Introduction: The Transformational Potential of Agentic AI in Banking

The global banking industry is poised for a profound transformation, driven by the adoption of Agentic AI—autonomous software agents capable of handling complex workflows, learning from data, and adapting to changing conditions. As the financial landscape becomes increasingly competitive, the ability to leverage AI Agents is becoming a critical differentiator for banks seeking to enhance efficiency, manage risks, and deliver superior customer experiences.

This blog outlines how Agentic AI offers a compelling opportunity for banks to improve operational efficiency and financial performance. By automating complex finance, risk, compliance, and internal reporting processes, banks can achieve significant cost savings and improve key financial metrics such as the Cost-to-Income Ratio (CIR) and Return on Equity (RoE).

Disclosure: This blog was created based on a conversation (or series of prompts) with Revvy, a ChatGPT developed by Revvence and trained on finance transformation and systems change-related content.

Jump to Section

1. What Makes Agentic AI Revolutionary
2. Why Banks Should Act Now
3. What Are AI Agents?
4. How Do AI Agents Work?
5. The Business Case for Agentic AI in Banking
6. Call to Action: Begin Your Agentic AI Journey

What Makes Agentic AI Revolutionary?

According to McKinsey, Agentic AI and related technologies, such as generative AI, could add between $200 billion and $340 billion annually to the banking sector.

This value stems from dramatic improvements in:

1. Cost Efficiency: AI Agents can reduce operational costs by up to 20%, automating tasks that traditionally require significant manual effort, such as compliance checks, fraud detection, and customer onboarding.
2. Revenue Generation: Hyper-personalised customer interactions powered by AI Agents lead to increased cross-selling opportunities, greater customer retention, and higher lifetime value.
3. Risk Management: Real-time monitoring, predictive analytics, and proactive issue resolution minimise financial losses due to fraud or market volatility.
4. Regulatory Compliance: AI Agents ensure banks can adapt quickly to changing regulatory landscapes by automating complex reporting and policy alignment tasks.

Accenture emphasises that banks embracing Agentic AI are better positioned to meet the demands of the modern financial ecosystem. These institutions can scale their operations without proportional increases in cost, allowing them to compete more effectively in a challenging market. Moreover, Agentic AI enables banks to pivot from reactive problem-solving to proactive, predictive decision-making, fundamentally altering their operations.

Why Banks Should Act Now

The banking sector is under immense pressure to improve its cost-to-income ratio, enhance customer experiences, and mitigate risks in an increasingly volatile environment. Agentic AI addresses these challenges head-on:

• Enhanced Efficiency: AI Agents automate routine workflows, such as loan processing, compliance checks, and customer support, allowing human employees to focus on strategic, value-added tasks.
• Improved Decision-Making: through advanced machine learning and real-time analytics, AI agents provide actionable insights that empower banks to respond faster to market changes.
• Scalability: Banks can scale operations to handle growing transaction volumes and customer demands without significant cost increases.

For example, a global retail bank that deployed AI Agents to handle customer inquiries and fraud detection saw a 50% reduction in response times and a 30% drop in operational costs within the first year. These results underscore the transformative potential of this technology.

What Are AI Agents?

AI Agents are intelligent software entities designed to execute tasks, solve problems, and make decisions autonomously. Unlike static, rule-based programs, AI Agents are dynamic and adaptive. They can analyse data, understand context, and interact with users or other systems to achieve specific goals. AI agents excel in environments where workflows are complex and multifaceted and require continuous learning or decision-making.

Key Capabilities of AI Agents

1. Autonomy: Operate independently, requiring minimal human intervention.
2. Task Decomposition: Break down large workflows into manageable subtasks, executing them sequentially or in parallel.
3. Collaboration: Interact with other agents or systems to delegate tasks and share information.
4. Goal-Oriented Behavior: Focus on achieving defined outcomes, such as reducing fraud, optimising liquidity, or enhancing customer experiences.

Autonomous Task Decomposition and Collaboration

AI Agents go beyond simple task automation by autonomously managing workflows. This approach involves:

• Analysing Objectives: Understanding the overarching goal and the necessary steps to achieve it.
• Breaking Down Workflows: Decomposing the objective into smaller tasks, such as data collection, analysis, validation, and reporting.
• Prioritising Actions: Sequencing tasks based on dependencies, urgency, or business logic.
• Collaborating with Other Agents: Delegating specialised tasks, sharing insights, or dynamically adjusting workflows.

Example: Multi-Agent Loan Processing

In a loan approval scenario, a set of AI Agents might collaborate as follows:

1. A data aggregation agent retrieves the applicant’s financial data from internal and external sources.
2. A credit scoring agent applies predictive analytics to assess creditworthiness.
3. A risk assessment agent evaluates potential risks, such as income instability or market volatility.
4. A decision agent compiles the findings and approves or denies the loan, providing a rationale for regulatory compliance.
5. If approved, a customer engagement agent creates personalised loan terms and next steps for the applicant.

Each agent focuses on its domain expertise, working together to complete the end-to-end process autonomously, accurately, and efficiently.

Multi-Agent Collaboration

In scenarios where workflows are highly complex or span multiple domains, multiple AI Agents collaborate to achieve shared objectives. This collaboration is critical to how AI Agents scale efficiently and operate dynamically in real-world environments.

Key Features of Multi-Agent Collaboration

1. Task Delegation: Agents can assign specialised tasks to others. For example, a compliance agent detecting a suspicious transaction might delegate a deeper investigation to a fraud detection agent.
2. Information Sharing: Agents exchange data to consistently understand the workflow’s status. For instance, a treasury agent managing liquidity might share cash flow predictions with a risk management agent to ensure alignment.
3. Dynamic Adjustment: If one agent encounters a bottleneck, others can reallocate resources or reprioritise tasks to prevent delays.

Example: Multi-Agent AML Monitoring

In anti-money laundering (AML) operations, a multi-agent system might work as follows:

1. A transaction monitoring agent flags an unusual transfer based on predefined thresholds and historical patterns.
2. A risk assessment agent evaluates the transaction against known fraud scenarios and assigns a risk score.
3. A compliance agent generates a suspicious activity report (SAR) and sends it to the relevant regulator.
4. If necessary, a customer interaction agent contacts the account holder for verification.

This collaborative process ensures that the bank addresses the issue quickly and comprehensively, with each agent contributing its specialised expertise.

How Users Interact with AI Agents

The way users engage with AI Agents depends on their purpose and context. In banking, interactions are tailored to specific use cases and can occur in multiple ways:

Prompting

Users provide specific instructions or queries in natural language, which the agent interprets and acts upon. Prompting is ideal for situations requiring detailed or customised outputs.

Example: A treasury manager prompts an agent:

👱 “Generate a liquidity forecast for the next quarter, considering current market conditions and projected inflows.”

The agent analyses cash flow data, applies predictive models, and delivers a detailed forecast with actionable recommendations.

Conversational Interfaces

Conversational AI tools, such as chatbots or voice assistants, often serve as the interaction layer for AI Agents, enabling users to access agent capabilities in a familiar, user-friendly manner.

However, unlike standalone chatbots, conversational interfaces connected to AI Agents:

• Enable deeper, goal-oriented interactions.
• Allow the agent to complete complex, multi-step tasks in the background.
• Provide dynamic, context-aware responses based on real-time analysis.

Example: AI Agent-Powered Customer Interaction

A customer asks via the bank’s app:

👱 “Can I afford a £50,000 loan over five years?”

The agent performs the following tasks autonomously:

1. Analyses the customer’s financial data, including income, expenses, and credit score.
2. Considers external factors, such as interest rates and market trends.
3. Generates loan scenarios, calculating monthly payments, interest costs, and risk profiles.
4. Presents tailored advice through the conversational interface
   
   

🤖 “You can afford a £50,000 loan at 4.5% APR. Monthly repayments would be £931. Would you like to proceed or explore other options?”

The conversational interface makes the interaction seamless, but the AI Agent executes the complex analysis behind the scenes.

ChatBots vs. AI Agents: Key Differences

While conversational interfaces resemble chatbots like ChatGPT, the underlying technology and scope of AI Agents are far more advanced:

• ChatBots: Primarily reactive tools designed to interpret and respond to user queries. They are ideal for simple interactions, such as answering FAQs or retrieving basic information, but lack the ability to perform autonomous, multi-step workflows.
• AI Agents: Task-driven systems capable of executing complex workflows, breaking them into smaller tasks, collaborating with other agents, and making decisions. They can use conversational interfaces as one interaction method but are not limited to them.

ChatBot + Agents Coexistence:

A bank might use a chatbot for quick compliance queries from a finance team member, such as:

👱 “What’s the current status of our CSRD reporting submission?”

For more complex tasks, the chatbot can escalate the interaction to an AI Agent:

👱 “Can you generate a draft CSRD report for Scope 3 financed emissions, including insights on data gaps and compliance with EU Taxonomy?”

Here, the AI Agent takes over, aggregating data from various systems, applying sustainability metrics, and generating a detailed draft report. The agent can identify areas where data quality or completeness may impact compliance and offer recommendations to address gaps. The chatbot then presents the draft report and actionable insights to the finance team, streamlining the reporting process and enhancing compliance readiness.

How Do AI Agents Work?

AI Agents combine advanced technologies to execute complex workflows, make decisions, and adapt dynamically to achieve predefined outcomes. While the underlying technology is sophisticated, modern tools like Oracle's Generative AI Agents have made designing and managing AI Agents significantly more user-friendly, ensuring banking executives and teams can achieve their goals without deep technical expertise.

Designing AI Agents to Achieve Outcomes

The power of AI Agents lies in their ability to be custom-designed for specific banking functions. Users collaborate with technology teams to define objectives, design workflows, and configure the agent’s decision-making logic, leveraging intuitive tools provided by modern AI platforms.

Steps to Design Effective AI Agents

Designing effective AI Agents requires close collaboration between business teams and their technical colleagues.

1. Define Objectives: Start by identifying clear, measurable goals. For example:
   
   • Fraud Detection Agent: Reduce fraud-related losses by 30% within 12 months.
   • Compliance Agent: Automate 90% of regulatory reporting processes.
2. Map the Workflow:
   • Break down the workflow into specific tasks the agent will perform. For example, in fraud detection, tasks might include transaction monitoring, risk scoring, and anomaly reporting.
3. Train the Agent:
   • Historical data trains machine learning models embedded in the agent, enabling it to identify patterns, detect anomalies, and make accurate predictions.
4. Integrate with Core Systems:
   • Embed the AI Agent within existing systems to ensure seamless operation alongside traditional workflows.
5. Test and Iterate:
   • Simulate real-world scenarios to test the agent’s accuracy and efficiency. Based on the test results, refine workflows and models.

Key Components of an AI Agent Solution

An AI Agent’s effectiveness depends on its architecture, which includes the following critical components:

Data Integration Layer

• Purpose: Aggregates data from multiple sources, such as core banking systems, customer databases, external APIs, and regulatory feeds.
• Key Functionality: Ensures data consistency, accuracy, and accessibility.
• Example Use Case: A compliance agent collects transaction histories, customer profiles, and external fraud intelligence to detect suspicious activity.
  
  

Machine Learning Models

• Purpose: Drive the agent’s ability to predict outcomes, classify risks, and recommend actions.
• Key Functionality: Pre-trained models for common banking scenarios (e.g., credit scoring, AML) and customisable models trained on proprietary data.
• Example Use Case: A risk management agent predicts potential loan defaults by analysing borrower behaviour and macroeconomic conditions.
  
  

Natural Language Processing (NLP)

• Purpose: Enables the agent to interpret and generate human-like text or speech for seamless communication.
• Key Functionality: Powers conversational interfaces, document analysis, and context-aware responses.
• Example Use Case: A compliance agent reviews regulatory documents, extracts relevant rules, and flags inconsistencies in internal policies.
  
  

Orchestration Engine

• Purpose: Manages the workflow, ensuring tasks are executed in the correct sequence and with optimal resource allocation.
• Key Functionality: Prioritises actions, handles dependencies, and facilitates multi-agent collaboration.
• Example Use Case: In customer onboarding, the engine coordinates document verification, credit scoring, and AML checks in parallel.
  
  

Decision Framework

• Purpose: Provides the logic and rules for the agent to make autonomous decisions.
• Key Functionality: Combines business rules, predictive analytics, and real-time insights.
• Example Use Case: A treasury agent recommends fund allocations to maintain liquidity buffers while meeting operational needs.
  
  

Security and Compliance Framework

• Purpose: Ensures data handling and decision-making comply with regulatory requirements and organisational security policies.
• Key Functionality: Includes encryption, access controls, audit logs, and explainable AI features.
• Example Use Case: A fraud detection agent anonymises customer data during analysis to comply with GDPR.
  
  

Feedback and Learning Mechanisms

• Purpose: Allow the agent to continuously improve performance by learning from interactions and outcomes.
• Key Functionality: Incorporates feedback loops to refine models and workflows over time.
• Example Use Case: A customer service agent learns to prioritise high-value customers based on historical satisfaction data.

Learning by Observing Human Actions

Modern AI Agents can also learn by observing user behaviour, such as mouse movements, keyboard actions, or workflow patterns. This technique, known as imitation learning, enables agents to replicate human workflows and automate repetitive tasks.

OpenAI refers to this type of agent as a Computer-Using Agent (CUA) and has released a product called Operator, which can perform tasks on the web after being trained by humans.

How It Works:

Observation Phase: The agent monitors and records user actions as they interact with software systems, such as navigating banking applications, processing customer applications, or completing regulatory reporting tasks.

Pattern Recognition: Using AI and machine learning algorithms, the agent analyses recorded actions to identify patterns, workflows, and dependencies within the user’s behaviour. It learns which inputs, sequences, and decision points drive specific outcomes.

Automation: Once trained, the agent autonomously replicates these tasks, executing them consistently and efficiently. Over time, it can refine its approach, optimising processes to improve speed, accuracy, and resource utilisation.

Example: Automating Impairments Reporting with a Computer-Using Agent

Scenario: A bank's finance team needs to prepare a monthly impairments report for regulatory compliance, incorporating data from multiple systems, including loan management, risk analytics, and external economic indicators.

How the Computer-Using Agent Works:

1. Observation Phase: The agent observes a financial analyst as they manually gather data from various sources, including downloading loan book data, accessing external economic reports, and inputting figures into the impairments reporting template.
2. Pattern Recognition: Using machine learning algorithms, the agent identifies the repetitive steps involved in the process—such as logging into different systems, extracting specific data sets, applying accounting rules for impairments, and formatting the final report according to regulatory standards.
3. Automation: Once trained, the agent autonomously replicates the entire process:
    
   • Data Collection: It logs into internal and external systems to collect up-to-date loan and market data.
   • Analysis: It applies pre-configured accounting standards (e.g., IFRS 9) to calculate expected credit losses.
   • Reporting: The agent populates the impairments report template, ensuring data accuracy and compliance.
   • Review & Submission: It can even initiate a workflow for human review before final submission to regulatory authorities.
4. Outcome:
   • Efficiency: Report preparation time is reduced from days to hours.
   • Accuracy: Automated data collection and processing reduce manual errors.

The Business Case for Agentic AI in Banking

Agentic AI represents a paradigm shift in how banks operate, interact with customers, and manage risks. We believe the core business case for adoption is built on three pillars: cost efficiency, revenue growth, and capital optimisation.

1. Cost Efficiency: Driving Operational Productivity

Banking operations are traditionally labour-intensive, with high costs associated with repetitive tasks, manual processes, heavy use of spreadsheets, and human error. Agentic AI reduces these inefficiencies by automating workflows end-to-end.

Example Use Case: Internal Management Reporting Automation

Challenge: Banks' internal reporting teams spend significant time and resources preparing management reports, often requiring manual data extraction, analysis, and presentation from multiple systems. This process is labour-intensive and prone to delays, impacting decision-making and operational efficiency.

Agentic AI Solution:

1. Automated Data Collection: AI agents integrate with internal systems (e.g., finance, risk, and operational data sources) to automatically collect and validate data required for internal management reports.
2. Report Generation: The agents populate standardised reporting templates and generate dynamic visualisations like dashboards and charts.
3. Anomaly Detection: During data processing, AI agents can identify anomalies—such as unexpected variances in financial performance or deviations from budget forecasts—and flag these for human review.
4. Streamlined Distribution: Once finalised, the AI agents automatically distribute the reports to relevant stakeholders, including tailored insights for different roles (e.g., executives, department heads, analysts).
5. Intelligent ChatBot: A ChatBot can further transform internal reporting for banks by providing a conversational interface that allows employees to access real-time financial data, generate reports, and receive insights through natural language queries.

Business Benefit:

• Efficiency Gains: Achieve a 70–80% reduction in manual effort for report preparation, allowing teams to focus on analysis rather than data gathering.
• Speed and Timeliness: Generate management reports in a fraction of the time, ensuring decision-makers can access the latest insights.
• Improved Accuracy: Reduce the risk of manual errors and enhance data consistency across reports.
• Enhanced Strategic Value: By automating routine tasks, internal reporting teams can contribute more strategically, offering deeper insights and increasing business partnering activities across the bank.
  
  

2. Revenue Growth: Unlocking New Opportunities

Agentic AI enables banks to generate new revenue streams by delivering hyper-personalised customer experiences and identifying untapped opportunities.

Example Use Case: Personalised Financial Planning

Challenge: Traditional banking services often rely on standardised product offerings that fail to account for individual customer needs.

Agentic AI Solution:

AI Agents analyse customer behaviour, spending patterns, and life events to recommend tailored financial products, such as retirement plans, insurance policies, or investments.

1. Data Aggregation: The AI Agent collects data from various sources, including transaction histories, spending patterns, financial goals, and external data such as market trends or life events (e.g., a change of address or a new job).

2. Behavioural Analysis: Using machine learning algorithms, the agent identifies patterns in customer behaviour, such as regular spending habits, savings patterns, or responses to previous offers.

3. Predictive Modelling: The agent applies predictive analytics to anticipate future financial needs, such as planning for retirement, managing risk through insurance, or optimising investment strategies.

4. Product Matching: The AI Agent cross-references customer profiles with available financial products to identify the most suitable offerings. This includes customising products based on risk tolerance, financial goals, and personal circumstances.

5. Proactive Engagement: Through conversational interfaces (e.g., chatbots, banking apps), the agent initiates personalised interactions, such as:

   • "Based on your recent transactions, would you like to explore investment opportunities for your savings?"
   • "We noticed your spending has stabilised—would you like tailored advice on boosting your retirement savings?"

6. Continuous Learning: The agent learns from customer responses and outcomes, refining its recommendations to improve accuracy and relevance over time.

7. Compliance and Personalisation: All suggestions comply with regulatory requirements and are personalised to ensure relevance and timeliness, enhancing customer engagement and satisfaction.

This approach boosts cross-selling and upselling opportunities and strengthens customer loyalty by offering genuinely valuable financial advice.

Business Benefit:

• Increased cross-selling and upselling opportunities.
• 10–15% higher customer retention rates due to improved satisfaction (extrapolated from Oracle studies on AI-driven personalisation).
• Enhanced revenue per customer by matching products to needs in real time.
  
  

3. Risk Mitigation & Capital Optimisation

Proactive risk management is a critical priority in a volatile financial landscape. Agentic AI enhances banks’ ability to detect, predict, and respond to real-time risks.

Example Use Case: Dynamic Risk-Weighted Asset (RWA) Optimisation

Challenge:  Banks must optimise Risk-Weighted Assets (RWAs) to comply with regulatory requirements (e.g., Basel III/IV) while ensuring efficient capital allocation. Traditional methods for managing RWAs are often manual and reactive and lack the agility needed to respond to market changes or portfolio dynamics in real-time. Inefficient RWA management can lead to excessive capital buffers, reducing Return on Equity (RoE) and limiting growth opportunities.

Agentic AI Solution:  

1. Real-Time RWA Monitoring: AI Agents continuously analyse loan portfolios, trading positions, and off-balance sheet exposures to dynamically assess the current RWA profile.  
2. Predictive Analytics: The agents use machine learning models to forecast the impact of market movements, changes in credit risk, and portfolio shifts on RWAs.  
3. Automated Capital Allocation: The AI Agents identify opportunities to optimise capital use by reallocating exposures, adjusting risk weights, or recommending hedging strategies.  
4. Regulatory Compliance: The solution ensures that proposed adjustments meet regulatory requirements, including stress testing and Pillar 3 disclosure obligations.  
   
   

Business Benefit:  

• Improved Capital Efficiency: The potential to reduce RWAs by 5–10%, freeing up capital for revenue-generating activities.
• Enhanced RoE: By lowering capital requirements, banks can potentially improve their RoE by 50–100 basis points, directly enhancing shareholder value.  
• Regulatory Readiness: Ensures ongoing compliance with evolving regulatory frameworks, reducing the risk of capital penalties.  
• Strategic Agility: Enables proactive responses to market conditions, optimising balance sheet use without compromising risk management standards. 

Supporting Evidence: Case Studies and Industry Trends

While comprehensive longitudinal studies on Agentic AI are still emerging, there is evidence from related AI and automation technologies that demonstrates significant benefits:

1. Accenture Research: Banks using AI-powered automation have achieved a 20% reduction in operational costs across key functions like compliance, risk management, and customer service.
2. McKinsey Insights: Depending on the maturity of the implementation, AI-driven personalisation leads to 5–15% revenue increases.
3. BCG Reports: Financial institutions adopting advanced fraud detection systems report 40% reductions in fraud losses within 12–18 months of deployment.

4. Improving Cost-to-Income Ratios with Agentic AI

Agentic AI offers a compelling opportunity for major UK banks to improve operational efficiency and financial performance. By automating complex finance, risk, compliance, and internal reporting processes, banks can achieve significant cost savings and improve key financial metrics such as the Cost-to-Income Ratio (CIR) and Return on Equity (RoE).

Current Cost-to-Income Ratios (CIR) of Major UK Banks

The current CIRs of major UK banks, sourced from Statista (2024), are as follows:

• HSBC Holdings: 48.5%
• Barclays: 67%
• Lloyds Banking Group: 54.7%
• NatWest Group: 51.8%

Financial Impact of a 3% Reduction in Operating Expenses

To quantify the business case for Agentic AI, we modelled the impact of a 3% reduction in operating expenses for these banks. The table below illustrates the potential savings and the resulting improvement in CIR.

Data Sources and Assumptions

• Revenue Data: Sourced from the 2024 Fortune Global 500 list.
• CIR Data: Sourced from Statista (2024).
• Operating Expenses Calculation: Derived using the formula:

Operating Expenses = Revenue × (CIR/100)

• Savings Calculation: Based on a 3% reduction in current operating expenses.
• New CIR Calculation: Calculated as:

New CIR = (New Operating Expenses / Revenue) × 100

Strategic Benefits Beyond Cost Savings

1. Significant Cost Reductions: The analysis shows Agentic AI could save these major banks between £0.52 billion and £1.96 billion annually.
2. Enhanced Operational Efficiency: Lower operating expenses improve the CIR, contributing directly to profitability.
3. Improved Return on Equity (RoE): Savings and efficiency gains translate into stronger financial performance and shareholder returns.
4. Scalability and Agility: Agentic AI automates routine tasks, enabling banks to scale efficiently and respond quickly to regulatory and market changes.

Implementing Agentic AI presents substantial financial and operational advantages for major UK banks. The potential to enhance the CIR by 1-2% through cost savings alone positions early adopters to secure a significant competitive advantage.

Call to Action: Begin Your Agentic AI Journey

The competitive landscape in banking is evolving rapidly, and early adopters of Agentic AI can achieve tangible financial and operational benefits. These include reducing full-time equivalent (FTE) costs, optimising cost-to-income ratios, and enhancing Return on Equity (RoE) through improved decision-making and strategic agility. Delaying adoption risks falling behind competitors leveraging AI to transform efficiency and profitability.

Key Reasons to Act Now:

1. Achieve Significant FTE Cost Savings:
    
   • Automation Potential: Agentic AI can automate up to 70% of manual, repetitive tasks within functions such as finance, risk, compliance, and internal reporting.
   • Cost Impact: This automation can reduce FTE costs by 15-25%, contributing directly to a lower cost-to-income ratio and enhancing overall profitability.

2. Enhance Decision-Making and Profitability:
    
   • Better Insights: AI agents deliver real-time data analytics and predictive insights, supporting more informed and timely business decisions.
   • Strategic Outcomes: Improved decision-making drives better risk management, optimises capital allocation, and identifies new revenue opportunities—all contributing to a higher RoE.

3. Strengthen Operational Efficiency:
    
   • Scalability Without Proportional Costs: As transaction volumes and reporting requirements grow, AI agents handle the increased workload without requiring a linear increase in headcount.
   • Agility in Regulatory Compliance: Automate complex regulatory reporting (e.g., CSRD, ESG disclosures) to ensure compliance and reduce the risk of regulatory penalties.

4. Future-Proof Your Bank:
    
   • Long-Term Resilience: Building AI-driven capabilities positions your bank to adapt quickly to market changes, regulatory shifts, and emerging competitive threats.
   • Unlock Growth Opportunities: Agentic AI offers the potential to innovate service offerings, improve customer experiences, and increase market share, in addition to cost savings.
     
     

The Bottom Line: By adopting Agentic AI now, banks can transform their cost structures, boost profitability, and enhance strategic decision-making. Early adopters are improving operational efficiency and fundamentally reshaping their business models to drive sustained growth and competitiveness.

Take the First Step with Oracle and Revvence

At Revvence, we specialise in implementing Oracle’s leading-edge Agentic AI solutions for banks. With our expertise in risk, finance, and treasury, we help you deploy AI Agents tailored to your specific challenges and goals. From pilot projects to full-scale deployments, we ensure that your journey with Agentic AI delivers measurable business impact.

How can we help?

Revvence can help in several valuable ways:

• Check out Revvy, our Narrow-GPT for Finance Transformation. Read all about Revvy here.
• Review your existing finance processes to recommend where Oracle's AI capabilities will have the most impact.
• Create a proof-of-concept to show you the art of the possible and help you build your business case for change.
• The design and delivery of end-to-end solutions.