Only 28 percent of bank consumers believe that AI will make their lives better within the next three years, and 17 percent believe it’ll actually make their lives worse.
New technologies can come at the cost of human connection, leaving customers feeling undervalued. This raises an important question: Is AI implementation in banking worth the risks that come with it?
What Are Some Challenges of AI in Banking?
- Compliance to constantly evolving regulations around AI can be tricky.
- Companies rushing to implement AI can risk the customer experience.
- Banks’ incompetent AI, such as chatbots, can frustrate customers and lose their trust.
AI in Banking: The Good
Banks are using AI to streamline operations, enhance customer service and detect fraud. From chatbots that provide round-the-clock support to sophisticated fraud detection systems, AI is rapidly reshaping the banking industry, bringing with it a wave of efficiency and innovation.
How, exactly?
Credit Product Evaluation Enhancement
In the past, loan officers manually reviewed all credit product applications, relying on limited data points, such as traditional credit history, income statements and a few other static data points, and often subjective judgments. This process was prone to biases and could take days or weeks to complete. The offers were often generic, lacking personalized recommendations based on individual needs and spending habits.
Today, AI-powered algorithms analyze vast data sets, including credit history, spending patterns, social media activity and online browsing history to assess creditworthiness in real-time. This results in faster, more accurate decisions, with some banks offering approvals within minutes.
Operational Efficiency
AI-powered fraud detection frees human analysts from repetitive tasks, allowing them to focus on more complex investigations and making operations more efficient. For example, Indian Axis Bank used AI to reduce inward debit processing from 20 minutes to only eight minutes.
Fraud Detection
AI-driven fraud detection systems are safeguarding both banks and customers. For instance, Mastercard’s implementation of AI in fraud detection helps them identify fraudulent activities before they can cause damage. It doubles the speed at which Mastercard is able to detect compromised cards.
The model constantly monitors millions of transactions of banks’ clients per second. This means AI already knows approximate patterns of behavior of a particular customer. If AI notices, for instance, that the client does an unusual action (transfers a large amount of money to someone else’s account), it blocks the transaction.
AI in Banking: The Bad
While AI offers tremendous potential, its integration comes with a shadow side.
Regulatory Complexity
As the technology grows more prominent, the efforts to regulate it become more complex as well. The need for banks to comply with the constantly evolving AI regulations demands extra efforts and resources, which some banks may not be able to afford.
The Race to Implement AI
There’s a lot of AI hype right now. In trying to win the technology race, some companies forget about the customers. They’re sure that enhancing their efficiency is as simple as buying the right technology.
But this isn’t true. AI should be implemented by professionals. Hasty AI implementation can cause many problems, such as customers’ dissatisfaction and distrust.
Chatbots Aren’t Up to Speed
If we take a look at banking chatbots, we can see that they aren’t working perfectly yet. For instance, say a client has a unique question or problem that can’t be resolved without human assistance, so the customer tries connecting to a support manager.
Meanwhile, the bot offers different questions it can answer instead of connecting the customer to an operator. The client wastes time struggling with the machine instead of getting an immediate answer from a human. This is how AI turns from a solution into a problem, making customers feel that AI complicates their lives.
How Banks Can Responsibly Integrate AI
A measured approach is essential for ensuring successful and responsible AI integration. Here are the main things to consider.
Regulatory Clarity
Before making substantial investments in AI, banks should wait for clear and comprehensive regulations to emerge. A regulatory framework will provide a solid foundation for responsible development and deployment, minimizing risks and fostering trust among stakeholders.
This report by U.S. authorities is a great example of what authorities should do to draw lines around safe AI adoption. As AI is a relatively new phenomenon, though, the regulations might still be blurry.
I recommend companies first familiarize themselves with existing rules and guidelines from relevant bodies, such as the European Union’s AI Act and the Federal Trade Commission guidelines. Then, you can build your own approach to AI regulation, prioritizing data security and focusing on transparency.
Data Security and Privacy
Banks must prioritize data security and privacy. Investing in robust compliance and security departments, if not already established, is essential.
Companies should consider hiring a chief compliance officer and establishing comprehensive policies and procedures, including technical standards for data protection. This will protect sensitive customer information from unauthorized access, manipulation or misuse.
For instance, JPMorgan Chase has a highly effective compliance and security department. It’s overseen by a chief information security officer whose aim is to protect the bank’s assets and customer information. The bank makes significant investments in cybersecurity, reportedly spending over $600 million annually on information security alone.
Human and AI Collaboration
AI is a powerful tool, but it shouldn’t replace human judgment entirely. A hybrid model, where employees act as supervisors to AI systems, is the ideal approach.
For example, in banking, AI-driven credit scoring can be a beneficial and time-saving solution. Meanwhile, human supervision can handle exceptions that the AI might not fully understand. This cooperation allows AI to enhance human capabilities, rather than replace them, ensuring a balanced and effective integration.
Education and Transparency
Training programs for employees will equip them to recognize and respond to AI-related fraud and use the technology for improved performance.
For example, frontline staff may need basic training on recognizing AI-driven fraud indicators, while IT staff may require more in-depth technical knowledge on AI systems and cybersecurity protocols. Being transparent with customers by explaining AI’s risks and benefits and how exactly your organization is using it will alleviate fears and build trust.
While challenges and risks exist, the potential of AI in banking is undeniable. By embracing a calculated approach, banks can harness this technology to revolutionize operations, improve customer service and create a more efficient and secure financial ecosystem.
The key lies in understanding that AI is not a replacement for human intelligence, but rather a powerful tool to be used strategically and responsibly.
Banks should remember who they are working for: their clients. And the best way to make your customers’ lives better is to provide them with the best service. So, while implementing AI, think twice about whether you’re ready for this technology.
