A governed agentic credit workflow

Our client, an Australian financial services and credit provider operating under an Australian Financial Services Licence, wanted to automate a significant part of preparing credit applications for assessment. Credit applications arrive with large volumes of banking and financial information, often in inconsistent or semi-structured formats. Before this project, people had to interpret, classify and normalise much of that information by hand before it could be used in credit decisioning. The difficulty wasn't extraction. Banking data is noisy, inconsistent and highly contextual: transactions have to be understood and mapped into a consistent credit data model, with every output traceable back to its source. And because the result feeds credit decisions, the system couldn't be an opaque AI making calls on its own. It needed strong controls around confidence, validation, human review and auditability from the start.

We treated this as a governance problem as much as an AI one. The principle was simple: AI reasoning sits inside a controlled processing pipeline, never on its own. Delivery was principal-led, with senior technical oversight of the architecture, the AI design and the governance model, supported by engineers responsible for implementation and integration. Every stage was designed around the question "what happens when the answer is uncertain?" The system is expected to escalate an ambiguous item rather than invent or force a classification, and it is tested for that behaviour as deliberately as it is tested for accuracy. We used AI throughout our own engineering: implementation, testing, documentation and analysis. Architecture, risk boundaries, system design and production decisions stayed human-led.

A governed, multi-agent credit workflow: Ingest: banking and financial data associated with a business or customer is brought in and normalised into a common format. Interpret: AI interprets and classifies unstructured or inconsistently structured information, working alongside deterministic processing rather than replacing it. Structure: the results are mapped into a structured credit data model, identifying the financial characteristics, categories and signals the credit workflow needs. Validate: validation and confidence checks run on every output. Ambiguous or low-confidence items are flagged for human review, not silently assumed. Hand over: structured credit information and its supporting context pass to a specialised underwriting agent, which helps prepare the assessment. Decide: the appropriate human credit authority makes or approves decisions wherever required, under the client's own governance processes. Human checkpoints sit at exceptions, low-confidence outputs and every consequential credit decision. The architecture is built for human-in-the-loop decisioning, not autonomous lending. The workflow preserves the link between source information, the AI's interpretation and the structured information presented to the credit process. Agent activity, processing outcomes, exceptions and decision context can be traced, not just the model's final answer, giving engineering, credit, risk and compliance teams evidence they can use when reviewing how information moved through the workflow. Changes are evaluated against representative financial-data scenarios and expected structured outputs before they reach production. Tests cover extraction accuracy, classification, schema compliance, edge cases and failure behaviour.

Around 70-80% of banking transactions are now classified and mapped into structured credit categories automatically, significantly reducing manual data preparation before underwriting and letting credit teams spend more of their time on assessment and exceptions. Controls are built in, not bolted on: confidence checks, escalation and end-to-end traceability were part of the design from day one, giving risk and compliance teams an evidence trail. The data-structuring capability became part of a broader agentic credit workflow, handing structured information to a specialised underwriting agent. The architecture lets further governed agents join the credit lifecycle without removing human oversight.