AI is transforming the banking industry, but the expected profits and savings come at a very human cost with impacts on financial employment.
The report is a collaboration between Digital Bankzopa and Juniper Research, and estimates that the generator AI will provide £1.8 billion in cost savings by 2030. However, this 100% investment return comes at a massive human costs that puts an estimated 27,000 financial industry jobs at risk.
The findings suggest that AI technology is moving beyond experimental pilots and is deeply embedded in the bank’s core processes, from customer service to invisible back-office capabilities.
“We are committed to providing a range of services that are important to us,” said Peter Donlon, Zopa’s Chief Technology Officer.
“At Zopa, we operated machine learning for over a decade before LLM became mainstream, and the depth of experience shaped the belief that Genai is a fundamental ability, not a feature add-on. It’s rare for Zopa technicians to build completely new intelligence layers at a level that redefines the industry.”
Silent AI revolution in the bank’s back office
Customer-oriented chatbots and personalized app experiences often capture headlines, but the report reveals that the most dramatic impact of AI is occurring behind the scenes. 82% of all time saved through this technology, reaching 154 million hours by 2030, comes from back office operations.
These features include regulatory compliance, fraud detection, and risk management – traditionally are labor-intensive and extremely complex. AI is expected to automate the vast swath of this critical financial effort, helping everything from customer (KYC) checks to money laundering anti-money laundering (AML) monitoring.
The financial impact of AI on these back-office functions is immeasurable. Cost reductions are projected in the region alone, reaching £923 million per year by the end of the decade. It represents more than half of total savings across the sector.
This automation isn’t just about reducing costs. Regulations such as the authorized push payment (APP) fraud reimbursement rules that increase bank liability are becoming a competitive and economic need for AI to detect new fraud patterns in real time and reduce human error.
By automating daily checks and analysis, as we hear a lot about AI across the industry, this technology unlocks human experts. For the financial industry, these experts can focus their skills on the most complex investigations to improve both efficiency and effectiveness in fighting financial crime.
Superpersonalize your banking experience with AI
The motivating desire for hyperpersonalization in the financial industry is driving large investments in customer service AI. The report predicts that UK banks will pour more than £1.1 billion into AI for clients by 2030. This is the largest investment share in all segments.
This capital inflow for personalization is used to develop sophisticated virtual assistants and chatbots that can handle complex queries. We provide personalized financial advice and even look forward to the needs of our customers.
The goal is to move towards a truly conversational and intelligent interface, far beyond past rule-based bots. This shift is expected to bring great efficiency by 2030, save £540 million in operating costs and free up 26 million hours of human agent time each year. These employees can also be relocated to handle more complex, higher value interactions that require human touch.
Portfolio management is also set to make profits. Investments in the region are projected to grow to £145 million by 2030. Here, AI is positioned not as an alternative to human advisors, but as a powerful augmentation tool. It can integrate vast amounts of market data, simulate portfolio performance, automate daily reporting, and enable human experts to focus on decision-making and client relationships.
The impact of AI on financial employment
The increased efficiency provided by AI inevitably raises urgent questions about the future of the financial workforce. The report’s forecast that 27,000 roles could be replaced by 2030 is of concern. Customer service and back office positions are expected to bear the brunt of this change, with 14,000 and 10,000 jobs at risk, respectively.
However, the authors of the report suggest that this is not just a story of unemployment, but one of the fundamental redefine roles. Moving financial jobs around repetitive manual tasks creates opportunities for new positions to increase the banking workforce, focusing on AI governance, data strategies, and overseeing these complex automated systems.
Donlon emphasizes this point, viewing technological change as a catalyst for positive change. He says, “This investment leads to a one-time opportunity to reskill and rethink the workforce that drives our financial system.”
The challenge for the industry is proactively managing this transition. “More than anything, our aim is to equip banks, fintechs, regulators and policymakers with the insights they need to seize this historic moment in order to shape future work,” he said.
The report concludes with a clear warning to established agencies. A notable capacity gap has already emerged between the technologically sophisticated challenger banks building platforms around AI and the legacy banks that are hindered by older systems.
Nick Maynard, Vice President of Fintech Market Research at Juniper Research, commented: “The UK banking sector is at a turning point and is set up to restructure how banking operations work fundamentally, and Gunai creates risk and opportunities.
“Digital-only brands like Zopa have already gained deep AI and deep experience in their operations and are less susceptible to this shift. That’s why digital banks and their experience are important to lead the banking market through this revolution.”
For the high street banking giant, the message is clear. The risk of adapting to the AI revolution or losing relevance to the financial industry as redefines by efficiency, personalization and intelligent automation.
reference: Gen AI makes no economic difference in 95% of cases
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