112 FEATURE reached a critical stage where banks are trying to understand how they can more broadly use generative AI and track its benefits.” As organisations explore these possibilities, they’re finding that generative AI is no longer limited to straightforward productivity enhancements but has the potential to transform other areas of work. “While productivity, research and content creation are some of the more common AI use cases in banking, we are also starting to see a wide variety of other functionalities including fraud detection, product, risk and marketing,” says Hamblin. Firms can leverage the capabilities of generative AI to enhance decision-making and streamline processes, enabling them to go beyond productivity use cases where the lowhanging fruit is. “Banks should look beyond the traditional apps of generative AI by using it more dynamically to generate products and offers tailored to individual customers,” says Hamblin. “For example, most banks offer 30, 20 and 15-year mortgages but what if a customer needs a 13 or 22-year mortgage due to personal circumstances or specific financial goals?” Banks could also use generative AI to facilitate dynamic payments and provide customers with a payment mechanism that best matches their individual financial needs. For example, different types of payments – like buy now pay later or credit and debit cards – could be offered to customers under one platform. “In a perfect world, you could even change the terms and conditions based on a person’s financial profile,” says Hamblin. “Customer A might have good credit while customer B may have bad credit, so banks could provide each of them with different terms and conditions.” Another potential use case for generative AI is creating a financial education tool for customers to better understand wealth management, adds Hamblin. “Intelligent bots can be trained with data from wealth management to improve financial literacy. They will be able to break down information for the general public to understand their financial opportunities.” There is a lot of opportunity with fraud prevention and detection too. “Current fraud systems use machine learning and predictive analytics, but generative AI can help to more accurately identify fraudulent patterns and trends, then use that intelligence to create models offering a higher prevention rate,” explains Hamblin. To capitalise on the opportunities created by generative AI, the financial services industry must first overcome its biggest challenges: security, compliance and governance. “A lot of banks are still trying to understand how they can tap into the power of generative AI but still protect themselves from a security and compliance standpoint.” While Hamblin acknowledges these challenges, he believes the opportunities for AI adoption outweigh the burden of overcoming security and compliance regulations. “There’s tremendous opportunity with generative AI,” he says. “Banks can offer more relevant products and upgrade their key processes and we’ll see generative AI being used across various departments, from marketing to compliance and risk. “Over the next two years, the technology will mature, improving its reasoning and decision-making capabilities. We’ll also see different type of AI technologies merging. Whether that’s robotic processes, predictive analytics, machine learning, or even metaverse technology, they will all integrate with generative AI for more focused use cases and business scenarios like creating best-offer products, case resolutions and renewals.” “ Everyone is very excited about generative AI but it’s still very much a nascent technology”
RkJQdWJsaXNoZXIy NzQ1NTk=