Among the plethora of technologies taking root in today’s retail banking sector, two of the most exciting are artificial intelligence (AI), or the capability of machines to simulate human thinking, and the branch of AI called machine learning (ML). An increasing number of banks have gone further than implementing digitized or automated solutions and have invested in AI and ML for key aspects like customer enrollment, loan application, customer service, and financial crime and compliance management.
Now that banks and their customers are several years into the digital age, it makes sense that AI, ML, and related technologies like artificial neural networks and deep learning have made an impact in the present. But how will these technologies continue to shape the future of customer experiences, and what’s in store for the retail banks that choose to embrace them?
At a glance, AI and ML in retail banking solutions have the potential to enhance the speed, accuracy, and efficiency of banking processes in ways that financial institutions have yet to imagine. They’ll also unlock new possibilities for “phygital” or hybrid “physical-digital” customer experiences, which seamlessly mesh the contributions of human agents and digital interfaces for complete front-to-end service. To illustrate further, here’s an overview of retail banking aspects that AI and ML are due to permanently transform:
Faster Digital Onboarding of Customers
The concept of digital onboarding is not necessarily new to today’s generation of customers, given that many would already choose to open new accounts online versus over the counter. But in the future of retail banking, new customers can expect onboarding to be even faster, more streamlined, more secure, and more personalized thanks to AI and ML technologies.
For example, a retail bank’s AI-driven solution can quickly process pertinent information about the customer’s preferences and current life stage, making it easier to advertise specific products and services during the customer’s introduction to the banking ecosystem. AI can also be used to implement quick but efficient verification processes to validate new customers’ identities. In terms of banking applications that will soon be driven by AI and ML, digital onboarding is one of the most obvious ones.
Immediate Origination Processes
AI and ML may also soon be utilized for origination processes. These are the multi-step processes that customers have to go through when they apply for home loans or mortgages.
Loan applications have traditionally been driven by manual processes since human staff must exercise judgment on matters like loan eligibility. Soon, however, AI and ML may help in ascribing eligibility and improve turnovers for loan applications through intelligent pre-qualification.
Automated Customer Assistance
Before, the assumption was that customers would only want to talk to human agents for their concerns. But many banks that have tried a combination of human and machine-driven customer assistance have realized that this may not necessarily be the case. It may not always be a human presence that customers are looking for per se; rather, most are looking for immediate answers and a clear sense of direction for the resolution of their problems.
In the future, more banks may opt for a hybrid customer service arrangement in which chatbots provide real-time customer assistance for simple matters while more complex concerns are escalated to human agents. With this arrangement, customers can be assured of consistent support from the bank at every step of their journey.
Intelligent Fraud Detection and Know Your Customer (KYC) Processes
ML tools may soon be at the forefront of retail banks’ know your customer (KYC), transaction monitoring, and anti-money laundering (AML) processes. Intelligent solutions that are capable of “learning” about illicit trends can be quickly trained to detect the distinct patterns and conditions that indicate fraudulent behavior.
Such technologies will do a lot to help a retail bank gain a fully consolidated view of their transactions and see if either individual customers or clusters of customers are engaging in anomalous behavior within the bank’s system. This, in turn, will go a long way in protecting the bank’s assets from malicious agents, like money launderers and terrorist backers, as well as safeguarding the interests of legitimate customers.
Intelligent Credit Decisioning
Lastly, AI and ML may play a more prominent role in retail banks’ credit assessment processes and their assignment of proportionate credit ratings to customers. For example, neural network technologies can be used to process huge volumes of credit-related data and to learn about customers’ credit risk based on documents like bank statements and pay slips.
As a result, banks will be able to roll out intelligent credit decisioning and avoid serious losses due to unmitigated credit risk. Customers will also be appraised and rewarded for doing well with their credit.
It’s important to remember that AI and ML technologies are not meant to work in isolation or completely replace human intelligence for financial applications. A customer-focused industry like retail banking still requires the judgment and fine-tuning abilities of human overseers who are better equipped to identify human factors like intent.
As you can see from the insights above, there’s significant potential for customer experiences to improve when AI, ML, and other technologies in the same family are combined with human ability. In time, all these may significantly change the way we do retail banking in the future.
