AI in Banking: Driving Efficiency and Innovation

As we approach 2025, AI and machine learning are poised to continue evolving the banking sector, as leading financial players seek to enhance operational efficiency, improve customer engagement and offer innovative financial solutions.

While the legacy banking industry has long been seen as lagging behind digital competitors – chiefly newly-formed neobanks – Scott Hofmann, Chief Revenue Officer, US at GFT, believes that banks' AI and machine learning deployments will mature in three main areas in 2025: fraud detection; customer service; and risk and compliance.

“Banks will scale their ability to scan transactions for suspicious activity in real time, at a rate that would be nearly impossible to accomplish manually,” Scott explains. 

This real-time scanning capability represents a significant leap forward in banks' ability to protect customers and themselves from fraudulent activities.

By leveraging AI and machine learning algorithms, banks can analyse vast amounts of transaction data instantaneously, identifying patterns and anomalies that might indicate fraudulent behaviour. 

Prashant Jajodia, Managing Partner & Financial Services Sector Lead at IBM, emphasises the impact of generative AI on fraud detection.

“By analysing massive amounts of transaction data, AI can identify unusual activity and flag potential fraud before it becomes a bigger problem,” he explains. 

This proactive approach can save banks significant amounts of money and protect customers from financial harm.

The rise of generative AI in banking

AI has, of course, been deployed by banks for some time, but the rise of Gen AI is spurring transformative change. 

Ryan Cox, Co-Head of AI at Synechron, says: “Banks are already using Gen AI to improve machine learning algorithms, but, in 2025, it will become an even more significant focus. 

The use of GenAI to create synthetic data is a game-changer for banks, particularly in areas where real-world data may be limited or difficult to obtain.

For example, in modelling rare financial events or testing new products, Gen AI can generate realistic, diverse datasets that allow banks to train their algorithms more effectively and comprehensively.

Prashant highlights the top three use cases for Gen AI in financial services: customer service, risk management and software development. 

“Large Language Models (LLMs) can perform tasks such as summarisation, content generation, classification, semantic search, code generation and extraction, with estimates suggesting a 40% productivity gain in many of these areas,” he reveals.

Streamlining risk management and compliance

In the realm of risk and compliance, Scott anticipates a shift toward AI-powered regulatory reporting: “We'll see more banks and financial institutions applying AI to streamline reporting to regulators.

“Tackling this will require banks to provide clean, consolidated data to their AI systems, but once this data is in place, they'll be able to ensure compliance across all of their products and avoid hefty fines for any reporting errors.”

The implementation of AI in regulatory reporting represents a significant opportunity for banks to reduce costs and improve accuracy. 

By automating the process of gathering, analysing and reporting regulatory data, banks can not only save time and resources but also minimise the risk of human error. 

This is particularly important in an increasingly complex regulatory environment, where the consequences of non-compliance can be severe.

Transforming customer service and engagement

Customer service is another area where AI is expected to make significant strides. 

Scott predicts that banks will continue to bring their behind-the-scenes AI work to the forefront of customer interactions. 

“Using AI to power customer service chatbots and automate responses to common questions, banks will enable customer service employees to dedicate their time to solving more complex issues that require human interaction,” he says.

This shift towards AI-powered customer service has the potential to improve response times and provide 24/7 support to customers. 

By handling routine inquiries and tasks, AI chatbots can free up human customer service representatives to focus on more complex issues that require critical thinking and personalised attention.

Viren Patel, Financial Services Industry Strategist at Workday, predicts that banks will increasingly use advanced Natural Language Processing (NLP) and GPTs to accelerate and grow their customer service capabilities. 

“At the frontline, banks will increasingly come to use advanced Natural Language Processing (NLP) and GPTs to accelerate and grow their customer service capabilities,” Viren says.

The use of NLP and GPTs in customer service represents a significant leap forward in banks' ability to understand and respond to customer queries. 

Boosting operational efficiency

The potential for AI to significantly boost productivity in the banking sector is widely recognised.

Prashant cites research from the IBM Institute of Business Value that predicts AI will add a 14% increase to global GDP by 2030, equivalent to a growth of US$15.7tn.

Ryan highlights the potential of AI to take over mundane tasks, freeing up data science teams to focus on more complex problems. 

“AI will take over more mundane tasks, like cleaning data,” he says. “Data science teams in banks, freed from monotony, will be able to focus on improving their algorithms.”

This shift in focus has the potential to accelerate innovation in the banking sector, as highly-skilled data scientists can dedicate more time to developing and refining sophisticated AI models that drive business value.

Viren emphasises that AI's impact extends beyond customer-facing applications: “It can boost real-time decision-making for leaders – for example by highlighting skill gaps within the organisation – alongside enabling better financial planning tools.”

This internal application of AI can help banks operate more efficiently, making better-informed decisions about resource allocation and strategic planning.

Modernising legacy systems

Prashant points out that AI is playing a crucial role in modernising legacy systems in banks and insurance companies. 

“IBM is successfully using Gen AI to reverse engineer and modernise these legacy platforms,” he says. 

“For a building society in the UK, we are modernising the core mortgage platform. Gen AI is helping our software developers by reading the code of the mortgage platform and generating business logic, translating the code to modern languages and testing the application.”

This application of AI in modernising legacy systems represents a significant opportunity for banks to update their technology infrastructure more efficiently and cost-effectively. 

Prashant notes that, in many cases, this can “reduce the time and cost of modernising legacy platforms by more than 50%, thus improving the ROI on such programmes”.

While the potential benefits of AI in banking are significant, experts caution that its adoption comes with challenges. 

“AI promises enormous benefits and much positive change but that won't happen if those who are supposed to benefit from the technology don't trust it,” Viren warns.

This issue of trust is particularly crucial in the financial services sector, where banks are the custodians of sensitive customer information. 

Viren cites a 2024 Workday report which found that only 52% of workers trust AI will be deployed responsibly. 

Prashant echoes this sentiment, highlighting the need for strong governance in AI adoption. 

He says: “Strong governance is central to building trusted AI, especially in financial services. It’s crucially important to understand what AI models the organisation has, the data the organisation tunes and applies those models to, the models' intended uses and their compliance with regulations.

“At least five countries have AI regulations and two-thirds of the world's countries have privacy and data governance laws.” 

The future of AI in banking

As AI continues to transform the financial services industry, our experts agree that its impact will be far-reaching. 

From enhancing customer service and improving fraud detection to streamlining regulatory compliance and boosting operational efficiency, AI is set to revolutionise every aspect of banking.

Ryan predicts that AI will facilitate more advanced Open Banking ecosystems, “allowing data to be shared between banks and third-party providers. This should add innovation and insights on customer data more holistically than it is today, where a person's financial data is split across several organisations”. 

This development has the potential to provide customers with more comprehensive and personalised financial services, as banks and other financial institutions gain a more complete picture of their customer's financial lives.

However, as Prashant points out, the success of AI in banking ultimately depends on people. 

“The industry needs to invest in training people on AI,” he says. “Putting this technology into the hands of users, across all functions and lines of business—not just technology users—allows everyone to understand how transformative it can be to their role and the workflows around them.”

As we head into 2025, it’s clear the future of banking will be shaped by those institutions that can successfully harness the power of AI while maintaining the human touch that customers value. 

Prashant concludes: “As AI continues to transform the financial services industry, the challenge lies in upskilling people – many of whose jobs will dramatically change.”

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