Banks Are Making Record Profits. Is Artificial Intelligence the Next Big Advantage in Banking?

The global banking industry is having an exceptionally profitable period. But while major financial institutions are reporting strong earnings, another development is beginning to reshape the competition: artificial intelligence.

Banks are investing heavily in AI to improve customer service, automate internal processes, detect suspicious transactions, assess financial risks, and make banking more efficient.

The opportunity is substantial. Institutions that successfully integrate AI into their operations could reduce costs, improve customer experiences, and develop new financial services. Those that struggle to adapt, however, may find themselves competing against faster, more technologically advanced rivals.

According to McKinsey's November 2026 Global Banking Annual Review, the industry generated a record $1.3 trillion in net income in 2025. Yet the report suggests that maintaining this performance will require banks to rethink how they operate as technology and customer expectations change.

The question is no longer whether AI will influence banking. It is how much competitive advantage financial institutions can gain from using it effectively.

Global banks generated $1.3 trillion in profits

McKinsey's Global Banking Annual Review 2026 describes 2025 as another exceptionally strong year for the industry.

Global banking net income reached approximately $1.3 trillion, representing a 7% increase from the previous year's record.

Banks benefited from growth in deposits, loans, and assets under management, alongside improvements in operational efficiency. The industry also continued to generate substantial returns for shareholders through dividends and share buybacks.

However, the strong financial performance does not mean every bank is equally positioned for the future.

McKinsey identifies growing competition from digital-only banks, financial technology companies, and new services that make it easier for customers to compare financial products.

Traditional banks are also facing a change in customer behaviour. People increasingly expect financial services to be accessible through digital channels, personalised to their needs, and available without unnecessary delays.

This creates pressure on established institutions to improve their technology while maintaining the reliability and security expected of financial services.

Why AI is becoming a major banking investment

Artificial intelligence offers banks several opportunities to improve how they work.

One of the most immediate applications is automation.

Banks handle enormous volumes of information and repetitive processes every day. These include reviewing documents, processing customer requests, checking transactions, assessing applications, and preparing internal reports.

AI systems can assist with many of these activities, potentially reducing the time employees spend on routine work.

Some of the most important applications include:

Fraud detection: AI can examine transaction patterns and identify activity that may indicate fraud or suspicious behaviour.

Customer service: AI assistants can respond to routine enquiries, help customers navigate banking services, and provide information around the clock.

Credit assessment: Machine learning systems can help analyse financial information and support decisions about lending and credit risk.

Compliance: AI can assist financial institutions with reviewing documents, identifying unusual activity, and monitoring regulatory requirements.

Personalised financial services: Banks can use customer information to provide more relevant product recommendations and financial guidance, subject to appropriate permissions and safeguards.

These applications are not entirely new. Banks have used machine learning and automation for years. What is changing is the growing ability of generative AI and AI agents to handle more complex, connected tasks.

AI agents could change how people interact with banks

One of the most significant developments highlighted in McKinsey's report is the emergence of AI agents in financial services.

Unlike a conventional chatbot that mainly responds to questions, an AI agent can be designed to carry out a sequence of tasks within defined permissions.

For example, a customer might ask an AI assistant to compare savings accounts, review spending, identify recurring subscriptions, or help organise a monthly budget.

More advanced systems could potentially coordinate several financial activities across different services.

This could change the relationship between customers and their banks.

Traditionally, people visited a bank's website or application to find information about its products. Increasingly, they may ask an independent AI assistant to compare products from several institutions before deciding where to keep their money.

McKinsey's research, based on a survey of 46,000 retail banking customers across major markets, highlights how quickly this behaviour is developing.

The report indicates that nearly one-quarter of surveyed customers were already using generative AI for financial purposes.

It also found that 74% of consumers who use AI for banking tasks relied on tools such as ChatGPT, Claude, or Gemini, rather than their bank's own chatbot.

This creates a new challenge for financial institutions. Banks may continue to process customers' transactions while losing influence over the digital conversations that shape those customers' financial decisions.

The race to reduce costs is becoming more complicated

For banks, AI is attractive partly because of its potential to improve productivity.

But deploying AI at scale is not automatically inexpensive.

A November 10 Reuters report on McKinsey's banking review highlighted a growing concern: the cost of operating AI agents.

According to the report, spending on the tokens that power AI agents could potentially reach 20% of total labour costs in some scenarios.

This creates a difficult calculation for financial institutions.

AI may reduce the time employees spend on certain tasks, but sophisticated systems also require computing resources, infrastructure, monitoring, security, and ongoing maintenance.

When several AI agents communicate with one another to complete a task, their combined usage can increase quickly.

Some banks are responding by establishing specialised AI financial operations teams, sometimes referred to as AI FinOps, to monitor spending and improve efficiency.

Rather than introducing AI everywhere, institutions are increasingly being encouraged to prioritise applications with measurable financial benefits.

The goal is not simply to use more AI. It is to make sure the technology creates more value than it costs.

Digital-only banks are increasing the pressure

Traditional banks are not competing only with one another.

Digital-only financial institutions have demonstrated that banking services can be delivered through relatively lean operating models and technology-focused platforms.

Companies such as Revolut and Nubank have attracted large customer bases by combining digital banking with additional financial services.

McKinsey's report highlights their growth and profitability as examples of how newer banking models are challenging traditional institutions.

These businesses have built customer experiences around mobile applications, streamlined services, and digital distribution.

AI could strengthen that competitive advantage further by helping financial institutions automate operations and deliver more personalised services.

For established banks, the challenge is significant. They often have extensive customer relationships, large financial resources, and established regulatory infrastructure. But they may also carry legacy technology systems and complex internal processes that make rapid innovation difficult.

The institutions that manage to combine their existing strengths with more agile technology operations could be better positioned for the next phase of competition.

What this could mean for customers

For everyday banking customers, the growing use of AI could eventually lead to more convenient services.

People may find it easier to resolve routine problems, compare financial products, understand spending patterns, and receive assistance outside traditional banking hours.

AI could also help banks identify unusual transactions more quickly and improve the way they respond to potential fraud.

However, the benefits will depend on how responsibly the technology is implemented.

Financial information is highly sensitive. Customers need to know how their data is being used, which systems can access their accounts, and whether an AI assistant is authorised to perform transactions.

There are also concerns about inaccurate recommendations, biased decisions, cybersecurity vulnerabilities, and the consequences of automated errors.

An AI assistant that provides an incorrect restaurant recommendation may cause inconvenience. An AI system that mishandles a financial transaction can have much more serious consequences.

For this reason, banks will need strong security controls, clear customer permissions, human oversight, and reliable procedures for resolving mistakes.

The implications for African banking

The developments taking place in global banking also have important implications for African financial institutions.

Across markets such as Nigeria, Kenya, South Africa, and Ghana, digital financial services have become an important part of everyday transactions.

Mobile banking, digital wallets, electronic payments, and fintech platforms have changed how many people access and manage money.

AI could extend this transformation.

Nigerian banks and fintech companies, for example, could explore AI-assisted customer support, improved fraud monitoring, more efficient transaction analysis, and better tools for understanding customer needs.

There may also be opportunities to develop financial services that are more accessible to people who have traditionally had limited access to conventional banking.

However, the region has specific challenges. Reliable digital infrastructure, data protection, cybersecurity, affordability, regulatory compliance, and the availability of relevant training will all influence how quickly institutions can adopt advanced AI systems.

Banks also need to consider local languages, customer behaviour, and the realities of financial access across different communities.

Simply copying technology strategies from major international banks may not produce the best results. Successful adoption will require solutions designed around local financial systems and customer needs.

The next competitive advantage in banking

The banking industry's record profits demonstrate how financially powerful the sector remains. But financial strength alone may not guarantee future success.

AI is changing the way financial institutions operate, how customers discover financial products, and how banks compete with technology companies.

McKinsey's central message is that institutions need to move faster. Banks that can combine reliable data, effective AI systems, responsible governance, and efficient operations may be better positioned to capture the next wave of growth.

The transition will not happen overnight, and not every AI investment will produce meaningful returns.

Some applications will improve productivity, others will introduce new costs, and some may create risks that financial institutions are still learning to manage.

The next generation of successful banks may not simply be the institutions with the largest customer bases or the most branches. They may be the ones that know how to combine financial trust with intelligent technology.

For customers, the real test will be whether these investments produce banking services that are genuinely safer, more accessible, and easier to use.