Suresh Arumugam@hserus1985

Every year, financial institutions fail to detect over $100 billion in illicit transactions. Traditional rule-based systems miss sophisticated money laundering schemes that evolve daily. 95% of AML alerts are false positives Manual investigation costs $10M+ annually for mid-size banks Compliance teams drowning in alerts, real threats slip through We built Regulus AI to solve this problem with intelligent, real-time transaction monitoring. Watch as transactions flow through our system in real-time. Each transaction gets analyzed across multiple dimensions: This customer typically spends $500/day locally. Suddenly $50K transferred internationally? Red flag. Show profile comparison before/after Our agents map transaction relationships. This customer just received funds from an account linked to 5 other suspicious profiles. Show network visualization Instead of 10,000 alerts, we generate 50 high-confidence cases with confidence scores and risk breakdown. Show risk score calculation (behavioral + network + contextual) Each flagged case includes AI-generated documentation that investigators can act on immediately: Our document generator creates comprehensive case files with: Transaction timeline Network relationships Behavioral anomalies Regulatory indicators (CTF, STR triggers) We identify 40% of alerts as likely false positives and deprioritize them automatically. Show comparison: Traditional system vs Regulus Save investigators 4-6 hours per day The system recommends actions: Block, Escalate, or Monitor. Show decision history and accuracy metrics Investigators approve in seconds, not hours

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