Artificial intelligence is undergoing an evolution in the financial services space: from completely innovative “hype” to standard operating technology. Banks not currently exploring its many applications risk being left behind.
For now, artificial intelligence remains a competitive advantage at many institutions. But AI’s increasing adoption and deployment means institutions that are not currently investing and exploring its capabilities will eventually find themselves at a disadvantage when it comes to customer satisfaction, cost saves and productivity. For banks, AI is not an “if” — it’s a “when.”
AI has proven use cases within the bank and credit union space, offering a number of productivity and efficiency gains financial institutions are searching for in this low-return, low-growth environment. The leading drivers behind AI adoption today are improvements in customer experience and employee productivity, according to a 2020 report from International Data Corporation. At Microsoft, we’ve found several bank-specific applications where AI technology can make a meaningful impact.
One is a front-office applications that create personalized insights for customers by analyzing their transaction data to generate insights that improve their experience, like a charge from an airline triggering a prompt to create a travel notification or analyzing monthly spend to create an automated savings plan. Personalized prompts on a bank’s mobile or online platform can increase engagement by 40% and customer satisfaction by 37%; this can translate to a 15% increase in deposits. Additionally, digital assistants and chatbots can divert call center and web traffic while creating a better experience for customers. In some cases, digital assistants can also serve as an extension of a company’s brand, like a chatbot with the personality of “Flo” that auto insurer Progressive created to interact with customers on platforms like Facebook, chat and mobile.
Middle-office fraud and compliance monitoring are other areas that can benefit from AI applications. These applications and capabilities come at a crucial time, given the increased fraud activity around account takeovers and openings, along with synthetic identity forgery. AI applications can identify fraudsters by their initial interaction while reducing enrollment friction by 95% and false positives by 30%. In fact, IDC found that just four use cases — automated customer service agents, sales process recommendation and automation, automated threat intelligence and prevention and IT automation — made up almost a third of all AI spending in 2020.
There are several steps executives should focus on after deciding to implement AI technology. The first is on data quality: eliminating data silos helps to ensure a unified single view of the customer and drives highly relevant decisions and insights. Next, its critical to assemble a diverse, cross-functional team from multiple areas of the bank like technology, legal, lending and security, to explore AI’s potential to create a plan or framework for the bank. Teams need to be empowered to plan and communicate how to best leverage data and new technologies to drive the bank’s operations and products.
Once infrastructure is in place, banks can then focus on incorporating the insights AI generates into strategy and decision-making. Using the data to understand how customers are interacting, which products they’re using most, and which channels can be leveraged to further engage — unlocking an entire new capability to deliver business and productivity results
In all this, bank leadership and governance have an important role to play when adopting and implementing technology like AI. Incorporating AI is a cultural shift; executives should approach it with constant communication around AI’s usage, expectations, guiderails and expected outcomes. They must establish a clear set of governance guiderails for when, and if, AI is appropriate to perform certain functions.
One reason why individual banks may have held off exploring AI’s potential is concern about how it will impact current bank staff — maybe even replace them. Executives should “demystify AI” for staff by offering a clear, basic understanding of AI and practical uses within an employee’s work that will boost their productivity or decrease repetitive aspects of their jobs. Providing training that focuses on the transformational impact of the applications, and proactively creating new career paths for individuals whose roles may be negatively impacted by AI show commitment to employees, customers, and the financial institution.
It is critical that executives and managers are aligned in this mission: AI is not an “if” for banks, it is “when.” Banks that are committed to making their employees’ and customers’ lives better should seriously consider investing in AI capabilities and applications.