Analysts still re-typing payslips and bank statements into the loan system?
Document intelligence for credit operations
Application paperwork turned into checked, structured data, with analysts reviewing only what needs judgment.
Applications / LN-7731
Payslip, classified with 99% confidence
PAYSLIPExample Trading Ltd
Basic salary2,150.00
Allowances300.00
Gross pay2,450.00
Income tax(392.10)
Pension(75.50)
Net pay1,982.40
Employer reg. ET-20▒·71 · Paid to account ••••2209
Extracted fields
1 field needs reviewNet pay: “1,982.40”, read from the totals line.
Cross-checks
- Net pay matches the salary credit on the bank statement (14 Mar)
- Gross minus deductions equals net pay
- Employer registration unclear: registry check waiting for review
RouteAnalyst review · 1 field
- Type
- Solution
- Sector
- Financial services
- Capabilities
- AI & Automation, Software & Platforms
- Built for
- Banks, microfinance institutions and other lenders
§1Context and challenge
Context and challenge
A lender receives loan applications with supporting documents: identity documents, payslips, bank statements, business registrations. Analysts re-type key fields into the loan system and cross-check them by eye.
The challenge
Reduce the manual handling without reducing control: every extracted value must be traceable to its source, uncertain cases must reach a person, and sensitive documents must stay within approved infrastructure.
What changes
Faster, more consistent application processing, with analysts' time spent on the decisions and exceptions rather than on typing.
What we deliver
Process analysis, the AI document pipeline, an evaluation framework and the analyst review interface.
§2Solution and architecture
Our solution.
A document pipeline that classifies each upload, extracts the fields the credit policy needs, validates them against each other and against registries, and assigns a confidence score. High-confidence, consistent files go straight to the analyst's decision queue; anything uncertain or inconsistent is highlighted for review, with each value linked to the place in the document it came from.
Document pipeline
Document pipeline. Components:
- Uploads (source): Portal · branch scan
- Classify (process): Document type
- Extract (process): Fields + source
- Validate (process): Cross-checks
- Decision queue (output): Straight-through
- Analyst review (people): Low confidence
- Loan system (store)
Connections:
- Uploads to Classify
- Classify to Extract
- Extract to Validate
- Validate to Decision queue
- Validate to Analyst review
- Analyst review to Loan system
- Decision queue to Loan system
§2.1The interface
What your team works in.
The interface we deliver, shown here with sample data.
Applications / LN-7731
Payslip, classified with 99% confidence
PAYSLIPExample Trading Ltd
Basic salary2,150.00
Allowances300.00
Gross pay2,450.00
Income tax(392.10)
Pension(75.50)
Net pay1,982.40
Employer reg. ET-20▒·71 · Paid to account ••••2209
Extracted fields
1 field needs reviewNet pay: “1,982.40”, read from the totals line.
Cross-checks
- Net pay matches the salary credit on the bank statement (14 Mar)
- Gross minus deductions equals net pay
- Employer registration unclear: registry check waiting for review
RouteAnalyst review · 1 field
Scroll sideways to see the whole interface.
§3Engineering decisions
The decisions that matter.
D1
Measure before automating
An evaluation set of real, anonymized files is built first, so accuracy per field is known before any file skips review.
D2
Every value has a source
Extracted values keep a link to the page and region they came from, so analysts and auditors can check them in one click.
D3
Confidence routes the work
Thresholds per field and per document type decide what goes straight through, and they are tuned from production corrections.
D4
Deployment matched to sensitivity
Model hosting is chosen with the risk team: private deployment of open models where documents may not leave approved infrastructure.
§4Implementation approach
Implementation approach
01
Assess
Sample applications, map the fields the credit policy uses and how they are checked today.
02
Evaluate
Build the evaluation set and measure extraction accuracy per field and document type.
03
Integrate
Connect to the loan system and launch the review interface with conservative thresholds.
04
Improve
Use analyst corrections to raise accuracy and widen straight-through processing safely.
§5How we measure success
The measures we agree with you before we start, and report against.
- Field-level extraction accuracy against the evaluation set
- Share of applications processed without manual re-typing
- Analyst time per application
- Errors caught at validation versus later in the process
Next solution
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