AI is taking off in development, providing a huge boost for many industries. And, of course, as a big player in the financial sector, it’s no surprise that banking is adopting AI at a higher rate. With the help of AI, a new secret sauce for customer service has emerged, giving financial institutions a more personalized approach to serving customers.

In this article, we discuss how and when banks can harness AI to improve customer service.

AI to Transform the Customer Experience in Banking

Often, the customer has to choose between trust, convenience, and personalization. Bank customer engagement has traditionally been about trust and convenience. But, by using AI to better understand user intent, preferences, and needs, banks can provide much more personalized experiences without compromising trust.

Today, more and more banks are embracing AI solutions. Many financial services companies report that they have implemented the technology into their business practices, like risk management and revenue-generating processes. Around 80% of the surveyed banks are aware of the potential benefits they can accrue with AI and ML.

Ways Banks Can Use AI to Better Serve Their Customers

The most productive AI methods are predictive analytics, making banking and AI

the perfect tandem. Predictive analytics often includes trends, historical data, and adaptive decisions to anticipate customer behavior. Interpreting these patterns and trends can help banks optimize customer service and efficiency.

Personalize the Customer Banking Experiences

The banking industry faces another set of business challenges and opportunities, one being the need to confront customer expectations around the customer experience. Globally,  executives are trying to enhance relationships with customers who want to be linked to their digital identities and embrace customer-centric business models. Companies like BBVA and U.S. Bancorp, who are using their leverage to offer digital banking solutions that are smarter and not just more intuitive, are setting examples for banks, while also redefining the customer experience.

Many central banks (Ally, Royal Bank of Canada, DBS) have already adopted AI to deliver tailor-made experiences for each customer during online and mobile banking. AI is also able to map narrative variations on a user’s activity to identify better alerts, suggest deals, and embed narratives for online banking to identify a customer’s specific needs. We expect to see more banks adopt the technology to become more time-efficient.

Handle Process-Oriented Work

Undergoing changes in operations due to digital technologies and disruptive startups, banks need to find a way to manage employee workload. Traditional methods are insufficient for carrying out a large number of tasks per day that involve both operational work and consultancy. This explains the high demand for AI that can automate banking processes such as loan approval and receipt processing, which reduces costs and helps make more out of every transaction.

Answer Customers’ FAQs

Technologies such as machine learning, cognitive reasoning, natural language processing, and conversational systems are at the framework level of the banking enterprise. The goal is that these technologies will reduce customer frustration, increase customer satisfaction, and improve service delivery. For example, AI-run solutions can provide customers 24-hour digital access to online banking, provide instant refunds, and satisfy requests for more information in a matter of seconds.

Banks are already spending a lot of time, effort, and money answering customers’ questions about checking up on their accounts, transfers, and other transactions over the phone and through social media. In time-saving scenarios, AI will be able to answer complex questions without human intervention.

Provide Financial Health Advice

AI is a concept with genuine promise. But, it has yet to prove its worth in a way that would be impactful for many. No, it is not about making predictions. Rather, it’s about building an economic narrative: an efficient economic ecosystem that will keep employees and investors on a path towards financial wellness, which will work towards achieving the company’s financial and societal goals.

In this age of high-tech startups, AI will finally help us fulfill our lofty financial health aspirations. Leaders in financial innovation are on the cusp of delivering the killer apps for a more well-rounded financial health—which will be central to financial wellness.

Support Lending With Predictive Analytics

Predictive analytics is a way to predict future events based on historical data. Types of data used to predict future events could include geographic, historical, or behavioral data—such as economic data, customer feedback, and collected social media data—to predict how likely a company is to make a loan, how long it will take for a loan to be paid, and which customers are more likely to pay back their loans.

Financial institutions are embracing AI to identify risk and personalize lending strategies. Predictive analytics can also improve lending rates and spot fraud risks. In addition, the AI-supported loan process can virtually eliminate manual, time-consuming decision-making, which can be a tedious and costly operation. The outcome is a system that makes for more lending and a better client loan experience.

Increase Transparency

Algorithms analyzing large bank transactions can help with anti-money-laundering and compliance concerns. Using AI, they can substantially increase the security and accuracy of personal banking transactions by using technologies such as facial imaging and voice recognition systems to detect counterfeit and error-ridden checks. Banks also use AI systems to recognize and analyze screen images, which is becoming an important security tool in preventing fraud.

Forecast Customer Habits

A major challenge in banking is forecasting customer behavior—a skill AI can help with. AI uses many metrics, including social media posts, purchasing card data, and natural language processing, to analyze buying patterns to better predict what consumers want and the timing of their desires. This data is then used in machine learning models to hone in on more accurate predictions.

Customer analytics, predictive marketing, and AI are three ways to get an edge in today’s complicated banking environment. It’s very tough for banks to win against the competition unless they’re taking full advantage of these advanced technologies. AI can help banks understand customer intent, which can help prevent costly mix-ups, save money, and improve customer service.

Final Word

Advancements in modern technologies are likely to significantly impact customer experience in the banking sector. AI can potentially be used to create new customer experiences that make banking more convenient and fast and waste less of the customer’s time.