Proof pending. Core topic summary fields are still materializing.
The proliferation of fake reviews, often produced by organized groups, undermines consumer trust and fair competition on online platforms. These groups employ sophisticated strategies that evade tradi...
In recent years, Large Language Models (LLMs) have shown great capability in processing graph tasks such as fraud detection. However, most existing methods rely heavily on rich text attributes, which ...
The proposed method (FraudFox) provides solutions to adversarial attacks in a resource constrained environment. We focus on questions like the following: How suspicious is `Smith', trying to buy \$500...
Money launderers take advantage of limitations in existing detection approaches by hiding their financial footprints in a deceitful manner. They manage this by replicating transaction patterns that th...
Fraud detection models in payment networks train on chargeback labels that are systematically biased. Every label must survive three sequential gates: authorization (declined transactions generate no ...
Freshness
Canonical route: /topics
Agent Handoff
Canonical ID fraud-detection | Route /topic/fraud-detection
REST example
curl https://sciencetostartup.com/api/v1/agent-handoff/topic/fraud-detectionMCP example
{
"tool": "search_papers",
"arguments": {
"query": "Fraud Detection",
"cluster": "Fraud Detection"
}
}source_context
{
"surface": "topic",
"mode": "topic",
"query": "Fraud Detection",
"normalized_query": "fraud-detection",
"route": "/topic/fraud-detection",
"paper_ref": null,
"topic_slug": "fraud-detection",
"benchmark_ref": null,
"dataset_ref": null
}Use This Via API or MCP
Topic pages bundle paper counts, viability trends, author concentration, and top questions into one canonical surface your agents can reference before they open Signal Canvas or create a workspace.