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Identity And Access Fraud In Travel Apps
Payment And Booking Fraud
Loyalty And Promotion Abuse
Inventory Content And Fare Manipulation
Bot And Abuse Traffic In Travel Funnels
Identity And Access Fraud In Travel Apps
New Login From Unfamiliar Country Or Device Just Before Points Or Card Redemption
Your app is vulnerable to country/device ATO patterns. The heatmap shows spikes from unfamiliar countries shortly before redemptions.
Account-takeover solution: Protects against logins from unusual devices or countries before redemptions.
See: https://www.crossclassify.com/solutions/account-takeover/
Spike In Failed Logins Followed By A Successful Login
Your app is vulnerable to credential-stuffing takeover. Failures spike first, followed by a success surge on the same window.
Bot-attack solution: Detects credential stuffing and brute-force attempts with anomaly detection.
Many Accounts Using Same Device Or Ip Making Overlapping Bookings
Your app is vulnerable to multi-account abuse. The network reveals devices linking to multiple accounts with overlapping bookings.
Account-opening solution: Detects fake or mass-created accounts abusing shared devices.
See: https://www.crossclassify.com/solutions/account-opening/
Reused Device Fingerprint Across Unrelated Surnames
Your app is vulnerable to identity sharing or farms. The table shows the same fingerprint across accounts with different surnames.
| deivce_id | account_id | surename |
|---|---|---|
| d2 | a7 | Smith |
| d2 | a12 | Garcia |
| d2 | a19 | Lee |
| d5 | a22 | Patel |
| d5 | a31 | Khan |
Device-fingerprint solution: Flags suspicious reuse of devices across unrelated accounts.
See: https://www.crossclassify.com/solutions/device-fingerprint/
Email Or Phone Change Then Password Reset Within 10 Minutes
Changes and resets are not tightly coupled. The series shows no temporal clustering.
Support Interaction Precedes Credential Change
Few or no changes occur right after tickets. The table shows healthy gaps between tickets and changes.
| ticket_id | contact_channel | change_event | minutes_before_change |
|---|---|---|---|
| T-1001 | chat | email_change | 180 |
| T-1008 | phone | phone_change | 240 |
| T-1022 | password_reset | 360 |
Payment And Booking Fraud
Ip Country Mismatches Departure Airport Country
Origin IP and departure mostly align. The cross-tab has strong diagonals and few off-diagonals.
| Ip_country | DE | GB | NO | SE | US |
|---|---|---|---|---|---|
| DE | 120 | 6 | 4 | 5 | 7 |
| GB | 5 | 135 | 3 | 4 | 9 |
| NO | 4 | 3 | 92 | 6 | 2 |
| SE | 6 | 5 | 7 | 110 | 4 |
| US | 7 | 8 | 3 | 5 | 160 |
Avs Or Cvv Mismatch Rate Spikes On A Bin
Mismatch rates by BIN are stable. Lines stay within normal bounds without spikes.
Short Stay High Value Prepaid Bookings With No Check In
High-price one-night stays don’t cluster in chargebacks. The heatmap’s high cells are not in risky quadrants.
Multiple Disputes From Same Traveler Across Merchants
Disputes are low and evenly distributed. The bar chart has no tall outliers.
Repeated Cancellations Near Free Cancel Deadline
Your app is vulnerable to deadline gaming. The curve spikes in the final minutes before the deadline.
Behavioral-biometrics solution: Detects unusual cancellation behavior close to deadlines.
See: https://www.crossclassify.com/solutions/behavioral-biometrics/
Refunds To Multiple Cards From Same Device
Your app is vulnerable to refund routing. The table shows devices refunding to many different cards.
| device_id | unique_cards |
|---|---|
| d1 | 1 |
| d2 | 5 |
| d3 | 4 |
| d4 | 2 |
| d5 | 6 |
| d6 | 3 |
Device-fingerprint solution: Identifies devices abusing refund mechanisms.
See: https://www.crossclassify.com/solutions/device-fingerprint/
Loyalty And Promotion Abuse
Login Contact Detail Change Same Day Points Transfer
Your app is vulnerable to loyalty drains. Changes peak first and transfers surge shortly after on the same day.
MFA solution: Adds step-up before loyalty redemptions after profile edits.
See: https://www.crossclassify.com/solutions/account-takeover/
Redemptions From Atypical Regions Or Destinations
Redemptions fit normal regional patterns. The heatmap is balanced without odd hotspots.
Multiple Voucher Redemptions From Same Device In 1 Hour
Your app is vulnerable to voucher farming. A device shows short-burst redemption spikes.
Bot-attack solution: Stops automated voucher farming.
Voucher Used Without Qualifying Spend
Most uses meet the threshold. The table shows “qualified” as predominantly true.
| voucher_id | basket_total | min_spend_required | qualified |
|---|---|---|---|
| V1001 | 160 | 150 | true |
| V1002 | 89 | 80 | true |
| V1003 | 120 | 120 | true |
| V1004 | 210 | 200 | true |
| V1005 | 95 | 90 | true |
Book Then Cancel Cycles To Accrue Status Segments
Segments track real travel, not cancellations. Lines don’t co-spike.
Many Bookings Canceled Within 24 Hours Of Booking
Most cancellations occur >24h after booking. The bar distribution favors long-window buckets.
Inventory Content And Fare Manipulation
Excessive Search Requests With Uncommon User Agents
Your app is vulnerable to scraping. “Bot-like” user-agent bars dwarf real browsers.
Bot-attack solution: Prevents scraping via bot user-agents
High Search To Booking Ratio From Ip Ranges
Ratios are within normal bounds. The table shows modest search-to-book values.
| ip_range | searches | bookings | search_to_book_ratio |
|---|---|---|---|
| 10.1.0.0/16 | 1200 | 80 | 15 |
| 10.2.0.0/16 | 950 | 70 | 13.57 |
| 172.16.0.0/16 | 1400 | 100 | 14 |
| 192.168.5.0/24 | 320 | 20 | 16 |
Review Bursts From New Accounts In Same Day
Your app is vulnerable to review stuffing. New-account reviews surge in short bursts.
Account-opening solution: Blocks fake review accounts at signup.
See: https://www.crossclassify.com/solutions/account-opening/
Ip Clusters Posting Across Multiple Properties
IPs map to a few properties only. The network has few multi-edge hubs.
Mismatch Between Quoted And Charged Fare
Deltas are near zero. The table shows parity between quoted and charged fares.
| booking_id | quoted_fare | charged_fare | delta |
|---|---|---|---|
| B-1001 | 199 | 199 | 0 |
| B-1002 | 249.99 | 250 | 0.01 |
| B-1003 | 320.5 | 320.5 | 0 |
| B-1004 | 149 | 149.01 | 0.01 |
| B-1005 | 410 | 410 | 0 |
Unsupported Fare Class Codes In Requests
Invalid fare codes are rare. Bars are near zero.
Bot And Abuse Traffic In Travel Funnels
Greater Than X Requests Per Min To Search Per Device
Your app is vulnerable to polling bots. Devices show sharp request spikes beyond thresholds.
Bot-attack solution: Detects and blocks polling bots.
No Session Depth No Detail Page Views
Sessions have healthy depth. Bars are tall for 3+ pages.
Many Holds Without Purchase Across Routes Or Dates
Holds are steady across routes/dates. The heatmap lacks concentrated hotspots.
Same Device Creates Holds Across Many Accounts
Devices manage holds for single accounts. The table shows low account counts per device.
| device_id | accounts | total_holds |
|---|---|---|
| d1 | 1 | 3 |
| d2 | 1 | 4 |
| d3 | 1 | 2 |
| d4 | 1 | 5 |
| d5 | 1 | 3 |
Very High Clicks With Near Zero Bookings Per Source
Conversion rates by source look consistent. Bars sit within expected ranges.
Abnormal Referrers Or Domains Generating Bounce Traffic
Bounce rates are healthy across referrers. The table shows balanced percentages.
| referrer | visits | bounces | bounce_rate |
|---|---|---|---|
| search.example | 1200 | 420 | 35 |
| meta-a.example | 980 | 392 | 40 |
| aff-c.example | 750 | 315 | 42 |
| news.example | 500 | 210 | 42 |
| email.example | 620 | 248 | 40 |