Agentic AI use cases in fintech, built for AU compliance

Where the week actually goes

For most Australian finance brokers and advisers, the bottleneck is not winning the client. It is the file. A single application means chasing documents, re-keying details across a CRM, an aggregator platform and a lender portal, then drafting a statement of advice that has to satisfy both the client and your licensee. Across a full pipeline the admin swallows the days you meant to spend advising. Small fintech teams hit the same wall from the build side, shipping product while their own data sits in a shape nobody trusts and every release waits on compliance sign-off.

You have heard that agents could help. What you cannot tell from a demo is which parts are safe to hand over when client money and a credit or AFS licence are on the line.

Why a tool on its own falls short here

You could buy an off-the-shelf assistant and point it at a few documents. In finance that usually fails for reasons the box never mentions. A generic agent does not know your lender panel, your SOA template or the product rules you live under, so it produces plausible filler that a broker has to unpick line by line. Worse, it leaves no record of how it reached a draft, which is the one thing your licensee and ASIC will ask for. An answer you cannot trace is an answer you cannot defend.

How we deliver it for finance

We lead with training, security and governance because client financial data and your licence obligations shape every other choice. Data stays inside your environment, an agent only touches what a given task needs, and access is scoped to the job. This is principle #2 from our approach, applied to your specific licence conditions rather than a generic policy.

On top of that we record and version every advice or decision step. How a recommendation was reached, which client and product data fed it, and which template version produced the draft are all captured, so you hold an audit trail your licensee and ASIC would expect. That is principle #6, decisions kept documented and reversible. Underneath both, we put your client and product data into a clean, usable ecosystem first, because an agent drafting an application is only as good as the data behind it. That is principle #4.

We start with one bounded process, test it against your real historical files, and measure where it is right and where it is not before anything touches a live client.

When an agent is the right call, and when it is not

An agent fits where the work is high in volume, mostly rules with some judgement, and a draft is checkable before it goes out. Application prep, SOA first drafts and alert triage all qualify. It is the wrong call wherever the act itself is advice or a credit decision, where a thin or messy data set would make any draft a guess, or where the volume is too low to repay the setup. We will say so when that is the case, and point you at a simpler automation instead.

Related reading

See the parent capability in AI Agents, the broader sector view in FinTech & Banking, and how grounding and traceability come together in our approach.