Did you know that you can now perform banking transactions and open accounts while cracking jokes with AI robots and sharing live selfies for verification? These examples are not taken out of a sci-fi movie but are real-life instances of the future of AI in banking. In one instance, NatWest bank’s AI for biometric verification used selfies and device locations for KYC verification. In another, HSBC bank used “Pepper”, the humanoid robot, to greet and guide customers in their branches across the US.
Several banks are quickly adapting to changing trends and advancements in the field of customer management, leaving behind their traditional methods. Today, the future of AI in banking is not merely limited to chatbots, virtual assistants, and other customer-directed actions. Moving beyond these, future of AI reasoning is equally utilized in the bank’s back office, ensuring safety in onboarding customers and preventing money laundering scenarios. The introduction of AI in banking has helped drop thousands of fraudulent account openings and unauthorized transactions at a time when almost 5% of the GDP went under money laundering.
A traditional bank’s fraud detection system is normally set to detect anomalies from a certain amount of money and above. But with AI, every transaction conducted through the bank is under surveillance. Therefore, no fraudulent transaction, no matter how small they might be, will go undetected.
While banking with Gen Z, the shortened attention span of the customer needs to be considered to create the bank’s perfect engagement platform. Therefore, a trip to the bank or a prolonged review process for account creation might appear as a red flag. Handing over the onboarding processes completely to AI can make the process more appealing to the bank’s future customers. This way, AI can report anomalies found among the thousands of transactions and verification processes to the manager and keep the workflow seamless. As an additional perk, the chances of false alarms and unnecessary blocking of checking accounts and credit/debit cards can be prevented.
For example, the AI developed by Ayasdi to prevent money laundering in banking has been helping Scotiabank and Intesa Sanpaolo in dropping false alerts. Pursuing the future of AI in banking has, therefore, saved the banks millions of dollars required to investigate and monitor money laundering.


