Cost transformation forces banks to innovate<\/strong><\/h4>\n<\/span><\/p>\n
European banks are navigating a complex landscape characterized by economic headwinds and cost pressures. The Eurozone economy will grow by approximately 1.5% in 2025<\/a>, which is modest compared to previous years. This will lead banks to tighten their operational efficiencies. They are now using technology as a lever to reduce costs and innovate.<\/p>\nHistorically, banks have faced high-cost pressures exacerbated by their legacy systems. According to S&P Global Ratings, operational costs for European banks increased by over 4% annually from 2021 to 2023<\/a>, emphasizing the need for effective cost management strategies.\u00a0To optimize costs, banks are reducing the number of applications and investing in technology that enhances customer experiences while maintaining efficiency. For instance, Deutsche Bank\u2019s<\/a> operational efficiency plan aims to achieve $2.8 billion in savings by streamlining processes, among other methods. Embracing new technologies allows banks to improve the customer experience whilst remaining cost-efficient.<\/p>\nAI as a catalyst for innovation<\/strong><\/h4>\nAI is emerging as a pivotal tool for driving innovation and transforming costs within banking operations. Of course, it demands an initial investment. Spending on AI in banking will rise from $21 billion in 2023 to $85 billion<\/a> in 2030. A strategic commitment to this technology helps banks rapidly increase efficiency and productivity. Potential long-term gains due to productivity improvements<\/a> are estimated at $200 billion to $340 billion annually<\/strong>.<\/strong><\/p>\nTo maximize AI\u2019s benefits, banks must adopt a pragmatic strategy that includes stakeholder buy-in and robust governance frameworks. This includes a commitment from the Supervisory and Executive Boards to ensure that all relevant stakeholders are aligned and a well-governed strategy is in place.<\/p>\n
For the technology to be most effective, it requires a strong data foundation. Once data is in place, banks start with an incubation phase where use cases are tested in a sandboxed environment. This allows banks to jump-start using AI in the bank and scale. Most often use cases that enhance productivity are the efficiency boosters. For example, data retrieval from annual reports for ESG purposes. Historically, this was a manual, time-consuming, and tedious job prone to errors. Using generative AI, the right data can be extracted, and the time can be brought down to minutes for scanning through multiple annual reports.<\/p>\n
Another area where AI is applied is in the contact centre. Historically, at the end of every call with a client, customer care professionals had to write a summary of the call manually. Now, through generative AI, all these calls are auto-summarised. This has an indirect bearing on customer experience. Auto-summarisation can help customer care professionals become 25% more productive<\/a>. For example, ABN Amro<\/a> uses generative AI at its contact centres to auto-summarize customer calls and improve productivity of customer care professionals. In another instance, <\/a>ING<\/a> developed a generative AI chatbot that offers customers real-time personalized responses in a responsible, guarded way. In the initial seven weeks since deployment, the bank helped 20% more customers avoid wait time. HSBC<\/a>, the global bank is working on over 550 AI use cases that include tackling money laundering, fighting fraud and supporting knowledge professionals with generative AI tools.<\/a><\/p>\nThe next rung on the complexity ladder is building voice bots and chatbots with the help of generative AI that can directly interact with customers. This helps reduce wait times and solves customer queries quicker, leading to a rise in a bank\u2019s net promoter score. This must be done by working with risk management and compliance with legal teams in a bank. Banks must embrace the technology but in a well-governed and compliant way.<\/p>\n
A commitment to governance<\/strong><\/h4>\nAs banks steadily climb the use case complexity ladder, a human needs to be in the loop. Robust governance is crucial for the responsible use of AI. Effective data governance protects data integrity, privacy, and security and ensures compliance with laws and regulations. AI governance requires human oversight to ensure fairness, accuracy, and compliance with standards. This helps promote responsible and ethical decision-making. A human-in-the-loop approach ensures active participation in developing and validating algorithms for accuracy and reliability.<\/p>\n
2025 will see the adoption of autonomous agents<\/strong><\/h4>\nThe end goal for banks is to help customers trigger transactions directly and automatically. While that has not yet been the case, in 2025, that could change. AI will be deeply integrated across the front, middle, and back offices to assist customers. Banks will work toward building AI agents \u2014 advanced software programs that observe their surroundings, process information, and autonomously take actions to achieve specific goals. Several agents can orchestrate complex workflows, solve problems, create and carry out plans, and use different tools. Think of them as knowledgeable digital assistants. Each agent works on a goal-oriented behaviour with adaptive decision-making. For example, in mortgages, AI can instantly analyse a customer\u2019s financial history and assist the loan officer in expediting the onboarding process. This helps improve the productivity of all stakeholders \u2014 from the front to the back office.<\/p>\n
The conversation around AI in financial services is transitioning from hype to reality. Banks must go beyond adopting standard use cases to make the most of AI. They must reimagine processes, transform operations, and shift to a federated data governance model \u2014 balancing centralised oversight with decentralised execution. This approach makes AI scalable, allowing business units to customise data practices without sacrificing consistency. But AI\u2019s impact goes beyond that \u2014 it accelerates innovation, speeds up development, and drives consistency across the bank. As AI shifts from a tool to an autonomous agent that makes decisions, delivers proactive insights and operates within set boundaries, banks must prepare their workforce for this new reality.<\/p>\n
About Author<\/h5>\n
Historically, banks have faced high-cost pressures exacerbated by their legacy systems. According to S&P Global Ratings, operational costs for European banks increased by over 4% annually from 2021 to 2023<\/a>, emphasizing the need for effective cost management strategies.\u00a0To optimize costs, banks are reducing the number of applications and investing in technology that enhances customer experiences while maintaining efficiency. For instance, Deutsche Bank\u2019s<\/a> operational efficiency plan aims to achieve $2.8 billion in savings by streamlining processes, among other methods. Embracing new technologies allows banks to improve the customer experience whilst remaining cost-efficient.<\/p>\n AI is emerging as a pivotal tool for driving innovation and transforming costs within banking operations. Of course, it demands an initial investment. Spending on AI in banking will rise from $21 billion in 2023 to $85 billion<\/a> in 2030. A strategic commitment to this technology helps banks rapidly increase efficiency and productivity. Potential long-term gains due to productivity improvements<\/a> are estimated at $200 billion to $340 billion annually<\/strong>.<\/strong><\/p>\n To maximize AI\u2019s benefits, banks must adopt a pragmatic strategy that includes stakeholder buy-in and robust governance frameworks. This includes a commitment from the Supervisory and Executive Boards to ensure that all relevant stakeholders are aligned and a well-governed strategy is in place.<\/p>\n For the technology to be most effective, it requires a strong data foundation. Once data is in place, banks start with an incubation phase where use cases are tested in a sandboxed environment. This allows banks to jump-start using AI in the bank and scale. Most often use cases that enhance productivity are the efficiency boosters. For example, data retrieval from annual reports for ESG purposes. Historically, this was a manual, time-consuming, and tedious job prone to errors. Using generative AI, the right data can be extracted, and the time can be brought down to minutes for scanning through multiple annual reports.<\/p>\n Another area where AI is applied is in the contact centre. Historically, at the end of every call with a client, customer care professionals had to write a summary of the call manually. Now, through generative AI, all these calls are auto-summarised. This has an indirect bearing on customer experience. Auto-summarisation can help customer care professionals become 25% more productive<\/a>. For example, ABN Amro<\/a> uses generative AI at its contact centres to auto-summarize customer calls and improve productivity of customer care professionals. In another instance, <\/a>ING<\/a> developed a generative AI chatbot that offers customers real-time personalized responses in a responsible, guarded way. In the initial seven weeks since deployment, the bank helped 20% more customers avoid wait time. HSBC<\/a>, the global bank is working on over 550 AI use cases that include tackling money laundering, fighting fraud and supporting knowledge professionals with generative AI tools.<\/a><\/p>\n The next rung on the complexity ladder is building voice bots and chatbots with the help of generative AI that can directly interact with customers. This helps reduce wait times and solves customer queries quicker, leading to a rise in a bank\u2019s net promoter score. This must be done by working with risk management and compliance with legal teams in a bank. Banks must embrace the technology but in a well-governed and compliant way.<\/p>\n As banks steadily climb the use case complexity ladder, a human needs to be in the loop. Robust governance is crucial for the responsible use of AI. Effective data governance protects data integrity, privacy, and security and ensures compliance with laws and regulations. AI governance requires human oversight to ensure fairness, accuracy, and compliance with standards. This helps promote responsible and ethical decision-making. A human-in-the-loop approach ensures active participation in developing and validating algorithms for accuracy and reliability.<\/p>\n The end goal for banks is to help customers trigger transactions directly and automatically. While that has not yet been the case, in 2025, that could change. AI will be deeply integrated across the front, middle, and back offices to assist customers. Banks will work toward building AI agents \u2014 advanced software programs that observe their surroundings, process information, and autonomously take actions to achieve specific goals. Several agents can orchestrate complex workflows, solve problems, create and carry out plans, and use different tools. Think of them as knowledgeable digital assistants. Each agent works on a goal-oriented behaviour with adaptive decision-making. For example, in mortgages, AI can instantly analyse a customer\u2019s financial history and assist the loan officer in expediting the onboarding process. This helps improve the productivity of all stakeholders \u2014 from the front to the back office.<\/p>\n The conversation around AI in financial services is transitioning from hype to reality. Banks must go beyond adopting standard use cases to make the most of AI. They must reimagine processes, transform operations, and shift to a federated data governance model \u2014 balancing centralised oversight with decentralised execution. This approach makes AI scalable, allowing business units to customise data practices without sacrificing consistency. But AI\u2019s impact goes beyond that \u2014 it accelerates innovation, speeds up development, and drives consistency across the bank. As AI shifts from a tool to an autonomous agent that makes decisions, delivers proactive insights and operates within set boundaries, banks must prepare their workforce for this new reality.<\/p>\nAI as a catalyst for innovation<\/strong><\/h4>\n
A commitment to governance<\/strong><\/h4>\n
2025 will see the adoption of autonomous agents<\/strong><\/h4>\n
About Author<\/h5>\n
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