Fraud Detection
Detect label misuse and track anomalies and coordinated account fraud at scale with this reasoning-capable GenAI solution.
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Executive summary
Detecting label misuse and coordinated account fraud requires real-time reasoning across distributed shipping data. Our Fraud Detection Agent helps agencies and logistics providers identify reused, spoofed, or misrouted labels while tracing anomalies across accounts, facilities, and routes. Built on SeekrFlow’s agentic architecture and Edge-Compatible Data Engine, it retrieves and analyzes scan logs, label files, and route metadata to deliver explainable, grounded insights. The result is faster, more transparent fraud detection that reduces audit overhead and strengthens the integrity of global networks.
Problem
Every industry that relies on labels, tags, or tracking IDs faces serious fraud risks. These identifiers tie into billing, security, and compliance workflows—and misuse is growing. Common tactics include:
Reuse of valid labels across unauthorized accounts
Misuse of programs like Global Direct Entry (GDE)
Inconsistent scan patterns across facilities
Fake return addresses to bypass scrutiny
Much of this data lives at the edge, scattered across scan logs, label files, and route metadata. Because of this, centralized detection is difficult and often reactive.
The impact:
Revenue loss and chargeback risk
Higher cost of manual audits and investigations
Undetected fraud across large, distributed operations
Solution
Our pre-built Fraud Detection Assistant helps organizations detect, investigate, and explain misuse across labeling and tracking systems.
It empowers fraud and compliance teams to:
Detect reused, spoofed, or misrouted labels
Identify mismatches between sender, account, and return data
Trace anomalous activity across time, geography, and user profiles
Correlate systemic patterns for deeper investigations
Generate explainable, auditable summaries for decision-makers
Value
Detects emerging fraud tactics before they escalate
Delivers explainable, auditable results
Reduces manual audit overhead
Enables proactive prevention at the edge
Adapts to evolving industry regulations and fraud strategies
How it works
The solution runs on a reasoning-capable agentic architecture, powered by SeekrFlow. It ingests structured scan data (markdown files, logs, manifests), retrieves relevant patterns, supplements with external context, and generates grounded insights using language models.
Planner Agent
Understands fraud-focused questions like: “Which accounts had reused tracking numbers scanned at multiple origins?”
FileSearch Tool
Mines markdown logs from edge systems for label, scan, and account data
WebSearch Tool
Augments investigations with insights about known global fraud methods
LLM-RAG Tool
Provides natural language explanations grounded in file data, not guesses
Thread-Aware Memory
Enables analysts to follow up, revise queries, and trace prior evidence
Key capabilities
Accepts prompts like: “Which accounts shared label IDs across different facilities?”
Retrieves and analyzes label, scan, and routing data from files
Enriches context with public policy references and online fraud indicators
Explains suspicious activity in natural language, with traceable logic
Supports follow-ups and iterative analysis across investigations
Ideal users
Postal Services
Validate label use and prevent billing fraud and misrouted packages
Customs & Border Agencies
Detect illicit use of entry/return programs
Global Logistics Providers
Detect internal and third-party label anomalies
E-Commerce Platforms & Retailers
Identify account abuse and return fraud
National Security Organizations
Track patterns tied to illicit networks
Built on SeekrFlow
Edge-Compatible Data Engine
Parses scan logs, label files, and routing data from decentralized systems
FileSearch
Searches markdown-based scan events and historic fraud patterns
WebSearch
Retrieves policy references and external fraud indicators (e.g. GDE misuse)
LLM-RAG
Generates grounded, natural language explanations for suspicious label behavior
Agent Framework
Planner and Evaluator agents coordinate multi-step fraud investigations
Session Memory
Maintains context across analyst queries and follow-ups
UI or SDK Access
Supports visual analysis or programmatic workflows
Where you can extend it
Integrate with billing systems to block fraudulent charges
Train on historical fraud cases for improved precision
Add dashboards for visual scan route anomaly detection
Expand data inputs to include images, customs forms, or metadata
Refine reasoning models with internal audit policies
Takeaway
Seekr’s Fraud Detection solution transforms fragmented data into actionable intelligence. It gives organizations across industries a scalable, explainable, and adaptable way to detect and investigate label misuse — not after the fact, but as it unfolds.
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See it in action
See how this AI solution works for your team. Request a live walkthrough with one of our experts and explore how it can adapt to your unique workflows and data.
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