.
Meanwhile, there is a broader stage to consider. On one hand, we debate the success of AI-based transformation; on the other, the banking industry deals with financial inclusion issues such as bringing the world’s unbanked population into an inclusive system. Here again, AI has a big role to play.
To be sure, AI-based automations—and thus transformations—will dominate the industry for years to come. As the future comes to fruition, hassles will decrease in number and scope. At this moment, banks stand in the balance.
And as they adopt artificial intelligence, the banking leaders of tomorrow will stand out by applying their very real intelligence today.
Satya Swarup Das is a senior solution architect with VirtusaPolaris. Aretail bankingconsultant, he has more than 12 years experience in the business and IT sides. He has worked for different products and multiple clients across the world in the retail banking transformations space.
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