Anti-Scam Mule Account Pattern Detection Engine with Mastra AI
Mitigating public scam networks requires rapid ingestion and analysis of reported mule accounts and transaction flows. Construct an automated scam analysis workflow using Mastra AI in TypeScript to trace scam signals across transaction logs. Deliver rapid anomaly cluster tagging and automated emergency hold recommendations.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an anti-scam telemetry parsing and mule account detection engine using Mastra AI framework.
How work is evaluated
Evaluates detection accuracy for mule accounts and speed of triggering protective holds.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
freeze_recommendation_check
Validates that accounts with high mule probability trigger emergency freeze.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
mule_detection_f1
F1 score on classifying fraudulent mule accounts. • target: 0.96 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Build Mastra AI workflows for multi-step scam telemetry ingestion and processing
Implement built-in Mastra RAG tools to cross-reference known scam phone numbers and accounts
Design dynamic action graphs to trigger automated bank hold alerts
Deploy stateful Mastra agents with persistent session memory
Reference links and supporting material
Synthetic scam report feeds and corresponding mule account velocity logs.
How this agent runs
Evaluates detection accuracy for mule accounts and speed of triggering protective holds.
Challenge input
JSON report containing account activity
Mastra AI
Assigned framework for TypeScript workflow orchestration.
Evaluated output
JSON threat evaluation and action recommendation
- Validates that accounts with high mule probability trigger emergency freeze.
- F1 score on classifying fraudulent mule accounts. • target: 0.96 • range: 0-1
- Benchmark: AntiScam-Mule-Bench
- Mule Detection F1 target: 0.96
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
View technical recipe
Configured tools
- Mastra AI · Required
- Merge · Optional
- Helicone · Optional
- Mastra AI · Required
Evaluation contract
- freeze_recommendation_check · Weight 1
- mule_detection_f1 · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.