Strategic Intelligence: Agentic AI in Banking
Description
Strategic Intelligence: Agentic AI in Banking
Summary
This report starts with a simple and inclusive definition of agentic AI, which captures the notion of a general evolution toward more autonomous capabilities, rather than strict qualifying criteria. It then maps out the key gridlines of change across technology, regulation, macroeconomic, and social trends, which will shape the evolution of agentic AI over the next 24 months. The report shares executive survey data to map out where tech decision-makers at banks are prioritizing investments, both across different technologies and within AI. It explores how early adopters have delivered the most business impact, reviewing organizational models around go-to-market and implementation. The report concludes with firm-level summaries of financial services providers and technology partners, summarizing their competitive position within the theme.
AI is a broad, longstanding, and dynamic category of tech disruption within financial services. For many years, machine learning algorithms were the central focus of analysis, helping refine various aspects of banking, such as credit risk, fraud detection, and product recommendations. More recently, generative AI (GenAI) has exploded into the mainstream, and that trajectory toward more autonomous tools has led to agentic AI becoming the next logical step in the overarching path toward artificial general intelligence. This is still some way off, but market participants are beginning to develop their GenAI workflows, systems, processes, and governance with a view to agentic AI being the next evolution-building in relevant capabilities and understanding so they can supercharge efforts in the coming years.
Scope
Summary
This report starts with a simple and inclusive definition of agentic AI, which captures the notion of a general evolution toward more autonomous capabilities, rather than strict qualifying criteria. It then maps out the key gridlines of change across technology, regulation, macroeconomic, and social trends, which will shape the evolution of agentic AI over the next 24 months. The report shares executive survey data to map out where tech decision-makers at banks are prioritizing investments, both across different technologies and within AI. It explores how early adopters have delivered the most business impact, reviewing organizational models around go-to-market and implementation. The report concludes with firm-level summaries of financial services providers and technology partners, summarizing their competitive position within the theme.
AI is a broad, longstanding, and dynamic category of tech disruption within financial services. For many years, machine learning algorithms were the central focus of analysis, helping refine various aspects of banking, such as credit risk, fraud detection, and product recommendations. More recently, generative AI (GenAI) has exploded into the mainstream, and that trajectory toward more autonomous tools has led to agentic AI becoming the next logical step in the overarching path toward artificial general intelligence. This is still some way off, but market participants are beginning to develop their GenAI workflows, systems, processes, and governance with a view to agentic AI being the next evolution-building in relevant capabilities and understanding so they can supercharge efforts in the coming years.
Scope
- When Celent (part of GlobalData) surveyed bank executives on their top technology priorities for 2025, “artificial intelligence and advanced data analytics” was ranked first by the highest number of respondents.
- Reinforcement learning is the prominent technique enabling AI agents to make decisions based on feedback from their environment. By learning through experience, AI agents are not restricted to following explicit or pre-programmed instructions for each scenario they are faced with; they can adapt to new situations independently.
- Between 2024 and 2025, the global proportion of consumers who had fallen victim to fraud in the preceding three years rose from 18% to 28%. Agentic AI capabilities are already being leveraged in a diverse range of fraud detection and mitigation scenarios to respond to this trend.
- GlobalData’s Strategic Intelligence is a single, integrated global research platform that provides an easy-to-use framework for tracking all themes across all companies in all sectors.
- This report is essential reading for senior executives at financial services companies seeking to understand how to reassess and potentially recalibrate AI efforts in accordance with evolving market conditions.
Table of Contents
62 Pages
- Executive Summary
- Players
- Technology Briefing
- Agentic AI is a significant evolution
- Trends
- Technology trends
- Macroeconomic and social trends
- Regulatory trends
- Industry Analysis
- Bank agentic AI deployment categories
- Top technology priorities
- Digital channels (customer service)
- Fraud prevention
- Personalized experiences
- Real-time payments
- Software development and core banking modernization
- IT security and resilience
- Timeline
- Value Chain
- Prioritizing agentic AI processes
- Task complexity
- Multi- or single-agent systems
- Task business value
- Implementation considerations
- Change management
- Management buy-in and talent
- Employee understanding
- Cost
- Frameworks
- Governance and model development
- Privacy
- Explainability
- Reliability
- Real-time performance
- Stakeholder awareness
- Ongoing improvements
- Task redesign
- Companies
- Financial services providers
- Tech providers
- Established financial services vendors with AI enhancements
- Agentic AI startups
- Sector Scorecards
- Banking sector scorecard
- Who’s who
- Thematic screen
- Valuation screen
- Risk screen
- Glossary
- Further Reading
- GlobalData reports
- Our Thematic Research Methodology
- About GlobalData
- Contact Us
- List of Tables
- Table 1: Technology trends
- Table 2: Macroeconomic and social trends
- Table 3: Regulatory trends
- Table 4: Financial services providers
- Table 5: Tech providers
- Table 6: Established financial services vendors with AI enhancements
- Table 7: Agentic AI startups
- Table 8: Glossary
- Table 9: GlobalData reports
- List of Figures
- Figure 1: Who are the leading players in the agentic AI theme, and where do they sit in the value chain?
- Figure 2: AI assistants to AI agents
- Figure 3: Most bank executives rank AI and advanced data analytics as their number one priority
- Figure 4: Most bank executives prioritise AI investment to improve digital channels
- Figure 5: 28% of global consumers have fallen victim to fraud, with the UAE and Singapore the most impacted
- Figure 6: Impersonation fraud continues to lead, but consumers are vulnerable to multiple attack vectors
- Figure 7: Net satisfaction with personalization is lower than most other dimensions of experience
- Figure 8: North American and European banks spend less on AI and analytics, with Asia-Pacific leading the way
- Figure 9: Over 79% of the global population have now used real-time payment systems, with usage highest in the Middle East and Africa
- Figure 10: Across industries, developers only spend a fraction of their time on actual coding
- Figure 11: The AI story
- Figure 12: Key considerations when delivering agentic AI
- Figure 13: Agentic AI deployment will get progressively more complicated
- Figure 14: Key considerations when prioritizing agentic task-flows
- Figure 15: Who does what in the Banking space?
- Figure 16: Thematic screen
- Figure 17: Valuation screen
- Figure 18: Risk screen
- Figure 19: Our five-step approach for generating a sector scorecard
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