Consumers increasingly expect seamless, frictionless and relevant experiences from every business they interact with and their bank is no exception. In order for banks to keep up with changing consumer expectations — and growing competition from digital-only challengers — they need to embrace new technologies, such as artificial intelligence (AI), that will help them evolve.
A full 77% of bankers believe that “unlocking value from AI will be the differentiator between winning and losing banks.” Banks have an opportunity to not only improve customer experience with AI, but also enhance operational efficiency and reduce losses by incorporating AI into how they do business. Seizing that opportunity is essential.
“With certain players — from challenger start-ups to the largest global banks — taking steps to adopt AI technology, banks without an implementation strategy could find themselves losing market share,” says Vivek Vinod, Senior Vice President, Decision Analytics at EXL.
High-value AI applications
Banks adopting AI technology need to assess which manual processes they want to automate and prioritize the AI use cases that will deliver the most benefit to their business. For some banks, risk assessment and management is a high-impact area for AI.
“AI has the ability to process massive amounts of data and spot trends at a level of detail that would be very hard to humans to even get to,” says Vinod. “From a risk management perspective, that makes it valuable to determine the probability of default at underwriting — at the time when someone is applying for a loan — and also for ongoing risk assessment based on what the customer is doing internally and externally.”
The precision with which a bank both assesses risk at the customer or segment level, and responds to it, can ultimately set that bank apart from competitors. Using AI can drive up that precision, helping banks better serve customers by making more informed loan and credit decisions based on highly accurate, real-time information.
AI is also helping banks extract insights and relevant information from large volumes of data and from unstructured data. For example, some banks are working with EXL to use AI to extract insights from large volumes of customer communication data from sources such as audio files, virtual chats, emails and even handwritten notes.
Sentiment and complaint insights are some of the most important information banks can cultivate from customer communication. It’s imperative for banks to listen to their customers, and respond to what they learn and AI can help them do that in three key ways.
“One is cracking exactly what the reason of complaint is — being very specific — so that the action of the complaint can also be very specific to the type of complaint,” says Shantanu Paliwal, Vice President at EXL. “The second is ensuring the appropriate closure of the complaint. Are the reps actually talking to the customers, replying on time and addressing the point of the complaint? And then third is extracting more insights from the complaint so that we can use complaints as a feedback loop to improve the root cause of problems and reduce any future such complaints.”
AI is not only capable of gleaning insights from customer interactions, it’s also capable of handling them altogether. Customer interactions are a growing opportunity area for generative AI in banking (with 66% of financial services executives reporting that their organization is likely to use the technology to enable more sophisticated chatbots and virtual assistants). Generative AI can also be used in fraud prevention and detection and reporting applications, among many other use cases.
Achieving data readiness
Banks may be eager to capitalize on AI capabilities across the business, but rushed implementation should be avoided. In order to see the greatest impact from generative AI and other forms of artificial intelligence, banks need a strong data infrastructure and sound enterprise-wide data management practices.
“Banks must design, normalize and build a high-quality, connected data infrastructure leveraging internal data — both structured and unstructured — and third-party data for use in AI applications,” says Vinod.
Several areas are especially key to ensuring a strong data foundation. The right technology partner can help banks focus on developing the scalable, end-to-end data architecture, robust data engineering capabilities and holistic data governance practices necessary to properly manage data for AI. Ongoing employee training also helps ensure AI-powered solutions are used to full effect.
Now is the time
Ultimately, banks that have yet to put an AI roadmap into action are not only missing out on the benefits AI can deliver, but also falling behind competitors who have already taken steps to implement it. But with the AI landscape changing rapidly, banks need the help of trusted partners if they want to put AI to the best possible uses for the future of their business.
