Agentic AI in finance: Build smart and reap the rewards

Financial services have always been a balancing act of efficiency, customer trust, and compliance. But now, AI agents are helping teams thread that needle with more confidence.

At our recent Financial Services Summit in Sydney, we heard from the agentic finance teams that are making AI go further. Take Melbourne-based global trading platform Eightcap, which is using Agentforce to provide 24/7 multilingual support and deflect nearly half of all service cases to free up more time for teams to spend on high-value work.

This is just one example of many powerful agentic transformations in Australia, and it shows why 90% of finance teams are using AI agents already or expect to within five years. For today’s finance leaders, it’s not a question of when, but how.

Of course, implementation isn’t as simple as ideation. Australia’s finance sector is tightly regulated by ASIC and APRA , with strong guardrails around privacy and accountability. It’s also reliant on customer trust, and with many consumers still sceptical about AI, finance leaders need to have a plan before they jump headfirst into agentic use cases.

In this guide, we’ll look at how AI can transform your finance business and what leaders need to prepare to keep AI agents reliable, governed, and productive at scale.

• The foundations that make agentic AI work in finance
• How AI can transform financial services
• Humans + Agents is the real enterprise victory
• Make agentic finance work for your organisation
• FAQs

Finance is a natural fit for AI adoption. The industry is built on structured data and repeatable processes, and that’s the exact foundation AI systems need.

However, having a lot of data doesn’t mean it’s ready for AI. In many financial organisations, the context agents need is spread thin across legacy systems, service notes, compliance records, spreadsheets, and a web of third-party tools. That means agents can never work from the full picture, especially when data is outdated or incomplete.

As per our Connected Financial Services Report , 88% of technical decision-makers agree that AI outputs are only as good as data inputs. However, 93% still feel their organisation should be getting more value from their data, and 45% lack full confidence in their data’s accuracy.

That’s why the first goal for any financial services organisation needs to be building a foundation of accurate, trusted, connected data. That unified layer gives your teams the context they need to understand customer needs and ensures every action agentic AI takes is grounded in dependable business context.

A good place to start is with Data 360 for Financial Services. Our platform can help you bring all of your financial data into a standardised view, then activate it across Agentforce Financial Services to power next-level customer and agentic experiences.

Back in October 2024, ASIC published its review of 624 consumer-impacting AI use cases across financial businesses. The takeaway was that innovation doesn’t eliminate obligation. Regardless of automation, organisations need to hold accountability for AI outcomes.

And that’s before AI use was widespread. Today’s agents influence more decisions and complete more work than they did even two years ago. Here are some of the key areas you’ll need to consider:

• Consumer protection: An agent could influence whether someone receives credit, an insurance payout, or financial advice. Leaders need to understand where AI shapes the outcome and check whether lending criteria, such as credit score requirements for debt consolidation , could unfairly exclude certain applicants.
• Privacy and security: Agents work with sensitive information in finance – think identity details, account balances, and claims records. Access for AI needs to be limited to what each task requires.
• Operational resilience: AI-supported payments and customer services can grind to a halt when an agent gives an unreliable assessment. Safeguards need to stop incorrect actions from spreading while keeping essential services running.
• Accountability: Every use case needs a clear owner and defined points for human approval. You’ll also want the agent to maintain logs so it can easily reconstruct how it handled every decision or action.

Unified data goes a long way toward making each of these safeguards achievable. However, that context needs to be governable. Role-based permissions, clear audit trails, strong governance, and human-in-the-loop checkpoints should define where agents work independently, where humans take over, and how you’ll track every decision along the way.

To draw from our recent article for finance leaders on AI stewardship: An autonomous agent must never mean an unsupervised agent. Finance teams need to give AI enough freedom to complete useful work while keeping outcomes trusted and humans accountable for the important workflows.

That kind of supervision is especially important in our industry. While there are near-endless benefits of agentic finance, customers don’t always have the same view. Only 44% of consumers somewhat trust AI agents in financial services, and just 10% fully trust them. It isn’t as simple as hitting the big green button and watching the challenges melt away.

The solution? The same report identified five actions that help consumers trust AI agents:

1. Transparency: Tell customers when they’re interacting with AI, what it’s being used for, and when a person will get involved. Clear expectations remove uncertainty.
2. Validation: Use human expertise to check important outputs and decisions, particularly when they could impact someone’s finances or personal circumstances.
3. Control: Set clear boundaries around which tasks agents can complete independently and which require approval.
4. Explainability: Make sure you can understand and explain important outputs. Customers and employees should be able to see how an agent reached an outcome.
5. Accuracy: Test and monitor agents continuously to make sure their responses remain dependable as data, products, and customer needs change.

A strong platform will support all of those principles. With Agentforce, agents are treated like teammates. That means you can define role-based permissions as you would your employees. Guardrails prevent them from completing the wrong task and accessing off-limits data, while human-in-the-loop checkpoints give you the final say over agent outcomes.

Customers don’t need to understand every detail behind an agent. However, they still want accurate answers, clear security, accountability, and outcomes they can understand. Once you achieve that, you’re well on your way to some incredible use cases.

The most exciting AI agent applications aren’t necessarily the most elaborate. For many finance teams, it's the little wins that pique interest, like the chance to automate routine work and cut down on the 60-70% of banker time spent on low-value admin. Or, to make sense of unified data and get insights to personalise customer experiences and scale revenue.

Below, we’ve outlined a handful of interesting applications for AI agents in finance. That said, don’t feel limited to these use cases. The examples below are great options, but the reality is: if you can think it, you can almost always build an AI agent that can deliver it. See our low-code Agentforce Builder to find out more.

Finance teams have never had a data shortage. The difficulty is getting context fast enough for it to be useful. As per our State of Data and Analytics Report , 48% of financial services respondents say they aren’t sure they can deliver relevant insights.

AI agents can help to close that gap. For instance, with Agentforce Financial Services, an agent could:

• Pull together live data from across customer and financial systems to build a more complete view of trends and performance.
• Use that context to create powerful reports and visualisations, then update those dashboards in real time as the underlying data changes.
• Surface key changes to leaders, and let them dig deeper into patterns using natural language, all in the same flow of work.
• Share personalised digests through Slack or your CRM, with key insights and recommended next steps, so teams can act without hunting through dashboards.

Of the finance leaders we surveyed, 94% agree that agents can make data more accessible , easier to understand, and more timely. When you give AI agents a trusted base, they can help you see change as it happens and decide what to do about it in the moment.

Every invoice, payment, claim, or account update creates a trail of admin. Someone has to capture the details, categorise the transaction, match it to the right record, and check whether it needs review. It’s repeatable work, but it still takes time.

AI agents can help by handling the routine tasks that sit around every transaction. For instance, Agentforce can read key details and compare them against financial records, then flag up duplicates or mismatches. And it can do it all automatically, while routing unusual transactions to the right person for review.

This means finance spends less time checking straightforward transactions, meaning more bandwidth for the exceptions that actually need human judgment.

When consumers report a lost card, dispute a transaction, or query a fee, they want the issue handled there and then. However, with disconnected systems and reps short on time, customers often spend more time than they’d like waiting in queues and repeating details.

AI agents aren’t a cure for disconnected systems or messy data. However, with the right context behind them, they can handle routine requests even when your team is short on time. For example, Agentforce can:

• Speak directly with customers through your digital service channels, understand what they need, and respond without making them wait for a rep.
• Instantly gather the right account, transaction, and service context to resolve everyday requests like fee queries, lost cards, and disputed transactions.
• Complete approved actions in the conversation, like locking a lost card, then explain what happened and what the customer should expect next.
• Hand complex or sensitive cases to your team with the full conversation and relevant context attached, so customers don’t have to explain anything from scratch.

This is far more than a basic chatbot following a script. AI agents can interpret meaning, reason through a request, follow up over email if the customer leaves the chat, and reach out to your team rather than leaving them to piece every dispute together manually.

Just 32% of Australian banking customers say they’re fully satisfied with the effectiveness of their bank’s customer service. AI agents can be the difference between frustrating waits and personalised agentic support in real time.

Consumers want fast, dedicated support tailored to their needs. However, many finance businesses still rely on broad segmentation. That isn’t due to a lack of effort; teams are simply short on time to deliver the level of personalisation modern customers demand.

This is one of the areas where AI can completely transform how you engage with customers. With trusted data supporting personalisation in Agentforce, an agent could:

• Help marketers segment audiences and build out tailored journeys, such as an email nurture campaign.
• Launch that email nurture campaign as soon as a relevant event occurs, like a mortgage term ending.
• Personalise each message in that campaign using live data, such as the customer’s current finances, circumstances, and who they’ve interacted with previously.
• Pause journeys automatically if the customer enters hardship, or when it detects fraud.

Only 22% of Australian banking customers are fully satisfied with personalisation. Agents give teams a practical way to close that gap by adapting each journey in real time. Of course, handling the busy work also gives teams more time to spend on face-to-face interactions.

Agentic AI isn’t going to replace your finance team, but it will give them more time to focus on what they do best. Less automation tool, more agentic teammate. Agentforce can help you:

• Scale capacity without scaling admin. Agents can absorb routine preparation and follow-up work as demand grows
• Improve decisions. Your team has more time to assess risks, examine nuance, and think critically about problems
• Give people more rewarding work. Less time spent chasing information and completing repetitive tasks means more time applying specialist knowledge
• Protect the relationship advantage. Advisers and service teams can devote more attention to complex cases and conversations where trust matters

Beyond that, human-agent collaboration also gives financial businesses a more practical way to adopt AI. Teams don’t need to transform every workflow at once. They can start with contained tasks and expand as confidence and employee experience improve.

The strongest AI strategy starts with a clear role for agents and people. Start with the work that creates friction, set the right boundaries, and expand as your teams build confidence.

If your business is gearing up to build that foundation for agentic finance AI, read our guide to Data Maturity for Financial Services , or see Data 360 to see how Salesforce can help.

Ready to get started? Watch the demo to see how Agentforce Financial Services can help you create trusted agents that deliver value well beyond the balance sheet.