How IBM Safer Payments Works: An Educational Analysis

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1. What is IBM Safer Payments?​

IBM Safer Payments is an AI platform for real-time payment transaction analysis and fraud detection. Used by:

• Banks (Visa, Mastercard, major European banks)
• Payment systems
• Crypto exchanges (for AML control)

Key indicators (2024):

• Processes 10,000+ transactions per second
• Average analysis time is 20 ms
• Accuracy: Reduces false positives by 40% vs. traditional systems

2. System architecture​

A. Data Sources​

Data type	Usage
Transactional	Amount, MCC code, geolocation, device
Behavioral	Frequency of transactions, typical amounts, time of day
External threats	STIR/SHAKEN for telephony, blacklists of cards
Network connections	Account/Device Relationship Graphs

B. Decision-making mechanism​

1. Anomaly detection (AI models)​

• Isolation Forest - reveals "invisible" patterns:
  

  # Example: Detecting a suspicious series of transactions
  if transaction.amount > avg_spend * 10 and country != home_country:
  flag_as_fraud()

• LSTM networks predict fraud based on time series.

2. Rules (Rules Engine)​

Example of a hard rule for carding:
SQL:

IF card_used_in_3_countries_within_1h
AND device_id NOT IN trusted_devices
THEN risk_score = 900 (blocking)

3. Risk Score​

Each transaction is assigned a score:

• 0–300 → Approve
• 301–700 → Additional verification (3DS, OTP)
• 701–1000 → Block + alert in SOC

3. How does Safer Payments catch fraudsters?​

Example 1: Carding via online stores​

• Detection:
  • 50 cards from one BIN buy electronics in one store
  • Same User-Agent and IP-subnet
• Action: Freeze all payments with these cards

Example 2: Multi-account fraud​

• Detection:
  • 100+ accounts with one IMEI phone
  • Clustering by relationship graph (common recipients)
• Action: Cluster Blocking + Data Transfer to FinCEN

4. Why is the system difficult to bypass?​

Countermeasures against popular attacks​

The method of scammers	How IBM Safer Payments Responds
VPN/Tor	IP Reputation Analysis + Matching with Phone GPS
Device ID substitution	Detecting Emulators via Google SafetyNet API
Smurfing (splitting amounts)	Pattern Detection via Snake's Algorithm
Social engineering	Analysis of OTP input behavior (speed, errors)

Performance statistics (IBM, 2024):

• 94% of cloned card attacks are blocked
• 89% of cryptocurrency cashout attempts are detected by chains

5. Legal study of the system​

For security researchers:​

1. IBM Sandbox - a test environment with demo data (requires a corporate account).
2. AML courses - for example, ACAMS certification includes case studies.
3. Documentation:
   • IBM Safer Payments Whitepaper
   • NIST SP 800-63 Guide to Digital Identification.

Example of setting up a rule for a bank:​

// Blocking on suspicion of POS skimming
IF transaction.type = "POS"
AND card_used_in_another_country_last_24h
AND merchant_risk_score > 700
THEN decline_transaction()

6. Comparison with analogues (2025)​

System	IBM Safer Payments	FICO Falcon	Featurespace ARIC
Processing speed	10K TPS	8K TPS	5K TPS
AI models	LSTM + Isolation Forest	Random Forest	Bayesian Networks
Crypto integration	Yes	No	Yes

This material demonstrates how modern AI solutions make financial fraud extremely risky . For professional development in the field of fraud analysis, study:

• PCI DSS - Payment Data Security Standards
• AML regulations (FATF, 6th EU Directive)
• Ethical hacking (eg OSCP certification ).

Need a specific case analysis? Ready to help!

The material is based on official IBM documentation, bank cases and research in the field of AML monitoring. The information is intended for studying anti-fraud systems.