Plataforma de CTI Assíncrona focada em Crimes Financeiros (Pix/Cripto) e Compliance Regulatório (Lei 14.790). Powered by LangGraph & AsyncIO.

A deep exploration of how human psychology shapes fraud behavior and how those patterns become measurable signals in transaction data. This article reveals the behavioral, cognitive, and economic forces behind fraud, explaining how ML models detect deviations, anomalies, and intent hidden within financial transactions.

An analysis of the released data on FinCrime Files transactions as depicted on SARs.

Official AMLTRIX Data Exports – Versioned STIX 2.1 bundles, CSVs, XLSX files, and ATT&CK Navigator layers representing the latest AMLTRIX knowledge graph of adversarial behaviors in financial crime. See framework.amltrix.com for methodology and usage.

AFiCATo - Anti Financial Crimes Analysis Tools

Some Deep Learning for Financial Crime Experiments

White-Collar-Criminals.com official website related to the criminal RICO case targeting McDonald’s Corporation.

This category delves into the world of criminal activity and justice analytics, using data to uncover hidden patterns and trends in areas such as drug trafficking, money laundering, fraud, cybercrime, and human trafficking.

SQL-based KYC & Risk Profiling Engine simulating bank-style customer risk assessment using rule-based AML logic and FATF-aligned risk indicators

Advanced EDA for money mule account detection in banking data. 7.4M transactions, multi-table analysis, behavioral pattern profiling. RBIH x IIT Delhi National Fraud Prevention Challenge Phase 1.

Financial crime investigation platform that detects suspicious transactions, analyzes transaction networks using graph analytics, and generates Suspicious Activity Reports (SAR) through an interactive investigation dashboard.

SQL-based AML transaction monitoring engine simulating rule-based detection and alert generation.

Professional toolkit for crypto investigations, AML/CTF intelligence, blockchain risk analysis, and compliance research.

False-Positive Reduction Lab : rule-based transaction monitoring with threshold tuning and cost trade-offs. Demonstrates how adjusting detection rules reduces noise, lowers investigation cost, and improves fraud catch.

Practical repository for transaction monitoring scenarios, red flags, suspicious activity indicators, and AML-focused analytical review.

Production-grade AML transaction monitoring system — XGBoost fraud detection with rank-based tiered alerts (CRITICAL/HIGH/MEDIUM/LOW), 22 behavioural features, SHAP explainability, MLflow tracking, and Streamlit investigator dashboard.

Free Financial Intelligence & Market Analysis Starter Pack for CORTEX — forensic accounting, market surveillance, insider trading detection, AML/KYC compliance

JOBGUARD-UK is an automated cyber-intelligence system that actively hunts fake job campaigns across recruitment sites and Telegram. Using a fine-tuned BERT model and network graph analysis, it identifies crypto-mule schemes and organized fraud rings, generating actionable intelligence to disrupt financial crime

Enterprise AML Transaction Monitoring System — 179M transactions, XGBoost AUC 0.9877, SHAP explainability, GenAI SAR generation, Streamlit dashboard, CI/CD

Modern AML investigator workspace — Claude-powered agent with MCP tools, defensible decision trail, and case management UI. Front-end of the Argus financial-crime detection platform.