The global pandemic has vastly accelerated an existing trend for financial services—the drive toward digital transformation to meet customer expectations. Today’s financial services firms need to be digital-first and always on, providing a smooth, personalized, and innovative customer experience. Artificial intelligence and machine learning are the tools that can make this change happen.
Digital transformation has been gathering pace in every industry, but no one expected the jolt that occurred during the global pandemic. With stay-at-home orders proliferating in countries across the world, virtual healthcare, digital financial services, and more (and better) e-commerce services suddenly had to come online—fast.
Although this acceleration in digitization happened during a crisis, customers won’t be satisfied with a return to the status quo. Just as they now expect to be able to talk to their health provider and order medications online and in person, they also expect seamless omnichannel experiences from financial services firms. Customers want rich, personalized experiences that tap into their preferences, habits, demographics, and financial history. They’re also looking for a digital-first approach that allows them to open new accounts, browse and understand products, and get recommendations and access to new apps—online or on their smartphones.
Even before the pandemic, traditional financial firms had seen gaps in their customer experience. These gaps were revealed by disruptive startups, which were able to move nimbly and offer new, data-driven services. Innovative apps were helping people save toward their goals—for example, by automatically “rounding up” on purchases and putting the extra pennies into savings accounts. Other apps could help budgeting by putting purchases into categories, showing customers how much they were spending each month on groceries, holidays, or clothes.
This type of deeper customer understanding can’t be accomplished with legacy systems and processes. It requires harnessing the power of customer data with artificial intelligence (AI) and machine learning algorithms.
AI isn’t a new concept for financial firms. Many banks enthusiastically embraced AI in the form of chatbots, to help with direct customer service. But AI can enable so much more functionality across financial services, and so far, the industry has been playing catch-up.
According to a Capgemini report, more than half of financial services firms had more than 40% of their customer interactions AI-enabled in late 2020. But 3 years before, it was just one in every 100. Even with this rapid acceleration, many firms are still falling short. In the same study, one in two banking and insurance customers felt that the value they derived out of their AI-based interactions was nonexistent or less than what they expected.
To really harness the power of AI, financial services firms need to look beyond the chatbot to the ways that algorithms can enable smoother transactions. For example, AI can help with “know your customer” checks when clients are first onboarded. This process is usually laborious—often still requiring the customer to show up at some point with their ID in hand. But machine learning can be used to verify identity, with the customer’s permission, by recognizing a virtual copy of the ID and then matching it to a picture or video of the customer.
For backroom processing, AI and machine learning can be used to help populate application forms and customer records. This can speed up the process so that the customer gets set up with a new loan or credit card faster and can also help the financial firm with compliance.
Behind the scenes, AI can help identify and stop fraudulent transactions. Banks today deploy machine learning models that can detect suspicious transactions, often almost as they happen, and stop them from completing. Before this generation of learning algorithms, banks commonly tried to identify fraudulent transactions through rule-based programs, which frequently returned false positives and negatives. This could result in both legitimate transactions being held up and fraudulent ones avoiding detection.
We’ve already seen how AI can enable deeper personalization through budgeting and savings applications. But there’s much more that can be achieved. After customers have granted access to their data, financial services firms will be able to offer them the products they need, right when they need them.
A customer who has just bought a house, for example, could be sent information about homeowners insurance. Customers who have started regularly saving money could be offered new rates, or even investment advice. This type of personalization has benefits for both the bank and customers. The bank gets its services to those who might want them, and customers gain access to services when they’re ready to engage.
Customers are getting more personalized services and applications in many areas of their lives, and they increasingly expect the digital-first, always-on service of internet firms like Amazon and Google in more traditional arenas. Traditional financial services firms that fail to meet these expectations are likely to be left behind.
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Steve Rackham leads the technical pre-sales team for Strategic Enterprise FSI accounts across EMEA. Steve began his career in technology working for Sequent Computers, spending time at Intel and StorageTek. He joined NetApp in 2006 taking a role in pre-sales. For the past 13 years he has been involved with customers working on accounts across the FSI vertical including heading up a Global pre-sales team for a large, multi-national bank before moving to his current leadership role. He has been enhancing relationships with customers and strategic partners alike, helping them adopt their own Data Fabric utilizing NetApp’s Data Management solutions. Steve has also been exploring how the rapid advances in the use of AI is impacting organizations.
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