Data investigation and fraud detection analytics
CybersecurityOSINTData Science

Private Investigation Consortium

OSINT-Powered Fraud Detection & Financial Crime Investigation

Private Investigation / Fraud Detection

|8 months

60%

Investigation Time Reduced

$8.5M

Assets Recovered

92%

Detection Accuracy

The Challenge

A consortium of private investigators in Uruguay faced significant challenges in detecting sophisticated financial fraud schemes that operated across multiple jurisdictions and digital platforms. Traditional investigation methods were slow and resource-intensive. They needed to rapidly connect disparate data points from public sources to identify fraud networks and track illicit financial flows.

Our Approach

We built a comprehensive OSINT platform that aggregated data from multiple internet sources including business registries, social media, domain registration databases, and financial records. Implemented advanced graph analysis to identify connections between individuals, companies, and financial transactions. Created automated alerts for suspicious patterns and network structures. Trained the investigation team on advanced OSINT techniques and tool usage.

Key Deliverables

OSINT aggregation platform with multi-source data integration
Graph database for relationship analysis (Neo4j)
Automated fraud pattern detection algorithms
Real-time alerting system for suspicious activities
Investigator dashboard with visualization tools
OSINT toolkit training and documentation
Case management and evidence tracking system

Tech Stack

PythonNeo4jMaltegoPostgreSQLElasticsearchReactNode.jsDocker

Impact

The OSINT platform — built entirely on open-source tools (Neo4j, Python, Elasticsearch) with no proprietary black boxes and full audit trails — enabled investigators to reduce fraud investigation time by 60%, from weeks to days. Successfully identified and documented 12 major fraud networks operating across Uruguay and neighboring countries. Recovered $8.5M in fraudulent assets through evidence provided by platform-based investigations. The system achieved 92% accuracy in identifying fraudulent entities compared to traditional methods.

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