Three Ways AI will Drive Growth at Retail Banks

In the retail banking tech circles, artificial intelligence (AI) is one of the hottest topics for discussions. Barely a conversation with a CTO, CIO, or CMO occurs these days without mentioning the potential benefits of AI in the banking industry. And for good reason. The benefits AI can produce is staggering. 

According to a recent report AI can help banks save over 22% of operating costs. Little surprise that the expanding range of AI applications and opportunities continues to reshape the banking industry, especially at retail banks.

While AI is evolving to take up a bigger role in transforming retail banking, it is still in the early stages of AI adoption at most of the banks. In fact, another survey reveals that fewer than 20% of executives from leading financial institutions feel well-prepared for the future. 

Everyone is talking about AI, but many aren’t prepared to use it. This can be a dangerous position to be in. 

Given the rapid tech advancements, the concern for retail banks is to find ways to integrate new capabilities into their operations and strategies—and generate ROI on AI investments. Here are three ways AI can contribute to banks’ business growth and innovation.

1.    Accelerate customer acquisition rate: 

Increasing customer acquisition rate, especially pertaining to millennials, is one of the primary challenges being faced by today’s banks. New entrants such as challenger banks and BigTech players are rapidly capturing the market with their millennial-driven propositions, customized to suit the digital lifestyle of customers.

In such a competitive marketplace, banks can expand their customer base and increase their profits by boosting purchase rate with better segmentation and targeting. AI can help banks to:

• Leverage digital lifestyle data of the customer and target them with the right banking product offer at the right moment. For instance, customers may want to receive notifications only during holidays or every time their salaries get credited. Further, a close look at their spending patterns can also help in predicting customers’ expectations.
  
  
• Optimize relationships with customers with customized, dynamic pricing. Factors such as customers’ spending habits, social network frequency of spend, default history, referral potential etc., should reflect while pricing for a customer.
  
  
• Expand target audience and connect better with alternate data-based credit risk scoring. Products such as lending can support credit risk decision making and help banks identify more prospects. 

2. Leverage AI-powered selling for added revenue generation 

To stay relevant in today’s competitive market, banks need to put their customers in the center stage, or risk being left behind. The rule applies even in the case of up-selling and cross-selling–a strategy all banks seem to be interested in. Banks must know their customers, anticipate their needs, and engage with them where they are. Based on the demographics, financial standing, and product history, AI has the potential to offer significant insights for driving predictive recommendations around product offerings for existing customers, thereby enabling banks to recommend relevant offers to their customers at the right time. 

• Deposit growth leveraging AI-powered selling strategy
  
  Deposit growth is essential for banks to fund loans for their customers. Recent FinTech disruptions are making a shift in the deposits landscape by promising better yields on their funds when compared to deposits maintained at a single bank.
  
  Banks can still gain a competitive edge by capitalizing on their existing customer’s data and engaging with them in a meaningful manner. It can be as simple as educating customers on financial health and advising them on better utilization of their funds. In the wake of PSD2 and Open Banking, such experiences will gain momentum wherein customers may allow banks and third-parties access to their banking accounts/transactions, which will be a golden opportunity for banks to position right products at the right moment (of course, while complying with regulations like GDPR).

• Loan product to mass affluent customers:
  
  Even though the affluent market only makes up a fraction of the customer base, they generate majority of total retail profits. Clearly, this is the most attractive and profitable market for banks to compete for. Banks must capitalize on AI to allure this tech-savvy wealthy segment to increase their profits. AI would enable banks to predict right moment in customers’ life along with right loan product for mass affluent customers.
  
  It wasn’t much of a surprise when Goldman Sachs launched a digital lending service for affluent customers of other wealth management firms and brokerages.

3. Drive the share of wallet with increased customer activities 

A report by CNBC suggests that only 23% of customers are happy with their banks. It implies that banks need to step up and be more than just debit and credit card providers. They need to provide solutions and guidance in accordance with customers’ expectations and their lifestyle to delight them, boost the level of engagement and snag more wallet share. Here AI can help banks to: 

• Engage in a meaningful conversation by understanding and predicting customers’ needs to boost purchase rate.
• Deliver personalized smart coupons/offers to customers at right location at right time to increase share of transactions on bank’s product.

Today, emerging technologies and changing customer expectations are radically altering banking business models. Only those banks who are willing adapt, innovate, and transform themselves for future will rise to the top. Banks not willing to find ways to embrace AI will find themselves struggling to catch up to nimble competitors. 

Anil Awasthi heads the retail banking practice at Virtusa where he is responsible for capability enhancement, solutioning, building best practices and thought leadership.