WauGuard.AI
The Guardian of the Digital Sky
A production-grade fraud detection platform combining ML at scale, graph network detection, and LLM-automated reasoning — making every decision explainable, auditable, and defensible.
3-Tier
Hybrid Cascade Engine
ML → Graph → LLM
< 100ms
Time to decision
per transaction
AWS MY
100% Data Residency
ap-southeast-5 region
≥98%
Automated fraud review accuracy
Multi-agent AI consensus
Architecture
Three sequential intelligence tiers — each building on the last. Speed without sacrificing depth. Scale without sacrificing explainability.
ML Engine
XGBoost · SHAP
15-feature XGBoost classifier scores every transaction in microseconds. SHAP values are computed alongside every prediction — providing the explainability mandatory under BNM AML/CFT.
Graph Engine
NetworkX · Neo4j
Account, device, and merchant nodes are linked in a graph. Louvain community detection identifies fraud rings — exposing mule networks that transaction-level ML cannot see alone.
Multi-Agent AI Framework
3-Agent Claude Consensus
Three sequential Claude agents — Graph Sentry, ML Specialist, and Lead Auditor — each analyse a different evidence dimension before synthesising into a BNM-ready forensic brief. One-click SAR export included.
Why WauGuard.AI
Legacy systems flag anomalies and stop there. WauGuard.AI tells your team exactly what happened, why it is suspicious, and what to do next — powered by three intelligence layers working in concert.
Suspicious transactions are assessed and hard-blocked before money leaves the account. Deterministic rules fire first — ML and graph enrichment follow for every flagged case.
Shared devices, compromised merchants, and coordinated mule rings are invisible to single-transaction ML. WauGuard.AI's graph engine surfaces entire fraud communities across accounts.
BNM-aligned Suspicious Activity Reports are generated instantly — no analyst drafting time. One-click export of a fully formatted STR/SAR PDF, ready for FIED submission.
The Business Case
Malaysian financial institutions face mounting pressure from BNM to reduce fraud losses while cutting compliance overhead. WauGuard.AI addresses both.
The Pain
Every flagged transaction requires an analyst to manually review evidence, draft a Suspicious Activity Report, and file with BNM FIED. At scale, this is a headcount problem — not a fraud problem.
The Solution
The Multi-Agent AI Framework writes the complete forensic brief automatically — graph assessment, ML evidence, and regulatory recommendation. Analysts review and confirm; they no longer draft. One click exports a fully formatted BNM STR/SAR PDF.
The Pain
Legacy ML models produce a risk score with no explanation. BNM examiners, auditors, and courts require a traceable reasoning chain — not just a number. Opaque models create regulatory and reputational liability.
The Solution
Every determination is grounded in SHAP feature contributions, named rule triggers (R001–R007), graph community evidence, and an LLM-written rationale. The complete reasoning chain is stored, timestamped, and exportable for any BNM audit.
The Process
Every payment is assessed the instant it is initiated — in real time, at any volume. Rule engine fires first for deterministic hard blocks.
ML scores the transaction. Graph engine enriches with network-level context. The Multi-Agent AI Framework — three Claude agents in sequence — writes the full forensic brief for high-risk cases and generates a SAR-ready PDF.
Analysts open the Investigation Center, review AI insights and graph visualisations, then confirm or dismiss — with the complete reasoning chain always visible.
Three sequential Claude agents each analyse a distinct dimension of the evidence: Graph Sentry evaluates the account's community risk, ML Specialist interprets SHAP contributions and velocity signals, and Lead Auditor synthesises both into a compliance-ready forensic brief — in seconds, not analyst-hours.
[Risk Assessment]
Transaction TX-8A4F2C91 is assessed as HIGH risk with fraud probability 0.8731. Rule R003 triggered on high-risk merchant category.
[Key Evidence]
[Recommended Action]
Escalate to senior analyst for enhanced due diligence review.
Infrastructure
All transaction data, fraud reports, and investigator notes are stored and processed exclusively within AWS ap-southeast-5 (Malaysia) — satisfying BNM data localisation requirements without compromise.
AWS ap-southeast-5
Malaysia region — data never leaves MY
RDS PostgreSQL 16
Multi-AZ, managed backups
Neo4j on EC2
Graph DB in-region
VPC isolation
No public DB endpoints
Cost-Optimised for Financial Institutions
WauGuard.AI eliminates dedicated fraud analyst headcount for first-pass investigation. LLM-as-a-Service economics mean the cost per investigation brief is a fraction of manual review — scaling linearly with transaction volume, not headcount.
Built for Malaysian Financial Regulation
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