Software

3 Ways Banks Are Leveraging AI in 2024

Artificial intelligence and high-performance computing can support banks in protecting consumers, improving the customer experience and maintaining regulatory compliance.

Jordan Scott

Jordan Scott is the web editor for HealthTech magazine and a multimedia journalist with experience in B2B publishing.

Consumers interact with artificial intelligence daily without even realizing it. Spam filters use machine learning algorithms, a subset of AI, to determine which emails to divert. And many businesses use AI to monitor for fraudulent transactions and automate processes. It’s a powerful tool for protecting consumers and improving their experiences, but AI is also becoming essential to protecting banks themselves.

Banking and financial services are among the most mature industries when it comes to AI. As organizations look to apply more advanced algorithms to their data, high-performance computing will become critical.

Here are a few ways that banks are using AI and HPC to make the most of their data:

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1. AI for Fraud Detection

One way that banks can take advantage of AI and HPC is by applying the technology to fraud detection. Credit card fraud was the top type of identity theft in 2023, according to the Federal Trade Commission, with 416,582 reports. This is down from 440,692 reports in 2022; however, while theft targeting new accounts went down by 7 percent, it was up 14 percent for existing accounts.

Being able to quickly alert banks and consumers to potentially fraudulent activity is crucial to mitigating financial loss and other damage. HPC-powered AI algorithms can detect unusual credit card activity as it happens.

In a blog by IBM, Cloud General Manager Alan Peacock writes that “the algorithm can help trigger an action, such as flagging the suspicious credit card activity to the customer via text.”

In an instant, a customer can confirm with the bank whether the activity is fraudulent or genuine. And in the case of fraudulent use, the bank can take remediation steps, such as canceling the card or launching an investigation immediately.

2. AI to Improve Regulatory Compliance

AI can also help banks adhere to regulatory requirements. This goes for cybersecurity risks and standard compliance issues.

Generative AI solutions can assist banks in assessing customer risk profiles, detecting suspicious activity and monitoring data models. It can also notify banks of any noncompliance or suspicious transactions. Applying AI and HPC to risk management allows banks to run models with more data, computations and accuracy.

“To support the demands of today’s regulatory standards, HPC is designed to help financial services deliver the performance levels these computationally intensive calculations require, whether located on-premises or in the cloud,” Peacock writes.

AI can analyze a bank’s data sources to identify security vulnerabilities or run red teaming exercises to help financial services companies improve their cybersecurity posture.

Finally, generative AI can be used as a tool for compliance education or to create compliance efficiencies. Employees can ask a generative AI solution questions related to regulatory requirements or company policies. Generative AI can also draft credit risk reports.

However, it’s critically important that banks’ use of AI doesn’t affect regulatory compliance.

To support the demands of today’s regulatory standards, HPC is designed to help financial services deliver the performance levels these computationally intensive calculations require.”

Alan Peacock Cloud General Manager, IBM

3. AI for a Better Omnichannel Customer Experience

Many customers today prefer a quick, easy-to-access service that doesn’t require picking up the phone or getting in the car. Mobile banking is a leading example of how financial services have adopted AI to improve customer experiences.

Other financial services companies are using AI and HPC to help customers with money management. AI-powered robo-advisors use AI algorithms to optimize and automate investments and portfolio management. However, this area is still being explored.

AI-powered chatbots are another use of AI in financial services. These tools can engage with customers quickly at any time to assist with basic banking questions. Many chatbots can access information from different data sources within the bank’s ecosystem to respond to customer service requests more accurately and in a personalized manner.

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With machine learning applied, a chatbot can detect when a response is beyond its capabilities or if a customer is becoming frustrated so that it can direct the customer to a live agent for a better experience. This approach to customer service creates the best of both worlds for people who prefer quick, automated interactions and for those who prefer a human touch. Chatbots also make contact centers more effective by triaging customer service needs.

“Customers no longer have to waste valuable time waiting to talk to a representative, while banks stand to save a substantial number of resources that would be otherwise funneled to their call centers,” says Phillip Dudovicz, industry director for regulated industries at Hitachi, in a recent blog.

As banks consider these use cases and others, it’s important that they are prepared with the capabilities to support advanced AI tools, such as HPC and cloud computing.

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