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AI Engineering

Document AI for OneTwo Home Finance.

ML-powered document classification inside a live loan approval workflow.

Industry
Financial Services · Home Lending
Region
Australia
Services
AI Engineering
Technology
ML Classification · Automated Extraction · APRA-adjacent
01/ the brief

High volumes. Inconsistent formats. A manual process that couldn't keep up.

OneTwo Home Finance is a digital-first home lender built on speed and simplicity — qualities that were being undermined by their document handling process. Payslips, bank statements, and IDs arrived in inconsistent formats, and every document required manual sorting, review, and data extraction before a loan could progress.

  • Staff spent significant time manually sorting and categorising incoming loan documents
  • Extracting and validating key data fields required repeated manual review
  • Processing delays created bottlenecks in approvals — affecting both customer experience and compliance
  • Manual workflows increased the risk of human error and inconsistent verification
OneTwo Home Finance AI document classification case study
02/ the solution

Automated classification, extraction, and routing — across the entire document pipeline.

Crystal Delta built an AI-powered document processing pipeline that replaced manual triage with automated classification, data extraction, and workflow routing — integrated directly into the live loan assessment process.

What's under the hood
  • ML models automatically identify, classify, and route every incoming document type
  • AI-driven extraction of critical data fields — payslip figures, statement balances, ID details — without manual review
  • Workflow integration routes validated documents directly into the loan assessment pipeline
  • Audit-ready output at each stage to support compliance and verification requirements
03/

The Impact

Manual Document Triage
Eliminated

Manual document triage across the loan approval workflow.

Faster approvals
Automated classification and extraction accelerated review cycles end-to-end.
Stronger compliance
AI-driven consistency reduced human error and improved regulatory adherence.
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