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Top 5 Use Cases of AI in the Banking Sector

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Use Cases of AI in the Banking Sector: Chatbots and More

Having a clear technological strategy is a major requirement for those financial institutions that want to survive the digital revolution, transform, and thrive in the new environment. Experts are convinced that AI is the future of banking. Large banks often have their in-house Research and Developments teams, while others have to rely on the expertise of tech giants and start-ups.

There are several ways that AI can be used to make banks more efficient, prevent fraud, and improve customer experience. Let’s take a look at the top five use cases of AI in the banking sector.

1. Digitally Enhanced Customer Service

Old-time call centers were notorious for being the weakest links when it came to customer experience. Long waiting times and frequent inability of bank representatives to resolve the issues plagued customer support. With AI taking over this niche, clients of various financial institutions can receive answers to their questions at any time of the day or night without being confined to the bank’s usual working hours. Those customers who prefer not to interact with a human bank worker are relieved to have their information provided by an automatic service.

For those who still want to have a human specialist answering questions, there are customer service representatives. But with the help of AI, they are now much more productive and efficient, being able to help more clients in the same amount of time.

Two of the most common solutions in this niche are chatbots and virtual assistants. Generic customer questions that take up about 80-90 percent of the queries can be solved with the help of virtual assistants and chatbots connected to a Q&A database. Customer service representatives have their time freed to deal with more complex client issues that require human supervision.

While modern chatbots are still rather simple digital machines, technologies like predictive analytics and machine learning already make them useful in raising customer awareness about additional offerings, programs, and bonuses. With chatbots, customers can have a truly personalized experience, because AI can deduce the offers that are suitable to them, the best time to send those offers, and the right device to display the information.

Chatbots can also be used internally for onboarding new workers and freeing managers from answering typical questions from their subordinates to focus on more important tasks. Virtual assistants and chatbots have proven to be a blessing during the pandemic because they reduced potentially risky human behavior and most customers were able to successfully use banking services and resolve their issues through digital means.

2. Eliminating Risks and Overhead Costs

Modern banking is mostly digital when it comes to day-to-day operations, but some of the processes are still paperwork-heavy and require a lot of human involvement. This sometimes leads to unforeseen losses and risks due to human negligence or error. AI software can mimic tasks and operations performed by bank workers if they rely on a set of instructions.

Digital machines can fully automate rule-based tasks, eliminating the potential for human error. Robotic process automation also greatly reduces the time needed to perform these tasks, bringing down the overhead costs. Banks can fully delegate tasks like entering data from forms, contracts, and other sources to robots. For example, the software can recognize handwriting in various languages from documents and forms, then use natural language processing to adapt, merge, and store this information to process the customer’s application.

3. Detecting Fraud

Fraud detection is the area where AI in banking is vastly superior to even the best-trained human specialists. Machines process an incredible amount of data in seconds, pushing it through a number of algorithms designed to detect fraudulent activity. More importantly, the software can distinguish between false positives and real instances of fraud better than any human. For example, if a bank client had to travel a lot to different countries, previously they were at risk of getting blocked and having their credit card declined due to suspicious activity. With AI, they can use their credit cards at any location without worries. But the system will detect an unusual behavior pattern and warn the user immediately if there’s criminal activity concerning their cards and accounts.

4. Making Better Loan Decisions

Banks are using AI to decide the credit score and potential risks associated with their borrowers. Customer references, national credit score system, credit history, and previous banking transactions have been traditionally used to assess a client’s creditworthiness. However, this process is rather complicated and often leads to misclassifying borrowers and having their loan applications denied. AI can look at patterns in the client’s behavior, analyze the information previously processed by a human, and assess the risk better, providing a clear picture of whether the client is going to default on a loan or not.

5. Investment and Trading

AI machines can be incredible helpers when it comes to making decisions about investing or trading assets. Some financial companies are already using AI software applications to search the market for potential investment opportunities and supply trading robots with data. By using more precise models and being able to sift through tons of data, AI can find additional opportunities overlooked even by experienced traders and hedge fund managers. The software can even provide personalized advice on portfolio management that takes into account assets, market sectors, and investment strategies preferred by the customer.

Conclusion

AI is the future of the financial industry. Top US banks are integrating AI technologies into their day-to-day operations. With AI solutions, customers can receive a personalized user experience and access to banking services at any time of day or night. Banks are able to monitor fraud, make better loan decisions, find new investment opportunities, and bring down overhead costs.

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