WauGuard.AI
The Guardian of the Digital Sky
Stop losing money to fraud. Stop drowning in false positives.
WauGuard.AI gives your compliance team instant, explainable AI investigations — with BNM-ready STR reports generated in one click. No manual drafting. No black-box decisions. Every alert comes with a full audit trail a BNM examiner can follow.
1-Click
BNM STR Report
Generated automatically — zero drafting
< 100ms
Time to fraud decision
Real-time blocking before money leaves
AWS MY
100% Data Residency
Stays in Malaysia — BNM compliant
≥98%
AI review accuracy
Replaces manual first-pass investigation
Features
Legacy systems flag anomalies and stop there. WauGuard.AI gives your compliance team explainable decisions, fewer false positives, and STR reports written automatically.
Every fraud determination comes with ranked SHAP feature contributions, the rule that fired, and an LLM-written rationale. BNM examiners, auditors, and courts get a traceable reasoning chain — not just a score.
Three intelligence layers — deterministic rules, XGBoost ML, and Neo4j graph context — filter noise before it reaches an analyst. Only the alerts that matter land on your team's desk.
BNM-aligned Suspicious Transaction Reports are generated instantly — Parts A through F, fully formatted, FIED-ready. One click. Zero analyst drafting time.
Pain Points Solved
BNM pressure is intensifying. Fraud losses are growing. Analyst headcount cannot keep pace. WauGuard.AI was built to solve these exact problems.
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.
AI Highlights
Three AI tiers working in sequence — each layer adds context the previous one cannot see. Fast enough for real-time blocking. Deep enough for BNM audit.
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.
Investigator Journey
Every flagged or blocked transaction surfaces as a case. No triage needed — risk level, ML score, and rule trigger are already attached.
The Multi-Agent AI Framework has already written the full forensic brief — graph community risk, SHAP evidence, velocity pattern, and a compliance-ready narrative. The STR PDF is one click away.
Investigators review the AI reasoning, add notes, and record their verdict. The complete decision chain — AI + human — is stored, timestamped, and audit-ready for BNM.
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
Log in and run your first AI-powered fraud investigation — with a BNM-ready STR in one click.
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