AI Gauntlet Stops BIN Attack: 99.7% Fraud Block on Prepaid Card Network
How a layered agentic AI fraud detection system — combining terminal fingerprinting, behavioral sequencing, geo-velocity, and real-time decisioning — neutralized a sophisticated BIN attack before it caused catastrophic loss.
Jimmy Standaert
AI Fraud Architect · Stop It Before It Starts
Layered AI defense systems for payment networks
Executive Summary
A regional prepaid card issuer was hemorrhaging funds through a coordinated BIN (Bank Identification Number) attack — fraudsters systematically testing card numbers against their network using automated bots across hundreds of IPs. RiGi GROUP deployed a multi-layer AI fraud gauntlet that sequenced nine distinct detection signals in real time, blocking 99.7% of fraudulent authorization attempts within 72 hours of deployment. Total fraud loss averted in the first 30 days exceeded $2.3M.
The Challenge
BIN attacks exploit the predictable structure of card numbers. Fraudsters generate thousands of sequential card numbers from a known BIN prefix and use bots to test small-value authorizations — often $0.00 or $1.00 — at real terminals until they find valid, funded cards. Traditional rule-based fraud systems were too slow and too rigid to catch the attack in real time.
14,000+ authorization attempts per hour at peak attack volume
Attacks distributed across 340+ unique IPs, 12 countries
Bot patterns mimicked legitimate low-value authorization flows
Existing velocity rules were blind to the distributed, low-per-IP pattern
Valid cards being harvested and sold on dark web markets within hours
Estimated exposure: $4.1M in funded card balances at risk
The AI Fraud Gauntlet — 9 Layers of Defense
RiGi GROUP architected a living, learning fraud detection system that sequences nine independent AI signals into a unified decisioning engine — catching attacks that no single rule could detect alone.
01
BIN Sequence Pattern Recognition
- Trained a sequence model on authorization attempt order — legitimate users have random card number distributions; BIN attacks show tight sequential clustering
- Flagged any cohort where card numbers fell within a ±500 range of a previous attempt within 60 seconds
02
Terminal Identity Fingerprinting
- Built dynamic terminal trust scores from terminal ID, acquirer ID, MCC code, and hardware clock drift patterns
- Terminals processing 3+ unique BIN-range attempts in 5 minutes had trust scores degraded and all future transactions escalated
03
IP Reputation + Proxy/VPN Detection
- Real-time scoring against live threat intelligence — residential proxies, datacenter ranges, Tor exit nodes, VPN providers
- Composite IP Risk Score combining IP age, ASN reputation, and historical fraud association; scores above 72/100 triggered step-up verification
04
Geo-Velocity Impossibility Detection
- Tracked physical location of each authorization — flagged physically impossible card usage (e.g., Dallas at 2:04 PM, Frankfurt at 2:11 PM)
- 1,200+ geo-velocity violations caught in week one alone
05
Authorization Amount Micro-Pattern Analysis
- Flagged micro-authorization patterns: any card seeing 2+ sub-$2.00 attempts within 10 minutes was marked as a probe target
- This single signal alone caught 34% of total attack volume
06
Device + Browser Fingerprint Correlation
- For card-not-present channels, fingerprinted devices using canvas rendering, WebGL hash, timezone, fonts, and screen resolution
- Detected fraudsters reusing the same device across dozens of card attempts even when rotating IPs and cookies
07
Behavioral Velocity Sequencing
- Built session graphs connecting all authorization events by shared attributes (IP subnet, terminal cluster, BIN range, time window)
- Graph model scored entire sessions — catching low-rate distributed attacks that individually looked clean
08
Customer Behavioral Baseline Deviation
- Established behavioral baselines for active cards: average transaction size, merchant categories, home geo, usual transaction times
- Deviation beyond 2 standard deviations on 3+ signals triggered real-time soft decline and cardholder push notification
09
Adaptive Rate Limiting with Honeypot Tokens
- Deployed synthetic honeypot card numbers seeded into the BIN range — looked valid to attackers but triggered immediate silent alerts
- Any terminal or IP hitting a honeypot was shadowbanned to 1 auth per 10 minutes, slowing bots to a crawl while gathering intelligence for law enforcement
Performance Metrics — Before vs. After
Metric
Before
After
Change
Target
Peak Auth Attempts/Hour
Before
14,200
After
38
Change
99.7% reduction
Target
< 100/hr
Fraudulent Auths Approved
Before
312/day
After
1/day
Change
99.7% reduction
Target
0/day
Geo-Velocity Violations Blocked
Before
0
After
1,200+/week
Change
New capability
Target
Active
Honeypot Trigger Rate
Before
N/A
After
847 caught
Change
New capability
Target
Active
False Positive Rate
Before
N/A
After
0.3%
Change
Baseline
Target
< 0.5%
Mean Time to Detect Attack
Before
> 4 hours
After
< 90 seconds
Change
97% faster
Target
< 2 min
Cards Compromised (30-day)
Before
Est. 2,100+
After
6
Change
99.7% reduction
Target
< 10/mo
Fraud Loss Averted (30-day)
Before
$0 blocked
After
$2.3M blocked
Change
Full protection
Target
Active
Key Detections Achieved
Real-time BIN sequence clustering detection
Terminal trust scoring with degradation engine
Geo-velocity impossibility enforcement
Honeypot card network deployed and active
Session graph behavioral correlation
Device fingerprint cross-card linking
Business Impact
The AI gauntlet turned the fraud attempt into an intelligence goldmine — terminal fingerprints, IP clusters, and session graphs were packaged into a law enforcement referral that identified the fraud ring's operational infrastructure
The issuer avoided an estimated $4.1M in potential total exposure; the system now runs continuously, self-tuning thresholds weekly using reinforcement feedback
Cardholder trust preserved — no public breach disclosure required, and the issuer's fraud rate dropped below the card network's threshold, avoiding $180K/year in excessive fraud fines
Why RiGi GROUP for AI Fraud Defense?
RiGi GROUP doesn’t deploy off-the-shelf fraud rules. We architect living, learning systems that think in sequences, graphs, and behavioral baselines — not just individual transactions. Our AI gauntlet approach layers nine independent signals into a unified decisioning engine that adapts faster than fraudsters can pivot. For prepaid issuers, fintechs, and payment processors facing evolving attack vectors, we deliver fraud infrastructure that grows smarter with every transaction.
Next Steps & Recommendations
- Expand honeypot token network across full BIN range
- Integrate dark web monitoring for harvested card number alerts
- Deploy chargeback prediction model for dispute pre-emption
- Extend behavioral baseline model to merchant-side terminals
- Quarterly red team exercises simulating next-generation BIN attack variants
Ready to build your AI fraud gauntlet?
Contact Jimmy Standaert today for a threat assessment.