AI and Your Money: The Promise and Peril of Artificial Intelligence in the Banking and Finance Sector

Artificial intelligence (AI) has made significant strides in the banking sector in recent years. From customer service to fraud detection, AI is transforming how banks operate. With the use of machine learning algorithms, banks can analyze vast amounts of data and make informed decisions. However, as with any new technology, there is both promise and peril associated with AI in banking.

The increased reliance on artificial intelligence has pros and cons for the banking sector, and more widely to their customers. While widespread adoption of AI and machine learning throughout the finance sector may still be years away. Now is the time to explore the early results. With that in mind, here’s a look at potential positives and negatives of letting the robots take the financial reins.

Promises of AI in Banking

Improvements in the Customer Experience

Banks live and die on their customer service, and depositors who feel disregarded or disrespected are likely to vote with their dollars and take their business elsewhere. Providing stellar customer service while keeping costs down has been an ongoing challenge for banks, and it’s something AI could one day revolutionize.

AI has the potential to revolutionize the customer experience in banking. Chatbots and virtual assistants can handle customer queries, provide account information, and offer personalized recommendations based on customer preferences. This allows banks to provide 24/7 customer service without the need for human intervention.

Enhanced Fraud Detection

With electronic transactions becoming a greater share of banking business, fraud detection is an indispensable part of doing business. The sheer volume of daily transactions makes human scrutiny impossible, but AI can handle the load far more efficiently and effectively.

Fraud is a major concern for banks, and AI can help banks detect fraudulent activity in real time. Machine learning algorithms can analyze patterns in transactions and identify suspicious behavior. This can help prevent fraud before it occurs, saving banks and their customers significant amounts of money.

Increases in Efficiency

Many banks operate on razor-thin margins, and rising employment costs only make the problem worse. Those in the financial sector are always looking for ways to ramp up efficiency and do more with less, and that’s where artificial intelligence comes into play.

Artificial intelligence can potentially automate many of the routine tasks that bank employees perform. This includes data entry, record keeping, and compliance checks. By automating these tasks, banks can improve efficiency and reduce costs. This can also free up employees to focus on higher-level tasks that require human expertise.

More Effective Risk Management

Risk management is a big problem in the banking sector — one need only look at the rapid collapse of Silicon Valley Bank for proof of that. Forensic examination of the bank and its deposits showed that nearly 90% of deposits exceeded the $250,000 FDIC insurance limit — a fact that somehow escaped the scrutiny of all the human regulators.

AI has the potential to help banks manage risk by providing more accurate predictions and insights. Machine learning algorithms can analyze historical data to identify patterns and trends that may indicate potential risks. This can help banks make better-informed decisions and reduce the likelihood of losses.

Perils of AI in Banking

Potential Bias

AI critics like to point out that machine learning is only as good as its teacher. Even well-meaning data scientists may introduce their own thoughts, feelings, and biases without knowing it, giving their AI creations a skewed vision of reality and creating a number of problems down the line.

One of the biggest perils of AI in banking is the potential for bias. AI systems are only as unbiased as the data they are trained on. If the data used to train an AI system is biased, it will reflect that bias in its decision-making. This can lead to discrimination against certain groups of people, such as minorities or low-income individuals.

Security Risks

From the early days of the text-based internet to the latest social media iterations, every form of online technology has been hacked. Hacking is an especially pernicious problem, one that to date has not been solved.

Like their less intelligent counterparts, artificial intelligence and machine learning systems can also be vulnerable to cyber-attacks, which can result in the loss of sensitive customer information. Banks must ensure that their AI systems are secure and that they are not vulnerable to attacks.

Lack of Transparency

One of the hallmarks of artificial intelligence and machine learning is that the algorithms build on themselves, advancing their abilities along the way. Over time the advances have the potential to exceed the abilities of their human programmers, creating a troubling lack of transparency.

AI systems can be complex, and it can be difficult to understand how they make decisions. This lack of transparency can be problematic, particularly regarding compliance and regulatory requirements. Banks must ensure that their AI systems are transparent and that they are able to explain how they arrive at their decisions.

Job Losses

The AI revolution has often been likened to the industrial revolution. Proponents see that comparison as proof of a great leap forward, while critics point to the enormous societal disruptions that followed the sudden shift from subsistence farming to large-scale factories.

As AI systems automate many of the routine tasks performed by bank employees, there is a risk that jobs may be lost. This could have a significant impact on bank employees, particularly those in lower-level roles.

Artificial intelligence can potentially revolutionize the banking sector, but it has risks. Banks must ensure their AI systems are secure, transparent, and unbiased. They must also ensure that their employees are trained to work alongside AI systems rather than being replaced by them. Ultimately, the success of AI in banking will depend on how well banks can balance the promise and peril of this exciting technology.