Think of it as your starting kit to make AI agents work in your banking environment.
AI agents for banking work best when focused. That’s why the first move is to pick one process that checks three boxes:
✔️ It happens frequently.
✔️ It slows down due to manual steps.
✔️ It follows a known pattern with room for smart decisions.
For example, this could be internal requests that go through approvals, KYC verifications, or repetitive customer onboarding steps.
Start by answering:
→ Where do handoffs take time?
→ Which process involves checking multiple systems?
→ Where do employees wait for decisions before acting?
Choose a journey that’s valuable enough to show results and simple enough to control. This helps you build momentum before expanding further.
The next move is to prepare your environment. AI agents in banking need the right input, the right team, and the right space to operate.
Here’s what matters:
Make sure the systems used in your selected process can provide the data needed for actions. This could be via APIs, reports, or manual uploads.
Include your business process owner, IT lead, and compliance advisor early. They guide what’s allowed and how things flow.
Set up a controlled environment using synthetic data or test records. This helps the AI agent learn and act without impacting live operations.
Decide what the AI agent will handle and where your staff will step in. Some banks start with decision support. Others go for full task automation with human monitoring.
When these pieces are in place, your pilot moves faster and gives better clarity.
The third move is about execution.
AI agents require engineering, context, and a strong understanding of banking processes. That’s why your implementation partner should bring more than tools; they should bring real banking experience.
Look for a partner who:
✔️ Understands financial services workflows
✔️ Can work with both legacy systems and cloud infrastructure
✔️ Brings pre-built components to reduce effort
✔️ Supports pilots, scaling, and integration without long timelines
Being an enterprise AI development company, we work with banks across North America and Europe to develop AI agents tailored to specific goals.
Whether it’s assisting agents during customer calls or automating internal requests, our approach blends AI capability with banking logic.
We offer co-creation sprints, ready-to-deploy frameworks, and integration accelerators that reduce complexity and show results early.
Getting started with AI agents in banking becomes easier when you’re clear on what to skip. These are avoidable missteps we’ve seen across early pilots in both retail and corporate banking setups:
Here are several real-world examples of AI agents for banking, covering use cases like document review, customer service, fraud detection, and more:
JPMorgan uses an AI agent called COiN (Contract Intelligence) to go through thousands of legal documents like loan agreements in seconds. Earlier, this work used to take thousands of hours for legal teams. With COiN, the bank saved around 360,000 work hours a year and reduced human errors significantly.
Bank of America built a smart AI agent named Erica inside their mobile banking app. It helps customers check balances, track spending, get reminders, and even learn ways to manage money better. Erica has handled over 1 billion interactions and works 24/7 without needing human agents.
Capital One’s Eno is a virtual assistant that sends customers real-time alerts about spending, due dates, fraud activity, and unusual transactions. It talks to users via text or email and is always watching for anything suspicious or helpful.
Fargo helps customers with everyday banking needs via voice or text. In 2024 alone, the bank’s AI-powered assistant, Fargo, handled 245.4 million interactions, more than doubling its original projections
NatWest launched Cora+, a generative AI agent built with OpenAI. It helps both customers and bank staff. Customers can ask about payments or fraud, while staff use it to find quick answers from policy documents. NatWest saw a 150% jump in customer satisfaction and better fraud handling using this AI agent.
Mastercard uses an AI agent that reviews around 125 billion transactions each year. It can spot fraud instantly within just 50 milliseconds. Banks using this agent have seen up to 300% better fraud detection rates.
BNY Mellon has given certain tasks to fully autonomous AI agents they call “digital workers.” These agents have their own system logins and handle routine tasks like checking code for errors or validating payments.
ING uses AI agents to go through a customer’s financial information and other data before approving a loan. This has helped the bank make decisions much faster, within minutes instead of days, while still keeping risk under control.
DBS Bank uses AI agents to watch over its internal IT systems. If anything goes wrong, like a server crash or a slow payment system, the AI spots it early and often fixes it automatically. This has helped the bank cut system downtime by over 90%.
PenFed uses Salesforce’s AI chatbot to handle common customer queries. About 20% of incoming questions are solved on the first try without needing a human. This keeps customers happy and reduces waiting time.
You don’t need to wait six months to start. Here’s what your team can do this month:
✅ Identify one journey that takes time, involves repetitive tasks, or creates delays.
✅ Bring together your process lead, tech team, and compliance representative for a 45-minute discovery meeting.
✅ Reach out to our AI agent team for a quick-start workshop or roadmap session. We’ll help you turn your idea into an execution-ready plan.
Yes. AI agents can be designed to interact with legacy systems through APIs, file-based exchanges, or RPA bridges. A good AI agent development partner like Azilen helps you integrate without needing a complete overhaul.
Most banks we work with can launch their first AI agent in 4 to 8 weeks, depending on system access, data availability, and internal alignment. Starting small helps move faster without high risk.
AI agents need structured data to understand tasks and take action. This includes input from CRM, core banking, compliance tools, or document repositories. Even if APIs aren’t available, other access methods can be used to get started.
In retail banking, AI agents often assist with onboarding, service requests, or customer support workflows. In corporate banking, they help with document review, approvals, and internal operations. The key is to start with a process that has enough structure and value.
Bring together a cross-functional team, typically a process owner, IT lead, and compliance representative. This ensures the agent can function technically, align with policies, and support the right business outcomes.
1️⃣ Digital Workers: Autonomous AI agents that are assigned tasks previously handled by human staff, such as validating payments, reviewing documents, or monitoring systems.
2️⃣ Banking Workflow Automation: The process of using digital tools (like AI agents) to execute banking tasks, approvals, or validations without manual intervention.
3️⃣ Decision-Capable Systems: AI systems that can evaluate multiple inputs and take action based on rules, patterns, or goals, unlike static bots that follow predefined scripts.
4️⃣ Synthetic Data: Artificially generated data used in test environments to simulate real scenarios. Helps banks train and test AI agents without exposing customer information.
5️⃣ AI Agent Development: The engineering process involved in designing, training, and deploying AI agents within enterprise systems like banking software, CRMs, and legacy platforms.
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