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Fraud Detection

Surface risk signals before they reach the credit analyst.

Most lending fraud is invisible until someone spends 30+ minutes on a deal. Kaaj surfaces tampering, mismatches, duplicate submissions, and suspicious patterns at intake, before anyone invests time in analysis.

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Risk model
ML surfaces fudged-number signals for review
Live workflow
Submitted numbers
Application revenue$2.84M
Bank-derived revenue$2.06M
Invoice total$152,000
Requested amount$480,000
Model signal panel
Needs review
Revenue vs bank deposits+38% gap
Invoice total vs applicationMismatch
PDF metadataChanged
Signals are routed to the analyst with source evidence. Kaaj does not confirm fraud or make final credit decisions.

Document tampering detection

Altered bank statements, modified PDFs, inconsistent metadata. Forensic checks at the file level.

Name & address mismatches

Business name on the application vs. SOS records vs. bank statements vs. driver's license. Cross-checked and flagged.

Duplicate submissions

Same applicant, slightly different entity name, submitted to multiple departments. Detected and consolidated.

Suspicious web presence

No website, 2-day-old domain, no reviews, fake-looking business. Web presence signals scored and surfaced.

Document inconsistency

Invoice doesn't match equipment title. Tax return shows different revenue than bank statements. Flagged.

Pattern detection

Same phone, address, or bank account appearing across multiple unconnected applications. Surfaced as risk signal.

Cross-document signal scanner

3 signals need review
Name match: Application vs SOS
Acme Supply LLC
Address: Application vs Bank stmt
Different city
Statement formatting anomaly
Metadata inconsistent
Phone: Application vs Invoice
Same number
Invoice total vs application amount
$33,349 vs $480,000
Duplicate EIN across submissions
Needs review
Potential duplicateFormatting anomalyAddress mismatchName verified

Why fraud slips through

Fraud investigations often begin only after credit analysis has already consumed time. By then, the team has spent 30–60 minutes reviewing documents that should have been flagged at intake. Kaaj moves fraud detection to the front of the workflow, before anyone opens a bank statement.

0 minutes

Time spent on deals that should've been flagged. Fraud detection runs before any human touches a file.

Catch risk signals before they cost time.

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