AI’s true value in banking

ChatGPT may have sparked the initial buzz around generative artificial intelligence just a little over a year ago, but plenty of businesses have already either made it through or are approaching the implementation stage with AI tools. Global spending on AI is expected to top $500 billion in the next two years, according to International Data Corp., which identified banking as one of the top industries likely to garner investment.

For financial institutions, AI holds significant potential impact and opportunity in two areas: sales and marketing, and risk management.

Customer service management is likely the area within the sales and marketing function where AI will be able to provide the most value to institutions.

Take, for instance, Ally Bank, which made waves in 2023 by creating cloud-based platform Ally.ai. The first major product the organization derived from this initiative was directly related to customer service. Call center representatives at institutions are required to take notes during customer calls and then create a quick summary once the call is complete. Ally uses its AI technology to transcribe the conversation in real time, allowing the customer service rep to be more efficient and provide more accurate records—a critical point, given that accurate notes are required for regulatory compliance.

Beyond enhancing transcribing and note taking, AI can help use real-time voice recognition algorithms to redirect calls from distressed customers to experienced handlers. It can also optimize call center capacity planning by managing call volumes and predicting average handling times. Finally, the technology can increase customer satisfaction and reduce handling costs for institutions by routing requests to call centers based on multimodal data.

Channel management, pricing and promotion, marketing budget allocation, churn reduction, personalized offerings and lead generation are other key areas that AI can improve.

Another area in which AI may add considerable value is the risk department. AI has become a critical tool in efforts to detect and prevent fraud as well as money laundering schemes.

One multinational bank developed an AI platform that encompasses a range of tools and services. Within its payments division, the bank deployed a machine learning model trained on internal data to identify transactions that would be considered an anomaly. The transaction is then routed to an employee to determine the true nature of the transaction. This machine learning model has helped the institution prevent and detect many fraudulent transactions. 

In addition to identifying potentially fraudulent activity, AI can enhance debt analytics. Determining accurate reserves for defaults has consistently been a largely subjective task for many banks. By deploying the appropriate models and generative AI tools, institutions can create an early warning system that more accurately predicts defaults.