Fraud Detection Software Resources

Unit21 streamlines the investigation process by bringing alerts, transactions, user activity, and entity relationships into a single investigation workspace. Instead of switching between multiple systems, analysts can review alerts, explore linked accounts, and evaluate suspicious behavior within one unified platform. The system provides entity-level timelines, transaction histories, and network visualizations that help investigators quickly understand how activity is connected. This context makes it easier to identify coordinated fraud or money laundering patterns that may otherwise be missed. AI agents further support analysts by organizing investigation data, highlighting relevant signals, and assisting with case summaries. Rather than replacing analysts, these agents help reduce manual work and surface the information investigators need to make faster, more confident decisions.

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Unit21 can ingest and analyze a wide range of structured and behavioral data to power fraud detection and financial crime investigations. The platform supports transactional data, user activity signals, device intelligence, and identity information to provide a comprehensive view of risk across accounts and entities. Data ingestion is flexible and can be configured through APIs, flat file uploads, or integrations with existing banking infrastructure. This allows organizations to connect their systems quickly while maintaining full control over how data is mapped and monitored. Common data sources include: • Transactional data such as ACH transfers, wires, card transactions, and crypto transfers • User behavioral signals such as logins, password changes, and account updates • Device and IP data used for anomaly detection • KYC and onboarding information • Event and session activity data • Third-party enrichment sources such as Chainalysis, watchlists, or fraud consortium feeds This data foundation enables Unit21’s AI agents and detection models to identify suspicious activity earlier and provide investigators with richer context during case analysis.

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