It’s not an overstatement to say that artificial intelligence is reshaping the banking sector. In a 2024 PYMENTS report, 72% of US finance leaders say their departments are already using AI, while that figure is even higher in the UK — 75% according to the Bank of England.
An increased reliance on AI tools — particularly on generative AI — isn’t without a certain amount of risk; after all, new technologies nearly always bring new vulnerabilities. Yet use cases for AI are growing more varied every day. It’s clear that, for banks and other financial institutions, AI is now a central component of digital transformation efforts, and the key to unlocking (and sustaining) operational excellence.
But what are the operational areas where AI has the most transformative potential? And how can banks work to maximize the ROI of their AI solutions? Keep reading as we unpack the future of AI in banking in more detail.
Six ways banks are harnessing AI for operational excellence
The banking industry has high expectations when it comes to AI. The 2024 Forrester Report found 44% of leaders expect it to raise productivity, 44% anticipate an uptick in revenue growth and a further 38% think AI will drive innovation. The question is, how are banks making these statistics a reality?
1 - Process automation & efficiency
One of the key benefits AI offers financial services organizations is the ability to automate and streamline repetitive processes, freeing up people to focus on more complex tasks that require human input. For instance, banks can use AI to delegate high-volume, low-stakes tasks to Robotic Process Automation bots (RPAs): software robots which are trained to carry out actions like clicking buttons, transferring files, or entering financial data. RPA bots don’t flag or lose focus, so they reduce the margin of error, and eradicate the bottlenecks so often caused by slow, menial tasks.
Banks are also using AI for more intelligent automation, including using Intelligent Document Processing tools to optimize labor-intensive KYC processes. An AI solution like this can extract and validate customer data from identity documents and other relevant credentials very quickly, simplifying due diligence processes while reducing operational costs.
2 - Risk management & fraud detection
In the same vein, AI applications are already being leveraged for risk management. Machine learning algorithms can detect discrepancies in transactions and customer behavior, helping to catch fraudulent activity and enabling banks to respond quickly to threats from bad actors.
AI-powered “liveness checks” are also being incorporated into biometric security protocols. These checks establish that the person being identified is both human and verifiable (i.e. not a deepfake or an impostor), with 3D mapping and movement analysis.
3 - Customer experience & reviews
Over the last few years, the tone and efficiency of conversational AI has come on in leaps and bounds. Today, AI-powered chatbots are being utilized on a massive scale in the banking sector to improve the availability of customer service. Trained by Large Language Models (LLMs), these tools can handle simple customer interaction, understand user intent, and respond to a range of inquiries, like “what’s my account balance?” or “what was the amount of my last deposit?”.
AI systems are also transforming the way banks handle customer feedback. Armed with sentiment analysis, financial institutions can review a vast amount of review data across multiple platforms, and extrapolate key trends in customer experience.
4 - Compliance & reporting
Regulatory compliance goalposts are constantly shifting for the banking sector, but AI tools are reducing the cost and labor of compliance efforts considerably. For example, RegTech solutions identify suspicious activity in real-time, ensuring constant compliance with anti-money laundering directives and KYC regulations, and flagging any breaches with regulatory requirements for immediate action.
An increasing number of banks are also using AI tools to automate the reporting process. These tools can be fed custom reporting templates, ensuring the right data is pulled and presented in an on-brand, digestible way. This application of AI minimizes human error, drastically cuts the resources required for time-consuming manual reporting, and ensures banks avoid fines relating to the accuracy, completeness, and timeliness of reporting.
5 - Decision making & strategy
Through advanced data analytics and intelligent forecasting, AI tools are empowering financial institutions to make data-informed decisions which can proactively manage and lessen risk. Insights garnered from this type of AI technology can not only drive investment strategy for portfolio managers, but also revolutionize everyday processes in retail banking, like lending, loan approval, and credit eligibility checks.
Scenario modelling takes this technology even further. AI is used to simulate hypothetical future scenarios (like a new presidential administration, or cyber attack) and the impact they’d have on financial performance. Banks can then use this predicted data to stress-test various strategies in resilience, and better insulate themselves against real risk.
6 - IT ops & cybersecurity
Robust IT infrastructure is the backbone of modern banking, and AI implementation has many benefits when it comes to reinforcing cybersecurity. AI network tools analyze a huge volume of data — including network traffic and transaction data — and detect any unusual or anomalous activity that might indicate a cyber threat like unauthorized access.
Similarly, predictive maintenance tools monitor system performance data, and flags the possibility of hardware or software failures in advance, so preventative action can be taken. Some AI systems even feature self-healing capabilities, which can rectify minor issues without the need for human intervention.
The key to the successful implementation of AI in banking
Banks are investing in AI, but they’re also under considerable pressure to prove the return on those investments. The value of AI and analytics for global banking could reach as much as $1 trillion per year, but only 61% of banking professionals surveyed for our 2025 Process Optimization Report say they’re getting the expected ROI on their AI deployments (compared with 73% across all industries).
So what’s stopping the banking industry from reaping the benefits of its AI investment?
Simply put, the effectiveness of AI depends on the quality of the data and business context it receives. Right now, AI doesn’t have the input it needs to help banks achieve true operational excellence.
AI solutions need contextual knowledge to gain a deep understanding of a bank’s processes and the unique way the organization runs. These tools must have the visibility to see which systems individual processes sit within, understand how those processes interact, determine why anomalies occur, and decide what action should be taken.