About This Architecture

Fisc Radar is a five-layer intelligent architecture for automated tax risk detection, integrating public data sources, NLP analysis, and ML-based scoring to prioritize audit cases. Data flows from Google Maps, social media, commercial registries, and internal tax databases through collection, validation, normalization, and enrichment layers before entering AI/NLP modules for entity extraction, anomaly detection, and risk classification. The system extracts behavioral indicators—luxury markers, transaction patterns, pricing signals—and cross-references declared income against digital footprints to flag discrepancies with confidence scores. A weighted scoring engine (0–100 scale) categorizes risk levels and generates daily prioritized audit lists with timestamped evidence, enabling tax auditors to focus on high-impact cases efficiently. Fork this diagram on Diagrams.so to customize thresholds, add regional data sources, or integrate with your audit workflow platform.

People also ask

How can tax authorities automate fraud detection and prioritize audits using AI and multi-source data integration?

Fisc Radar demonstrates a five-layer architecture that ingests public data (Google Maps, social media, registries), validates and normalizes it, applies NLP for entity extraction and anomaly detection, trains ML models to classify risk, and generates weighted risk scores (0–100) to prioritize daily audit lists. This approach enables auditors to focus on high-impact cases by cross-referencing decla