As AI (artificial intelligence—machine intelligence that mimics human cognitive abilities such as problem-solving and learning) increasingly becomes a part of our daily lives, banks are discovering how AI can improve the customer experience and boost operational efficiency. Some banks may shy away from a technology that seems too big to grasp, but banks can apply AI discretely, to specific use cases, and still see significant impact.
Use Case 1: Customer Service
Most of us have seen what AI can do with voice-activated technology via Siri and Alexa. As these technologies demonstrate, AI uses algorithms to perform human-like tasks. Together with machine-learning capabilities like natural language processing (NLP) and natural language generation (NLG), AI systems can analyze data to make real-time decisions—making AI a perfect candidate for customer service applications.
That’s why some banks are now using AI to route customer calls to the proper queue based on customer data like call history, product set, etc. Similarly, many banks have deployed AI-enabled chatbots. Based on machine-learning algorithms, chatbots mimic how customer service agents diagnose and solve customer problems. Chatbots can help customers make payments, check their balances, and transfer funds. They can even suggest products to customers based on their spending habits.
When chatbots work like they are supposed to, customers’ inquiries are resolved faster and better—for an improved customer experience. An inferior chatbot, though, can result in a frustrated customer, so choosing the right vendor—often a fintech specializing in AI—is essential.
More Use Cases: Fraud Detection and Risk Mitigation
Another use case for AI is fraud mitigation and detection. AI can be used, for example, to build holistic customer views that allow banks to better detect suspicious behavior. It can also be applied to risk activities, like scanning and evaluating physical documents (e.g., loan applications) for compliance data. Additionally, AI can be used to analyze a bank’s past compliance issues and to detect similar patterns in a bank’s current data sets. It can then alert the compliance team in real time.
In these use cases, AI does not necessitate a reduction in head count; rather, it allows employees to spend less time sifting through piles of data and more time making better, faster, more accurate decisions.
AI as a Differentiator
AI probably won’t become widespread at financial institutions for seven to ten years, and a lot will happen to evolve the technology in that time. Forward-thinking banks, though, are recognizing that the predictive modeling enabled by AI can differentiate them from competitors by providing a superior customer experience from end to end. Using a vast trove of data to build a holistic view of the customer and his/her relationship with the bank, predictive modeling allows banks to stay ahead of customers, to know what they need before they themselves know it. Banks can then help those customers to find the products they need, and to steer clear of obstacles and issues before they happen.
AI’s potential can be overwhelming, but banks and credit unions can take it on in small steps. By applying AI to discrete use cases, financial institutions can learn much about AI’s capabilities while also reducing costs and gaining operational efficiencies. Then, over time, AI can be extended to bigger, better things that can ultimately set a bank apart from its peers.