Traditional fraud detection catches only 60% of attacks and generates overwhelming false positives. Our AI detects 95% of fraud in real-time—subscription fraud, SIM swaps, account takeovers, IRSF—while reducing false positives by 82%.
Telecom fraud costs the industry $39.89 billion annually. Traditional rule-based detection systems miss sophisticated attacks and burden operations teams with false alarms.
Global telecom fraud losses exceed $39.89B annually, with subscription fraud and IRSF causing the most damage.
Traditional systems detect fraud an average of 18 hours after it starts, allowing significant financial damage.
Rule-based systems generate 78% false positive rate, overwhelming fraud teams and blocking legitimate customers.
Fraudsters constantly adapt tactics—SIM swaps, account takeovers, synthetic identities—faster than rules update.
Boaweb AI analyzes billions of behavioral signals in real-time to detect fraud patterns that rule-based systems miss, stopping attacks instantly while minimizing false positives.
Our AI monitors every transaction, call, message, and account action across your network—analyzing 300+ behavioral signals per event including usage patterns, location data, device fingerprints, calling patterns, payment behavior, and account changes. Machine learning establishes normal behavior baselines for each customer and immediately flags anomalies that indicate fraud: sudden international calls from customers who never call abroad, device changes combined with unusual spending, or access from impossible geographic locations.
The system simultaneously detects multiple fraud types: subscription fraud (fake identities, stolen credentials), SIM swap attacks (unauthorized SIM replacements for account takeover), international revenue sharing fraud (IRSF), premium rate service fraud, account takeovers, call pumping, wangiri fraud, and roaming fraud. Each fraud type has unique behavioral signatures that AI recognizes—for example, SIM swaps typically show device change + location change + account modification attempts within minutes.
Each transaction receives a fraud risk score (0-100) based on behavioral analysis, historical patterns, and contextual factors. High-risk transactions (score 85-100) are automatically blocked in real-time, preventing fraud before any financial damage occurs. Medium-risk events (50-84) trigger additional verification steps like two-factor authentication or account review. Low-risk transactions proceed normally. This risk-based approach stops fraud instantly while minimizing customer friction for legitimate activity.
AI identifies coordinated fraud campaigns by analyzing patterns across your entire customer base. The system detects fraud rings—groups of fraudulent accounts operating together, bulk SIM swap attacks targeting multiple customers, organized IRSF schemes routing traffic through premium numbers. When the AI identifies one account in a fraud ring, it automatically flags related accounts, enabling proactive blocking of entire fraud operations rather than reacting to individual incidents.
Machine learning models continuously evolve as fraudsters change tactics. The AI analyzes confirmed fraud cases to identify new attack patterns, adjusts detection models automatically, and stays ahead of emerging fraud techniques. Fraud analysts provide feedback on false positives and false negatives, which the system uses to improve accuracy. This continuous learning means detection rates improve over time while false positives decrease—unlike rule-based systems that require manual updates and degrade as fraud evolves.
See how AI fraud detection can prevent 95% of attacks in real-time while reducing false positives by 82%. Get a free analysis of your fraud risk and potential savings.
A Central European mobile operator with 3.8 million subscribers was losing €14.3M annually to fraud—primarily subscription fraud (fake accounts using stolen identities), SIM swap attacks enabling account takeovers, and international revenue sharing fraud (IRSF). Their rule-based fraud system detected only 62% of fraud cases and generated 2,400 false positive alerts daily, overwhelming the 12-person fraud investigation team.
Boaweb AI Solution: We deployed machine learning fraud detection that analyzes real-time behavioral signals across all transactions, calls, messages, and account activities. The AI monitors 300+ fraud indicators per event and assigns risk scores to every action. High-risk transactions are automatically blocked within 200 milliseconds, while medium-risk events trigger additional verification steps.
Results after 12 months: Fraud detection rate increased from 62% to 95% (blocked €12.4M in fraud attempts). Annual fraud losses decreased from €14.3M to €1.9M (87% reduction). False positive alerts dropped from 2,400 to 430 daily (82% reduction), allowing the fraud team to focus on complex investigations. SIM swap attack detection improved from 45% to 97%—the AI identified SIM swaps within an average 47 seconds based on behavioral anomalies. The operator also identified and dismantled three major fraud rings operating across their network.
AI analyzes every transaction in real-time (typically 100-200 milliseconds) by evaluating hundreds of behavioral signals simultaneously—something rule-based systems can't do. Traditional systems check transactions against predefined rules sequentially, which is slower and misses sophisticated fraud that doesn't trigger specific rules. AI identifies subtle anomaly patterns instantly and blocks fraud before any financial damage occurs.
The AI detects all major fraud types: subscription fraud (fake accounts, stolen identities), SIM swap attacks, account takeovers, international revenue sharing fraud (IRSF), premium rate service fraud, roaming fraud, call pumping/traffic pumping, wangiri fraud, PBX hacking, number hijacking, and coordinated fraud rings. Each fraud type has unique behavioral signatures that machine learning recognizes across historical and real-time data.
AI understands context and normal behavior patterns for each customer, rather than applying rigid rules that trigger false alarms. For example, a customer traveling abroad would trigger rule-based alerts, but AI recognizes patterns like booked travel, gradual location changes, and normal spending behavior. Machine learning continuously learns what's normal vs. suspicious for different customer segments, dramatically reducing false positives (typically 75-85% reduction).
Yes. The AI uses anomaly detection to identify unusual behavioral patterns that don't match normal customer activity or known fraud signatures. When fraudsters introduce new tactics, the AI flags anomalous behavior for investigation. Fraud analysts review these cases and provide feedback, which the system uses to learn new fraud patterns. This continuous learning ensures the AI adapts to evolving fraud tactics faster than manual rule updates.
Most telecom operators reduce fraud losses by 80-90% within 12 months, translating to millions in saved revenue. Additionally, reducing false positives by 75-85% decreases fraud investigation costs by 40-60% and improves customer experience by not blocking legitimate transactions. For a mid-sized operator losing €10M annually to fraud, AI typically saves €8-9M in prevented fraud plus €2-3M in operational efficiency. ROI payback period is typically 4-8 months.
Stop losing millions to telecom fraud. Get an AI-powered fraud detection system that blocks 95% of attacks in real-time, reduces false positives by 82%, and adapts continuously to evolving fraud tactics.
Includes fraud risk assessment and ROI projection.