Transaction Monitoring

Most transaction monitoring systems operate on rules. If transaction amount exceeds threshold X, flag it. If velocity increases by Y percent, create alert. If geographic pattern changes, escalate. These rules generate thousands of alerts daily. Banks hire armies of analysts to review them.

The problem isn't the volume—it's that 95% of alerts are false positives. A customer sending wedding expenses triggers structuring alerts. A business expanding internationally creates geographic risk flags. Seasonal revenue spikes generate velocity alerts. Your analysts spend entire days clearing innocent activity instead of investigating real threats.

Rules can't understand context. They see a $9,000 transaction and flag it as potential structuring, but they can't tell if it's someone splitting a $20,000 car purchase into multiple payments or a money launderer deliberately staying under reporting thresholds.

Traditional monitoring also misses sophisticated schemes. Layering transactions across multiple accounts, structuring through business entities, timing transfers to exploit monitoring gaps—these patterns fly under rule-based thresholds. By the time analysts piece together the full picture, funds have already moved.

The cost grows linearly with transaction volume. Process 10 million transactions monthly, you need 50+ analysts. Double your transaction volume, you need double the headcount. The model doesn't scale.

Mirsad AI monitors every transaction in real-time with cognitive reasoning that understands behavior, not just patterns. It doesn't replace your existing transaction monitoring system—it enhances it. Your TM system generates alerts. Mirsad investigates them autonomously.

Rasid analyzes each alert with full context. It examines the transaction against the customer's complete behavioral history—spending patterns, income sources, business operations, geographic connections, relationship networks. It asks: Is this behavior consistent with what we know about this customer? Are there legitimate explanations? What would cause suspicion?

For a $9,000 transaction, Rasid doesn't just see the amount. It sees that the customer recently inquired about car loans, has consistent salary deposits, and this is their first large purchase in six months. It understands this is a car down payment, not structuring. Case cleared with complete documentation.

For cross-border transfers, Rasid examines business relationships, trade patterns, and invoice matching. A manufacturing company sending $500K to Vietnam gets flagged by rules. Rasid traces the payment to a verified supplier relationship with six months of consistent invoicing. It confirms shipping documentation matches payment timing. Transaction context makes sense—cleared.

Muhaqqiq handles complex cases. When transaction patterns show deliberate structuring, layering across accounts, or timing that suggests exploitation of monitoring gaps, Muhaqqiq conducts extended analysis. It traces funds through multiple entities, identifies beneficial owners, maps relationship networks, and determines if behavior indicates money laundering.

Muhtasib prepares regulatory filings. When Muhaqqiq identifies genuine suspicious activity—layering schemes, trade-based money laundering, organized structuring—Muhtasib prepares complete Suspicious Transaction Reports with jurisdiction-specific templates, full transaction histories, and documented reasoning ready for your compliance officer's signature.

The system operates continuously. Transactions are analyzed the moment they occur. No batch processing overnight. No waiting for end-of-day reconciliation. Real-time monitoring with real-time investigation.

Alert backlogs disappear. Rasid processes alerts the moment they're generated. What took your team days to clear now resolves in minutes. Analysts no longer spend entire days reviewing innocent transactions—95% of routine alerts are cleared autonomously with complete audit trails.

False positive rates drop by 60%+ because the system understands context. It recognizes wedding expenses, business expansions, and seasonal patterns that rule-based systems flag incorrectly. Your team investigates genuine risks instead of clearing false alarms.

Detection quality improves. Muhaqqiq identifies sophisticated layering schemes and structuring patterns that slip through rule-based thresholds. It traces transaction chains across accounts and entities, mapping networks that individual analysts might miss. Real threats get flagged faster with better evidence.

Your analysts transform from alert processors to strategic investigators. Instead of clearing routine cases, they handle complex investigations, refine detection models, and build regulatory relationships. The team focuses on high-value work that requires human judgment—not volume processing.

Cost structure changes fundamentally. Traditional monitoring teams grow linearly with transaction volume. Mirsad scales with software economics. When transaction volume doubles, you add digital capacity, not headcount. No recruiting. No training. No turnover.

Integration connects Mirsad to your existing transaction monitoring system via API. Your TM system continues generating alerts. Mirsad receives them in real-time and begins autonomous investigation. No replacement of infrastructure—we enhance what you already have.

Weeks 1-2: API connectivity and alert feed configuration. We map your alert types, transaction data structures, and customer profile fields. Test environment setup with sample data.

Weeks 3-4: Risk rule calibration. Your team configures thresholds, defines escalation criteria, and sets risk rating logic. We tune the system to match your institutional risk appetite and regulatory requirements.

Weeks 5-12: Parallel testing. Both systems run independently. Your analysts continue reviewing alerts through existing processes. Mirsad investigates the same alerts simultaneously. You compare decisions, validate accuracy, and measure false positive reduction.

Go-live criteria: 98%+ decision accuracy compared to your analysts, 60%+ false positive reduction, <1 minute average alert resolution time, 100% complete audit documentation. Production deployment only after you validate these metrics.

Your data remains in your environment. All customer profiles, transaction histories, and investigation records stay on your infrastructure. We push model updates and collect anonymized performance metrics—zero access to transaction data or customer PII.