Cost transformation obliges banks to innovate
European banks are sailing in a complex landscape characterized by wind -opposite winds and costs on costs. The economy of the euro zone will increase by approximately 1.5% in 2025, which is modest compared to previous years. This will lead the banks to tighten their operational efficiency. They now use technology as a lever to reduce costs and innovate.
Historically, banks have faced high cost pressures exacerbated by their inherited systems. According to S&P Global Ratings, the operational costs of European banks have increased by more than 4% per year, from 2021 to 2023, stressing the need for effective cost management strategies. To optimize costs, banks reduce the number of applications and invest in technology that improves customer experiences while maintaining efficiency. For example, the Deutsche Bank operational efficiency plan aims to achieve $ 2.8 billion in savings by rationalizing processes, among other methods. The adoption of new technologies allows banks to improve customer experience while remaining profitable.
AI as an innovation catalyst
AI emerges as a pivotal tool to cause innovation and transform costs within banking operations. Of course, it requires an initial investment. IA expenditure in Banks will drop from $ 21 billion in 2023 to 85 billion dollars in 2030. A strategic commitment to this technology helps banks quickly increase efficiency and productivity. Long -term potential gains due to productivity improvements are estimated from $ 200 billion to $ 340 billion per year.
To maximize the advantages of AI, banks must adopt a pragmatic strategy which includes the support of stakeholders and robust governance executives. This includes a commitment of supervisory and management councils to ensure that all the stakeholders concerned are aligned and that a well -adjusted strategy is in place.
In order for technology to be the most effective, it requires a solid data foundation. Once the data in place, the banks start with an incubation phase where use cases are tested in a sand environment. This allows banks to restart using AI in the bank and the scale. The use cases that improve productivity are efficiency boosters. For example, recovery of annual reports data for ESG purposes. Historically, it was a manual, long and tedious work subject to errors. Using a generative AI, the right data can be extracted and time can be brought back to a few minutes to travel several annual reports.
Another area where AI is applied is in the contact center. Historically, at the end of each call with a customer, customer professionals had to write a summary of the call manually. Now, via a generative AI, all these calls are self-employed. This has an indirect impact on the customer experience. Automatic assummarization can help customer professionals become 25% more productive. For example, ABN AMRO uses a generative AI in its contact centers to automatically write customer calls and improve the productivity of customer professionals. In another case, ING has developed a generative AI chatbot which offers customers personalized responses in real time in a responsible and guarded manner. During the first seven weeks following the deployment, the bank helped 20% to additional customers to avoid waiting time. HSBC, the World Bank works On more than 550 cases of use of AI which include the fight against money laundering, the fight against fraud and support of knowledge professionals with generative AI tools.
The next level on the complexity scale is to build vocal robots and chatbots using a generative AI that can interact directly with customers. This helps reduce waiting times and resolve customer requests faster, which has led to an increase in the net promoter score. This must be done by working with risk management and compliance with legal teams in a bank. Banks must adopt technology but in a well -regulated and conforming way.
A commitment to governance
While banks regularly climb the complexity of the use case, a human must be in the loop. Robust governance is crucial for responsible use of AI. Effective data governance protects data integrity, confidentiality and security and guarantees compliance with laws and regulations. AI governance requires human surveillance to guarantee equity, precision and compliance with standards. This helps promote responsible and ethical decision -making. A human approach in a loop ensures active participation in the development and validation algorithms of precision and reliability.
2025 will see the adoption of autonomous agents
The final objective of the banks is to help customers trigger transactions directly and automatically. Although this has not yet been the case, in 2025, this could change. The AI will be deeply integrated on the front, middle and rear offices to help customers. Banks will endeavor to build AI agents – advanced software that observe their environment, processing information and take independently to achieve specific objectives. Several agents can orchestrate complex work flows, solve problems, create and make plans and use different tools. Consider them as competent digital assistants. Each agent works on behavior focused on objectives with adaptive decision -making. For example, in mortgages, AI can instantly analyze a client’s financial history and help the loans to speed up the integration process. This improves the productivity of all stakeholders – from front to back office.
The conversation around AI in financial services goes from media to reality. Banks must go beyond the adoption of standard use cases to make the most of AI. They must reinvent processes, transform operations and move to a federated data governance model – balancing centralized surveillance with decentralized execution. This approach makes the AI evolutionary, allowing commercial units to personalize data practices without sacrificing consistency. But the impact of the AI goes beyond that – it accelerates innovation, accelerates development and stimulates consistency through the bank. While AI goes from an autonomous agent to make decisions, provides proactive information and operates within fixed limits, banks must prepare their workforce for this new reality.
About the author
Vice -president and sales manager – Financial services, EMEA
Country Co-Head, United Kingdom
Limited Infosys
Manish is vice-president and sales manager at Infosys for Financial Services (FS) EMEA. He is also co-chief of UK info and member of the regional leadership council EMEA.
Its expertise extends to digital, technology and outsourcing. He is a proven leader in sales, strategy, management of large P&Ls and commercial development, recognized to stimulate growth through strategic partnerships and avant-garde vision. Manish has helped some of the largest financial services organizations to sail in complexity, to take advantage of new technologies and reflections to generate business results.
In addition, it focuses on incubation and the Pioneer of the British market for public sector activities and the world’s fintech at Infosys.
Manish is a mechanical engineer with an MBA from Jamnalal Bajaj Institute, Mumbai.
