3 posts tagged with "graphrag"

📌 Key Takeaways

• Overcoming the Field Limitation: Addressing our honest admission from Part 2 that "procurement logs alone cannot identify borrowed-name dummy CEOs and decentralized shells", we built a real-time corporate governance ingestion client (CorporateRegistryClient) integrating DART filings and supreme court corporate registries.
• 2 Preempted Architectural Hurdles:
  1. Resolved identifier fragmentation across Tax IDs (10-digits), Court IDs (13-digits), and DART codes (8-digits) via a 3-way hash index bridge.
  2. Prevented knowledge graph explosion by enforcing governance significance thresholds (executive seats + 50%+ equity).
• 3 Real Production Bugs Resolved:
  1. Circular Cross-Shareholding: Fixed RecursionError crashes during recursive ownership traversals using NetworkX Strongly Connected Components (SCC) cycle condensation.
  2. Newly Minted Shell Blind Spots: Added delta-day calculations (<180 days) to flag entities incorporated immediately prior to high-value bids.
  3. 51:49 Spousal Equity Disguise: Unmasked dummy sole-source privilege fronts via compound family relation graph rules.
• Shipped Verification: Unmasked a $130,000 tender where a single hidden individual owned 80% and held board seats across two seemingly competing bidders; 17/17 test suites passed.

📌 Key Takeaways

• Interim Audit Deployment: Using our production-grade autonomous audit agent (national_budget_ai), we completed a full audit screening across representative public procurement datasets (5 major budget programs and 6 procurement contracts).
• 4 Core Fraud Findings (Strictly Anonymized):
  1. Split Private Contracts: Smurfing contracts under the $20,000 threshold across 12 days to evade competitive bidding ($43,000 total, 94.5% similarity to Supreme Audit rulings).
  2. Shared Address Shell Cartels: 3 dummy vendors sharing the exact same room and building winning exclusive tenders ($158,000 total).
  3. 7x–10x Unit Price Markup: Inflated advisory fees (7.0x median) and GPU server rentals (10.0x median) detected via automated HWPX table parsing.
  4. Low Delivery Efficiency (DEI 30.0%): Only 30.0% of funds reached target beneficiaries, with 70.0% absorbed in administrative overhead.
• System Performance Evaluation:
  • Precision: 0.00% financial calculation hallucination via deterministic SQL AST parsing; 11/11 test suites passed.
  • Throughput: 0.03s per contract to generate complete audit dossiers and statutory enforcement drafts.
  • Field Limitations: Blind spots in borrowed-name owners and multi-tier private subcontracts identified.

📌 Key Takeaways (TL;DR)

• The Goal: Build an autonomous AI agent to audit $500B annual public expenditures, tracking government procurement contracts (KONEPS), fiscal databases (dBrain+), and messy Korean HWPX budget tables to detect price gouging, split contracts, and paper-company cartels.
• Anticipated Challenges & Prevention: Prevented LLM numerical hallucinations by delegating calculations to a Deterministic Text-to-SQL Engine, while isolating relationship and cartel tracking to a Heterogeneous Knowledge Graph (GraphRAG) in a dual-brain architecture.
• Actual Bugs Encountered & Solved: (1) Solved company name and street address fragmentation breaking cartel detection via a robust EntityResolver, (2) Caught temporal split contracts slipping past date filters via a 30-day sliding window algorithm, (3) Overcame auditor distrust of AI black-boxes by building an XAI Dossier Generator binding findings 1:1 with statutory clauses and 10-year official audit precedents.
• Verification: 11 out of 11 regression test harness suites passed, successfully exposing a 38.5M KRW shared-address private procurement cartel and a 70% indirect overhead leakage (DEI score 30%).