AI in banking

To stay ahead of fintech trends, increase their competitive advantage and provide valuable services and better customer experiences, banks and other financial services firms have embraced digital transformation initiatives.

The advent of AI technologies has made digital transformation even more important and is remaking the industry. AI is no longer an option but an imperative, and financial institutions that invest in AI platforms have greater potential to lead and thrive.

Historically, incumbent financial service providers have struggled with innovation. A McKinsey study found that large banks were 40% less productive than digital natives.¹ Many emerging banking startups are pioneering artificial intelligence use cases, making it even more important that traditional banks catch up and innovate themselves.

Bankers have long used natural language processing (NLP) to parse the vast amounts of data they have internally or that they pull from third-party sources. They use data analytics tools to uncover insights and trends and make more informed decisions around key investments and wealth management.

The banking sector, in particular, is increasingly relying on the advantages of AI technologies to remain competitive. Customers want seamless digital banking experiences: apps that anticipate their needs and the ability to interact with people or virtual assistants depending on the complexity of their issue. Companies need to improve the user experience to keep those customers happy. Adopting and deploying generative AI solutions, coupled with effective data management, is a key step toward that goal.

While AI is powerful on its own, combining it with automation unlocks even more potential. AI-powered automation combines the intelligence of AI with the reliability of automation. Traditional tools like Robotic Process Automation (RPA) have been valuable for streamlining repetitive tasks, but banks are now beginning to adopt agentic AI systems to handle more complex workflows.

An AI agent is capable of autonomous decision-making and, for example, can guide a loan application from start to finish. It can interact with the customer, verify documents, check creditworthiness against internal and external databases and flag compliance issues. It adapts to changing information and makes decisions in real time instead of just following preset rules, all with minimal human intervention.

Banking and financial services organizations are embracing AI for various reasons, including risk management, enhancing customer experience and streamlining front, middle and back-office processes.

AI helps customers enhance their decision-making about financial matters. They are more likely to stay with banks that use cutting-edge AI technology to help them better manage their money.

But given extensive industry regulations, banks and other financial services organizations need a comprehensive strategy for approaching AI. Using AI requires an AI strategy and implementation framework that maximizes business value while mitigating risk.

The IBM Institute for Business Value (IBV) published a guide for banks looking to embed AI tools and practices into their operations in its Global Outlook for Banking 2025 report.² Some of the key actions are:

Adjust the business model to take advantage of the digitalization of financial services. Revise your business strategies by transforming how you cater to clients. Expand your capability to serve clients with embedded finance, allowing them to do banking anywhere, anytime. Enhance advisory propositions with AI to capture new service fees, both for consumers, businesses and specialized areas like investment banking. Reconsider payment initiatives as the backbone for new data to fortify AI-powered risk management across ecosystems.

Drive operational efficiency using AI.  Focus on high-impact workloads to streamline and enhance offerings, making them seamlessly digital-friendly. Embrace AI to reimagine processes end-to-end, driving efficiency and innovation. Design for hybrid cloud to optimize costs and simplify operations.

Renew your risk management culture—one where every banker becomes an AI risk manager. Accelerate software development with AI, but don’t overlook the risk of increased complexity—invest in clear platform governance to manage security, compliance and resiliency as innovation scales. Prioritize data governance to safeguard confidentiality, integrity and availability, helping ensure that AI models are built on robust frameworks to mitigate risks like data breaches, legal uncertainties and model biases.

Implement savvy education programs that keep pace with technological advances. Our perspective is that AI is an automation advantage as well as an augmentation opportunity—empowering bankers to reimagine their contributions in a digitally transformed industry.

This equally applies to both business domains and technology departments. Banks may struggle to find the right skills and can’t afford to delay reskilling workers who are often stuck in routine tasks and unable to keep up with rapid innovation.

Lead with AI or be left behind. Banks must articulate their business strategy clearly to distinguish themselves from competitors in the age of AI— technological innovation alone is not enough. Moving from innovating with AI to innovating based on AI demands an “AI-first” approach, where the AI platform becomes central to all business and operational strategies 

There are several key benefits for banks that embrace and deploy AI.

Enhanced APIs: Banking operations increasingly depend on the use of application programming interfaces (APIs) to enable customers to track their money on various applications. For example, banks must give API permission to third-party budgeting apps so customers can monitor multiple bank accounts. AI enhances API usage by enabling more security measures and automating repetitive tasks, making them more powerful.

More intelligent customer tools: The rise of generative AI and agentic AI powered by deep learning means that the investment and banking industries can deploy more sophisticated tools to streamline customer service. AI-powered chatbots and virtual assistants can enhance customer support, helping customers solve small problems on their own. AI can also power budgeting apps that help customers better manage their finances and save more money.

Smarter credit scoring: Determining creditworthiness is a critical banking service activity. Banks need to crunch significant amounts of customer data to make important credit decisions, such as whether they accept a credit card application or approve a credit increase. AI algorithms and machine learning techniques can help financial institutions approve or deny credit cards, credit increases and other customer requests at fast speeds.

Enhanced cybersecurity and fraud detection: Cyberattackers increasingly use AI to create more sophisticated ways to defraud financial institutions. They can use AI-created audio³ to imitate customers, confusing customer service agents. They can use AI to make phishing emails look increasingly legitimate. As a result, those financial institutions can use AI algorithms to protect their employees from cybersecurity threats in real-time, while creating tools to help customers avoid the same tricks. Financial institutions and governmental agencies can also use AI systems to thwart other financial crimes like money laundering or impersonation.

Embeddable banking: This is the introduction of banking into nontraditional experiences, such as when Starbucks started its own payments app.⁴ Embeddable banking is expected to grow as a service, especially as AI helps retailers and other companies collect and analyze data about potential market opportunities, predict creditworthiness and better personalize services to customers.

New markets and opportunities:  AI-driven predictive analytics and forecasting tools can identify new areas of growth, improve underwriting processes and better estimate which customers are a churn risk. For example, banks can analyze their customers’ habits, such as how often they log in or deposit money and compare it to other data points to determine whether individual customers can be on the verge of canceling their accounts.

Introducing AI in banking is not without risks and complications. A 2025 IBM IBV study found that 55% of business and financial markets CEOs say the potential productivity gains from automation are so great they must accept significant risk to stay competitive.⁵  Some of these risks include:

Cybersecurity: Generative AI technology can be used for fraud prevention and compliance management, but it also produces risks. Embedding open AI tools and technologies into banking IT systems creates some security challenges because AI models are especially valuable targets for malicious actors. That’s why banks need a holistic AI governance approach that effectively balances innovation and risk management.

Legal uncertainty related to operations: Generative AI models need training on existing datasets to be effective. There are still some unsolved issues on whether analyzing publicly available data, like news stories and explainer videos, constitutes copyright infringement.⁶ 77% of BFM CEOs say that inconsistent standards and regulations are inhibiting their ability to grow their business.² One way to avoid this issue is to use AI models that have been trained on data that the bank owns, such as customer service interactions or its own proprietary research.

Difficulties in controlling outcome accuracy: Currently, AI models do not reason or “understand” their outputs. Instead, AI models detect patterns in the data they’re given and generate results. Therefore, the model cannot tell the human employee if the data is incorrect or inaccurate. This makes explainability critical, especially in a regulated industry like banking, where understanding how a model reached its decision is essential.

Prejudice from model bias: Banks are increasingly investing in environmental, social and governance (ESG) initiatives as a way to demonstrate transparency and accountability for their actions. Because AI models are trained on human-created data, they can inherit some of the biases that influence humans. Banks need to eliminate bias in how they market products and determine factors like creditworthiness, which historically has negatively affected certain demographics.

Banking institutions are under increased pressure for digital transformation. Customers demand automated experiences with self-service capabilities, but they also want interactions to feel personalized and uniquely human.

Banks continue to prioritize AI investment to stay ahead of the competition and offer customers increasingly sophisticated tools to manage their money and investments. Customers continue to prioritize banks that can offer personalized AI applications that help them gain visibility on their financial opportunities.

The future of AI in banking will likely include institutions advertising their use of AI and how they can deploy advancements faster than competitors. AI will help banks transition to new operating models, streamline workflows, embrace digitization and smart automation and achieve continued profitability in a new era of commercial and retail banking.