Artificial Intelligence is Reshaping Banking. Here Are the Pros & Cons.

As artificial intelligence (A.I.) becomes increasingly accessible in 2024, many industries will experience positive and negative impacts of its widespread adoption – especially banking. In fact, according to a study by MarketsandMarkets, financial sectors are expected to play a major role in the global A.I. market reaching $190 billion by 2025.

From fraud investigations and data analysis to customer service, there are several ways that A.I. can help improve the efficiency, accuracy, and convenience of banking. However, it is critical for leaders in banking to understand the risks associated with deploying this technology and how to prevent potential consequences. 

For banks considering new opportunities to utilize A.I. in 2024, below are the pros and cons to consider when leveraging this advanced technology. 


From fraud investigations to improved operations, the list of pros associated with integrating A.I. into your bank’s infrastructure are aplenty. Below are the top reasons why A.I. should be a part of your banking business plan this year.

Operational Efficiency

Reducing costs, improving performance and accuracy, and enhancing operations are all goals that most organizations, including banks, can get behind. A.I. is making it easier than ever for organizations to accomplish just that. The Financial Stability Oversight Council (FSOC) notes that these are some of the top reasons that A.I. adoption is expected to accelerate in the year ahead.

For banking, A.I. offers opportunities to offload tedious and time-consuming tasks from staff responsibilities. For example, the technology can automate basic but critical business needs, from customer service support, data analysis, and document review to retail credit underwriting and more.

Risk Mitigation

Fraud is a consistent, top of mind issue that banking professionals are constantly on the lookout for. However, identifying risk patterns in large sets of data can take time and very close attention to detail. A.I. can help to eliminate factors of human error by identifying indicators of fraudulent activity in real-time and even aiding in investigations after a fraud occurs.

By utilizing large language models in their A.I. programs, risk and fraud teams will be able to efficiently analyze data sets and identify patterns of concern that are more elusive to human detection. Together with machine learning, A.I. can also aid in flagging and responding to access anomalies more effectively and efficiently than ever before.




While A.I. and machine learning can significantly improve productivity, analysis accuracy, and ease of reporting, technology poses its own special challenges for businesses that banking leaders must be aware of and prepared for prior to adopting A.I. across their organizations. Below are the top issues surrounding A.I. in banking this year.


Cybersecurity Concerns

Anytime you turn to automation, cybersecurity poses a risk. The National Institute of Standards and Technology (NIST) has been keeping an eye on emerging risks stemming from the widespread adoption of A.I. and has found that malicious actors can exploit poorly built A.I. models to the detriment of the organization. For example, cybercriminals can leverage models to potentially disclose users' third-party data and trick the program into disclosing sensitive information and credentials. Additionally, data poisoning can manipulate training data and introduce malicious behavior to A.I. models.


Therefore, it is critical for banking leaders to rigorously train their staff on identifying and reporting any suspicious behavior or data access, as well as how to leverage A.I. models safely and appropriately.


Lack of “Explainability”

In the most recent FSOC annual report, it is noted that A.I. models can suffer from what is described as a lack of “explainability.” This means that there is an increased risk of confusion around building A.I. models correctly, which can produce biased or inaccurate output results. To prevent this challenge from impacting data accuracy, the organization recommends that banking businesses establish controls on the original source and legal permission for data to train those models.


As the popularity of A.I. in banking businesses continues to grow, more organizations are considering making this advanced technology a part of their 2024 transformation goals. Leaders must take time to carefully consider the pros and plan for the cons so that they can take an informed approach at adoption. However, as technology becomes more sophisticated and accessible, one thing is clear: A.I. will continue to reshape banking in 2024 and beyond.


Christopher Salone, CISA, MBA, CCSFP is a Consulting Manager and Financial Services Practice Leader of FoxPointe Solutions, the Information Risk Management Division of The Bonadio Group. His work focuses on internal and external auditing of information technology and information security practices and controls, providing services to clients across multiple industries, including public and private companies, financial institutions, healthcare organizations, tech companies, and school districts. He conducts audits in accordance with regulatory compliance standards. Christopher can be reached online at and at our company website

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