Automate Synthetic Identity Fraud Triage with AutoGen Multi-Agent Systems
Synthetic identity fraud combines real and fabricated PII to bypass traditional credit checks. Construct an AutoGen multi-agent system that analyzes social graph linkages and bureau data to isolate synthetic identity networks with 95% detection accuracy.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an AutoGen multi-agent team to cross-examine identity signals and social graphs for synthetic identity fraud detection.
How work is evaluated
Evaluates synthetic identity detection accuracy across clusters of fabricated credit profiles.
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.
Sleeper Identity Detection Test
Fabricated identities with high shared address usage must flag is_synthetic == True.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Synthetic Fraud Recall
Ratio of correctly isolated synthetic identity profiles. • target: 0.95 • range: 0.9-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Configure AutoGen agents representing PII Verifier, Graph Link Analyst, and Fraud Supervisor.
Perform cross-entity resolution over shared SSNs, phone numbers, and delivery addresses.
Detect identity fabrication patterns such as sleeper profiles and credit-padders.
Generate prioritized investigator queues with graphical evidence trees.
Reference links and supporting material
Synthetic credit applicant dataset containing embedded Frankenstein identity networks.
How this agent runs
Evaluates synthetic identity detection accuracy across clusters of fabricated credit profiles.
Challenge input
JSON containing ssn, name, address, shared_phone_count, account_age_days
AutoGen
Orchestrates multi-perspective agent conversations for identity verification.
Evaluated output
JSON with is_synthetic (boolean), synthetic_score (0-100), evidence_summary
- Fabricated identities with high shared address usage must flag is_synthetic == True.
- Ratio of correctly isolated synthetic identity profiles. • target: 0.95 • range: 0.9-1
- Benchmark: SyntheticIdentityBench
- Synthetic Fraud Recall target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- AutoGen · Required
- Microsoft AutoGen · Optional
Evaluation contract
- Sleeper Identity Detection Test · Weight 1
- Synthetic Fraud Recall · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.