How Banks & Financial Industry are Navigating the Artificial Intelligence Landscape

Decreasing budgets and evolving customer expectations are pushing banks to adopt artificial intelligence solutions to remain competitive and efficient in today’s technology-focused landscape. According to a recent study by consulting firm Publicis Sapiens, banks are now spending nearly one-third of their budgets on AI and machine learning to create customer experience (CX) transformations and risk management operations. This shift underscores a clear trend toward using technology for more personalized services, data-driven decision-making, and agile processes to secure the financial industry's future.

Historical Use of AI in Banking & the Financial Industry

AI has been a part of the banking industry for a century, and “over two thirds of Fortune 1000 companies had at least one AI project being developed” in the 1980s. Use cases in the 1950s and 1960s explored automation improvements, statistical modelling research, and algorithmic trading, and “the application of Bayesian statistics became quite popular due to the work of Robert Schlaifer.” Bayesian statistics, or the ability “to make informed decisions based on probabilities,” play a crucial role in AI processes by allowing “algorithms to optimize based on updated data, revise relationships between variables, and make more accurate decisions and predictions.”

Other early applications of AI include fraud detection technology, credit scoring data analytics, and the implementation of customer service chatbots. Fraud detection systems, for instance, leverage machine learning algorithms to identify, with increasing accuracy, unusual patterns and potentially fraudulent activities in transaction data. Credit scoring models have utilized AI to analyze a wider range of data points beyond traditional credit histories, allowing banks to make better lending decisions and extend credit to underserved populations. These initial uses laid the groundwork for more advanced applications of AI in banking today.

Current Applications of AI in Banking & Finance

In the 21^st century, banks and financial institutions are leveraging AI in several innovative ways to navigate employee tasks, customer service needs, and financial risks:

• Transactional Use Cases: Roughly two-thirds of banks use AI for transactional purposes such as credit analysis, portfolio management, risk assessment, and legal contract analysis. AI algorithms can process vast amounts of data quickly, providing more accurate and timely insights than traditional methods. For example, AI can evaluate the creditworthiness of loan applicants by analyzing their spending patterns, social media activity, and other non-traditional data sources.
• Employee Productivity and Training: AI-powered assistants are becoming common in banks, and approximately 55% of banks use generative AI to help employees with tasks like scheduling, data entry, and responding to routine inquiries. Employees are also receiving better training using AI and much of the banking industry’s success relies on a strong cybersecurity culture, one that “entails constant risk assessments, complex risk management frameworks, and ongoing education and training.” This not only improves efficiency but also allows employees to focus on more complex and strategic tasks. 
• Fraud Detection and Prevention: AI systems can detect patterns and anomalies in transaction data that might indicate fraudulent activity. By identifying potential fraud in real-time, banks can take immediate action to prevent losses and improve customer satisfaction through financial security. Additionally, advanced AI algorithms can adapt to new fraud tactics and employ cybersecurity measures to remain effective against evolving threats.
• Regulatory Compliance: AI helps banks stay compliant with regulatory requirements by automating the monitoring and reporting of transactions. This reduces the risk of human error and ensures that banks meet all necessary legal standards. Compliance teams can leverage AI to scan and interpret large volumes of regulatory documents, like those listed by the American Association of Bankers, keeping up with the ever-changing legal landscape.
• Marketing and Customer Service: Nearly half of banks use AI in their marketing and customer service operations. According to the CIO of Singapore’s Trust Bank, “customers told us they wanted their banking to be easy and transparent, and we understood that real-time capability is a key enabler in this regard.” Technology like AI can analyze data in real-time to personalize marketing campaigns and predict customer needs, leading to more effective engagement and higher customer satisfaction. Personalized financial advice and product recommendations based on individual customer profiles are becoming standard practice as well, thanks to AI.

Risks, Challenges, and the Future of AI in Banking & Finance

Despite the benefits, financial institutions face several challenges in their digital transformation journey. Regulatory challenges, lack of operational flexibility, and outdated technologies are posing significant obstacles, as well as budget constraints which continue to be a major concern for many banks. It was noted that “the biggest challenge is really about finding that right prioritization and knowing where to put your resources, which are very thin, and make sure that you get good payback from all these investments in AI.” Additionally, issues like deepfakes, more convincing counterfeit money and check-washing, and other criminal trends are a growing concern for financial security. In fact, the IRS stated that “online payment fraud now exceeds $360 billion annually, and that check fraud is ‘skyrocketing’.”

To overcome these challenges, banks and financial institutions are focusing on developing existing talent and preparing employees to work with AI. This includes training programs and initiatives to foster a culture of innovation and adaptability. Equipping employees with the skills needed to leverage AI effectively ensures a smoother transition to a more technology-driven operation, as AI is not at the point of self-operation yet.

Looking ahead, the biggest focus for banks will likely be on AI-supported data analysis and the use of generative AI for internal processes. By integrating AI into business models and workflows, banks can drive operational efficiencies, reduce costs, and enhance customer experiences for a brighter, more secure technological future.

Exploring Artificial Intelligence Across Industries

As industries evolve, the demand for professionals who can adapt, innovate, and contribute to the integration of technology within complex systems will continue to grow. AI is increasingly being applied across various sectors, driving transformative changes. Capitol Technology University offers dynamic programs in AI and Management of Technology, focusing on the intersection of these fields across diverse industries. In addition to academic programs at both the undergraduate and graduate levels, Capitol Tech provides access to an innovative AI Center of Excellence that features valuable research and mentorship opportunities, guided by leaders in the field to further students' expertise and readiness to navigate the future.

To learn more, contact our Admissions team or request more information today.