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AI Fraud Detection

Canary-style ML fraud scoring on every booking and card authorisation

1
High Risk
2
Needs Review
2
Clean

Instant Risk Scanner

Recent Bookings — Risk Scores

James Chen — Suite

BK-001 · 3 nights · £1,800

60
/ 100
REVIEW
⚠ Prepaid card detected⚠ Booking made < 2 hours before arrival⚠ High-value booking + no prior stay history

Sarah Williams — Deluxe

BK-002 · 2 nights · £420

5
/ 100
LOW RISK

Unknown Traveller — Junior Suite

BK-003 · 1 nights · £950

92
/ 100
HIGH RISK
⚠ Billing address mismatch⚠ Multiple failed card attempts⚠ VPN / proxy IP detected⚠ Name/email mismatch from previous fraud flag

Mohammed Al-Hassan — Standard

BK-004 · 4 nights · £560

20
/ 100
LOW RISK
⚠ Booking made < 2 hours before arrival

Elena Kozlov — Deluxe

BK-005 · 2 nights · £380

55
/ 100
REVIEW
⚠ Prepaid card detected⚠ Billing address mismatch

Identity Document Verification

0
Pending
0
Flagged
0
Verified

No documents uploaded yet

Active Fraud Detection Rules

Prepaid card detected

Prepaid/gift cards are high fraud risk

+25

Billing address mismatch

Card billing address doesn't match booking address

+30

Booking made < 2 hours before arrival

Last-minute bookings with premium rooms

+20

Multiple failed card attempts

Card declined 2+ times before success

+35

High-value booking + no prior stay history

New guest booking a suite or long stay

+15

VPN / proxy IP detected

Booking made via anonymising service

+20

Name/email mismatch from previous fraud flag

Name or email matches a known fraud pattern

+40

Same card used for multiple different name bookings

Card used for bookings under 3+ different names

+45