Chew Ming Yau@Roycen

I use 3 layer hybrid architecture to handle both structured and unstructured data: Layer 1: Data Acquisition (Custom Skills): We built OpenAPI integrations to simulate accessing Internal Ledger (ERP), Open Banking APIs, and Government Compliance (SSM/LHDN sim). Layer 2: Orchestration (Skill Flows): This layer utilizes an Agentic Workflow to automatically fetch, sequence, and map data from all three isolated sources, guaranteeing stability and efficiency. Layer 3: Intelligence: The AI Agent receives the unified data packet and uses foundation models to perform forensic logic reasoning Business Value: The solution provides immediate ROI by: Preventing Fines: Ensures 100% compliance with statutory contribution rates (KWSP/LHDN). Eliminating Fraud: Stops payments to shell companies instantly. Increasing Efficiency: Reduces manual audit time from days to seconds by using automation. I use a scenario that involves a high-value payment for services: Amount: RM 250,000 Stated Purpose: Consultancy Fees Claimed Vendor (ERP): GHOST TECH SDN BHD AI successfully detects three major red flags by cross-referencing the three microservices: Red Flag 1: Shell Company Risk (SSM Check) Finding: The vendor, GHOST TECH SDN BHD, is listed with an SSM Status of "DORMANT (No Activity)". A company with no activity cannot legitimately provide RM 250,000 worth of consulting services. This indicates a high probability of a Shell Company being used to generate a fake expense. Red Flag 2: Identity Mismatch (Bank Trace) Finding: Although the ERP ledger claims the money went to the company, the actual beneficiary of the RM 250,000 transfer was "TAN AH KOW ". Red Flag 3: Tax Non-Compliance (LHDN Check) Finding: The vendor's LHDN compliance status is False. This confirms a high risk of Tax Evasion, as the counterparty is likely hiding the income, exposing the purchasing company to LHDN scrutiny. issue a CRITICAL FREEZE based on this overwhelming evidence.

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