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How Banks Can Leverage Existing RPA Bots with Generative AI & Agentic AI to Enhance Experience and ROI

April 14, 2025 No comments yet
Boost ROI in banking with RPA, Generative AI & Agentic AI synergy

Banks have been using Robotic Process Automation (RPA) for quite some time to make repetitive tasks easier, cutting down on manual work and saving on costs. But RPA has its limitations, especially when it comes to handling complex workflows, unstructured data, and making decisions. Thankfully, with the rise of Generative AI (Gen AI) and Agentic AI, banks now have the opportunity to take their automation game to the next level, moving from simple rule-based systems to smart, adaptable, and even autonomous operations.

What Do These Technologies Bring to Banking?

RPA in Banking: It streamlines those tedious, rule-based tasks, but it struggles with unstructured data and decision-making.

Generative AI in Banking: This technology brings natural language understanding, document processing, and content creation to the table, making automation not just smarter but also more dynamic.

Agentic AI: It introduces decision-making, adaptability, and self-learning features, paving the way for autonomous workflows.

When banks blend these technologies, they can elevate their operations from simple automation to intelligent process automation, greatly boosting efficiency and customer satisfaction.

Read Also: Reimagining Business Processes: The Power of Automation and AI Integration.

Key Use Cases of Generative AI in Banking, RPA, and Agentic AI in Banking

Diagram showing three interconnected benefits of automation: Intelligent Automation (advanced systems automating complex tasks), Customer Experience (AI-driven interactions improving client satisfaction), and Operational Efficiency (streamlined processes ensuring compliance and speed). Each benefit is represented with an icon and labeled around a central graphic of a computer with a dollar sign.

A. Customer Experience & Support

1. Smarter Virtual Banking with AI

  • Old-school chatbots are making way for new, AI-powered virtual assistants in banking. These modern bots don’t just follow scripts—they actually understand what customers are asking, give personalized answers, and even help complete tasks like money transfers or checking balances.
  • Thanks to a newer technology called Agentic AI, these virtual agents can now handle more complex requests. They can ask for missing details, walk customers through multiple steps, and know when to bring in a human if things get too complicated.

    Example: Let’s say a customer asks about their loan balance. The AI not only pulls up the balance but also explains the repayment terms and even offers options for refinancing—all on its own, without human help.

    2. Proactive Financial Advisory & Cross-Selling

    • Generative AI dives into transaction histories to provide customers with real-time insights.
    • Agentic AI anticipates what customers might need, like warning them about a possible overdraft, and takes the initiative to offer solutions or take action on its own.

      Example, if a customer’s account balance starts to dip, the AI bot can recommend a pre-approved loan or overdraft option and handle the entire process without needing any manual input.

      To know about Business Process Reengineering, Read this Blog: Business Process Reengineering (BPR): Definition, Steps, Methodology, Benefits, and Examples.

      B. Operational Efficiency & Compliance

      1. Automated KYC, AML, and Fraud Detection

      • Generative AI is great at pulling and verifying information from documents like passports and bank statements.
      • Agentic AI steps in to flag any suspicious transactions and fine-tunes fraud detection models on the fly, which helps cut down on the need for manual reviews.

        Example, rather than sticking to inflexible KYC processes, AI agents look at patterns and modify risk scores based on past fraud data.

        2. Self-Healing RPA Bots

        • Generative AI can troubleshoot automation hiccups by spotting errors in RPA scripts.
        • Agentic AI allows bots to adjust in real-time to changes in the system, keeping everything running smoothly.

          For example, when a banking interface gets an update, AI-driven RPA bots automatically make the necessary adjustments without needing a manual overhaul.

          To know about Business Process Governance, Read this Blog: Business Process Governance and its Importance: Complete Guide.

          C. Intelligent Process Automation

          1. Loan & Mortgage Processing

          • Generative AI is great at pulling out and summarizing the important details from loan applications.
          • Agentic AI takes it a step further by assessing credit risks on the fly, automatically approving applications that are low-risk, and flagging those that are a bit more complicated.

          Example, AI bots can tailor interest rates and approval terms based on a customer’s credit history, which really speeds up the whole loan process.

          2. Autonomous Reconciliation & Exception Handling

          • Generative AI also excels at spotting and summarizing discrepancies in financial records.
          • Agentic AI learns from previous reconciliations and can fix discrepancies all on its own.

            Example, if a bot notices a payment inconsistency, it can check related systems for any missing information and resolve the issue without needing any help from a human.

            Read Also: The Future of Finance: How RPA is Shaping the Next Generation of Banking Sector.

            How Banks Can Seamlessly Blend Generative AI and Agentic AI with RPA

            Step 1: Pinpoint Key Processes

            Start by honing in on crucial areas such as customer support, document processing, fraud detection, and compliance. These are the spots where adding AI can really pay off.

            Step 2: Supercharge RPA with AI

            Leverage Generative AI APIs (like GPT-4 or Claude) to boost your RPA workflows, especially for tasks like document handling and engaging in conversations.

            Incorporate Agentic AI to enable smart decision-making and flexible workflows.

            Step 3: Create AI-Driven Workflows

            Craft workflows that allow AI agents to actively monitor, adjust, and enhance automation. Think of multi-agent systems working together—one can spot anomalies, another can verify data, and a third can tackle any issues that arise.

            Step 4: Keep an Eye on Performance, Optimize, and Expand

            Monitor key metrics such as customer satisfaction, efficiency, and cost savings. Make it a habit to continuously fine-tune your AI models to keep up with the ever-changing landscape of banking needs and regulations.

            Read Also: Enhancing Process Mining with Customer Experience Data: A Banking Perspective.

            ROI & Business Impact

            Imagine processing customer service requests and backend operations 50% faster! Plus, there’s a 40-60% drop in the need for manual intervention, which means your team can dedicate their time to more valuable tasks.

            You could also see up to 30% in cost savings thanks to self-healing bots and smart automation. And let’s not forget about improved fraud detection and compliance, all thanks to AI-driven risk analysis and spotting anomalies.

            Conclusion

            The way Generative AI, Agentic AI, and RPA are coming together in banking is truly transforming the financial landscape. By stepping away from just basic automation, banks can unlock intelligent, adaptable, and even autonomous operations. This shift leads to greater efficiency, better customer experiences, and maximized ROI. It’s high time for banks to tap into the full potential of these technologies and revolutionize their operations in our ever-evolving digital world.