What Does Agentic AI Mean for the Future of Financial Services?

The banking sector is undergoing one of its most profound shifts, moving decisively from traditional workflows to autonomous, agentic AI-driven operations. This transformation is not simply about digitising processes, it signifies a fundamental rewiring of financial product design, risk management, and revenue capture. For executives, the rise of agentic AI is compelling institutions to re-examine long-established paradigms and ready themselves for competition where intelligence and autonomy, rather than inertia, define success. 

Agentic AI: Operating Principles and Application in Finance 

Progressing from conventional rules-based automation, agentic AI introduces autonomous agents that learn, adapt, and execute financial decisions across the value chain. This new class of AI is enabling banks and SME lenders to streamline processes while maintaining compliance and customer trust. 

Key mechanisms in banking and SME finance: 

• Deposit Optimisation: Agents constantly scan market yields and automatically move funds to achieve the best returns, reducing "inertia margins" that banks have long relied on.  
  

• Autonomous Underwriting: SME loan decisions accelerated by AI-led document verification, risk scoring, and continuous portfolio monitoring, minimising delays and subjective bias.  
  

• Payment Flows: AI agents route payments based on cost, speed, and context, potentially eliminating traditional credit card and interchange models in favour of the lowest-cost rails.  
  

• Risk, Fraud and Compliance: Agents continuously scan for anomalies, ensure regulatory adherence, and produce audit-friendly logs for rapid response and transparency. 

  
  

These capabilities collectively shift large financial organisations from reactive, manual operating models to intelligent environments, where risk, speed, and resource allocation can be continuously balanced and optimised. 

Evidence of Scale: Market Adoption and Industry Impact 

Complex data and accelerated deployments across leading markets validate  Agentic AI's rise. Industry analysis reveals clear momentum that goes beyond pilot projects and theoretical projections. 

• The global payments pool surpasses US$2.7 trillion in annual revenue; agentic AI agents are set to compress up to 30% of margin through autonomous yield and fee optimisation. 
  

• By 2027, it's projected that 50% of financial firms deploying generative AI will have integrated agentic models, up from 25% in 2025.   

• Real-world impacts: Saudi Arabia's AI-enabled lending has increased SME credit by 600% over seven years, with decision times dropping from 49 days to near-instant approvals.  
  

• The agentic AI market in finance is projected to grow from US$2.1 billion in 2024 to US$81 billion by 2034, indicating rapid adoption at an enterprise scale.  

  
  

Adoption numbers such as these not only validate the business case for agentic AI but also hint at an emerging divide between digital leaders and laggards in a high-stakes market. 

Industry Proof: Real-World Deployments 

Focusing on factual implementations, several industry players are using agentic AI to drive business impact across deposits, lending, and payments. 

• Platforms like Raisin, an EU- and UK-focused financial technology company, aggregate US$80 billion in deposits using agent-based logic to optimise customer yield and liquidity.  
  

• Biz2X, a fintech company based in New York, U.S, leverages agentic tools for full-cycle SME lending automation, from application review to real-time fraud monitoring, reducing operational friction and improving credit outcomes.  
  

• Major payment networks are launching agent-based APIs, enabling autonomous payment rail selection that reduces costs and accelerates fulfilment speed.  

  
  

Taken together, these examples illustrate a growing ecosystem in which agentic decision logic is the new standard, a present necessity in leading operational environments. 

Strategic and Operational Implications for Leaders 

Leaders must now prepare their organisations for this agentic era, not only technologically but also in business design and workforce development. 

• Revenue models are shifting; traditional banking margins are threatened as AI drives product evolution and price transparency.  
  

• Compliance is becoming increasingly stringent, with regulations such as the EU AI Act mandating that high-risk systems provide explainability and auditable results.  
  

• Talent and organisational agility are vital; integrated teams and AI upskilling now take priority over siloed legacy practices.  
  

• Strategic partnerships between banks, technology firms, and regulators drive successful and responsible adoption.  
  

• Investment in digital inclusion and AI readiness ensures all stakeholders, including SMEs, benefit from the new paradigm.  

  
  

As financial leaders adapt, the competitive advantage will accrue to those organisations most able to leverage agentic orchestration, not only for efficiency gains but for innovation and resilience in an increasingly complex financial landscape. 

Governance, Talent, and Trust: Managing Risks 

Transforming at scale necessitates robust risk management and governance. Executives must fortify systems and talent pools to manage complex agentic architectures. 

• Persistent AI talent shortages, legacy data silos, and infrastructure hurdles impede adoption.  
  

• Trust and accountability are non-negotiable; transparent decision logic and responsible data management are essential for all agentic deployments.  
  

• Responsible AI must blend oversight, transparency, and bias mitigation as foundational principles.  

  
  

Institutions that anchor their transformation in these pillars will not only accelerate value but also ensure legitimacy and resilience as scrutiny and stakeholder expectations rise. 

Conclusion: A Strategic Imperative for Banking Innovation 

Agentic AI now sits at the heart of banking and SME finance reinvention. It has been proven that scalable deployments are establishing new performance frontiers, driving efficiencies, compressing margins, and fundamentally changing how organisations allocate capital, manage risk, and serve customers. For executive teams, the imperative is clear: success will hinge on visionary leadership that places agentic AI at the centre of strategy, commits to continuous upskilling, and rigorously embeds accountability and trust.  

In an era where the pace of change can quickly redraw market hierarchies, the organisations that become truly agentic by fusing intelligence, transparency, and adaptability will define the next chapter in financial innovation and industry leadership.