Credit LensesRequest Demo

Processing workflow

From bank data to review-ready insight

See how consented transaction data is normalised, interpreted, and returned as structured evidence for your underwriting process.

1

Connect the data

It ingests raw, unstructured bank statement data—messy merchant names, cryptic reference codes, and inconsistent formats—directly via Australia's CDR infrastructure.

2

Interpret the patterns

A hybrid pipeline combines deterministic matching rules with contextual models to clean, categorise, and analyse transaction descriptions consistently.

3

Review the evidence

Within 5 seconds, it returns a structured JSON payload or visual report covering spending patterns, potential commitments, and indicators that may require analyst review. These signals inform, but do not make, a credit decision.

Processing detail

What happens at each stage

1

Data Ingestion & Normalisation

Credit Lenses connects directly to CDR-accredited data holders via secure, consent-driven APIs. When a consumer grants access, we receive up to 24 months of raw transaction history across all linked accounts—savings, credit cards, personal loans, and more.

  • Handles multiple account types and institutions in a single request.
  • Normalises date formats, currency codes, and merchant identifiers.
  • Deduplicates pending vs. settled transactions automatically.
  • Supports batch and real-time ingestion modes for flexible integration.
2

Hybrid AI Processing Pipeline

The engine combines deterministic rule-based matching with contextual models. Recognised transactions such as major retailers and utilities are categorised through a cached rules layer, while ambiguous entries receive additional contextual analysis.

  • Rules Layer: Handles ~80% of transactions in under 50ms using a continuously updated merchant database.
  • LLM Layer: Processes the remaining ~20% using contextual clues—transaction descriptions, amounts, timing, and frequency patterns.
  • Feedback Loop: Corrections from the LLM layer are fed back into the rules engine, improving speed over time.
  • Risk Indicators: Income stability, expense volatility, and potential liability signals are summarised for review rather than used as an unexplained approval decision.
3

Structured Risk Intelligence

The output is a comprehensive "Smart Financial Identity" delivered as a JSON payload via API or as a visual PDF report. It gives decision-makers everything they need at a glance.

  • Income Verification: Confirmed salary deposits, frequency, and employer identification.
  • Expense Breakdown: Categorised into 50+ spending categories with month-over-month trends.
  • Commitment Indicators: Potential BNPL commitments, gambling-related patterns, payday loans, and recurring debt payments for analyst verification.
  • Cash Flow Forecast: Probability of future cash flow stress based on historical patterns.
  • Review Indicators: Anomalous transaction patterns, potential round-tripping, and possible synthetic income activity that require validation against source data.

Performance

Built for Speed and Scale

< 5s

Average processing time per applicant

99.2%

Transaction categorisation accuracy

50+

Spending categories supported

24 mo

Transaction history analysed