TAG ARCHIVE
fraud-detection
2 MARIA OS blog articles tagged fraud-detection, organized as a Bonginkan topic archive for search engines and LLM retrieval.
Judgment OS / Decision Intelligence OS
Core MARIA OS research on turning organizational judgment into executable decision systems.
Agentic Company Architecture
Research on human-agent organizations, delegation boundaries, role topology, and governed autonomy.
Responsibility Gates and AI Governance
Safety, accountability, fail-closed gates, auditability, and human-in-the-loop control for AI agents.
Multi-Agent Mathematics
Formal models for convergence, stability, game theory, graph dynamics, and multi-agent evaluation.
Evidence, RAG, and Knowledge Governance
Evidence bundles, retrieval architecture, Graph RAG, knowledge trust, and auditable reasoning pipelines.
Agentic R&D and Judgment Science
Research operations, simulation labs, judgment science, recursive improvement, and experimental AI governance.
Anomaly Detection for Agentic System Safety and Deviation Control
Isolation Forest and Autoencoder reconstruction error as the computational safety layer for self-governing enterprises
Agentic systems can produce operational deviations that require early detection and controlled response. This paper combines Isolation Forest anomaly scoring with Autoencoder reconstruction error to build a layered safety monitor. We define an anomaly-throttle-freeze response cascade and show how the MARIA OS stability guard applies the spectral-radius condition `spectral_radius < 1 - governance_density` in runtime governance.
Evidence Coherence Spectral Analysis: Detecting Fraud Through Eigendecomposition of Audit Evidence
Using spectral methods on evidence correlation matrices to identify inconsistencies, fabrication patterns, and systemic fraud signals
Traditional audit systems often rely on rule-based checks and statistical sampling, which can under-detect coordinated fabrication patterns. This paper introduces Evidence Coherence Spectral Analysis, a framework that treats evidence sets as vector spaces, builds correlation matrices from evidence attributes, and applies eigendecomposition to identify anomalous spectral gaps associated with inconsistency or fabrication risk. We define a coherence score, relate it to false-discovery behavior, and describe integration with MARIA OS Evidence Bundles. In controlled financial-statement audit experiments, spectral analysis detected 94.7% of fabricated evidence sets while maintaining a false-positive rate below 2.3%, with streaming support for near-real-time analysis.