AI outsourcing
Extracting loan document data with AI and human quality control
A UK fintech lender was slowed by manual document review. An AI extraction pipeline with human quality control cut turnaround and lifted accuracy.
Results at a glance
- -81%
- Document turnaround
- +9 pts
- Extraction accuracy
- -74%
- Manual keying
- -34%
- Cost per application
3.2 hours to 36 minutes
field-level
The challenge
The lender received bank statements, payslips and identity documents for every application and keyed the figures in by hand. As volume grew the review backlog stretched decision times and applicants dropped out before an offer arrived.
The data was highly sensitive and the lender was regulated, so any faster process still had to hold accuracy, keep an audit trail and stay inside UK GDPR. Speed alone was not enough if it introduced errors into lending decisions.
Pain points
- Analysts keyed figures from statements and payslips by hand, which was slow and inconsistent
- The review backlog pushed decision times past the point where applicants stayed engaged
- Errors in extracted income figures fed straight into affordability checks
- There was no structured audit trail linking a decision back to the source document
Our approach
- Built an AI extraction pipeline that read statements, payslips and identity documents into structured fields
- Routed low-confidence extractions and a sampled share of the rest to a human quality-control team for review
- Added a confidence score and a source reference to every field so each figure could be traced back to the document
- Data handled under UK GDPR with a Data Processing Agreement and encryption in transit and at rest
Results at a glance
The results
- Document turnaround fell from hours to minutes for the majority of applications.
- Extraction accuracy rose because the model handled the routine reads and people focused on the hard cases.
- Every decision carried an audit trail linking each figure to its source document.
The model does the routine reading and our reviewers spend their time on the cases that actually need judgement.
Common questions
No. The pipeline extracts and structures the data, and a human quality-control team reviews low-confidence and sampled cases before figures feed the decision.
These are representative engagements. Client identities are anonymised and the metrics are illustrative of the type of results this work delivers, not audited figures for a named client.
Tell us what you need to outsource
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Six-hour response. UK GDPR compliant. Named UK accountability.