Build a Capacity-Constrained Fraud Review Queue Simulator
Allocate a limited investigator budget across synthetic alerts. Compare expected preventable loss, review effort, customer friction, and uncertainty without executing account restrictions.
What you are building
An alert has a 20% loss probability, potential loss of 1,000, and assumed intervention effectiveness of 50%. Review costs 20 and customer friction costs 10. The modeled net value is 70. Build a queue simulator that compares alerts under a fixed review budget. Report the assumptions and simulate actions without restricting accounts.
Rules
All queue actions are simulated. Expected loss reduction is not a proven causal benefit. No fabricated records, source citations, professional endorsements, or expert-adjudication claims. No future-data leakage into dated decisions, model fitting, or historical valuation inputs. No unauthorized payments, account restrictions, regulatory filings, or production changes. No automated appraisal certification, tax-compliance determination, or unsupported fraud accusation. No concealed uncertainty or silently invented inputs required for a numerical conclusion.
How work is evaluated
100-point weighted review with safety gates. Visible starter cases do not establish full application correctness.
A reproducible queue optimization service. A planning report comparing a simple baseline with the optimized proposal. Runnable local application, setup commands, and a dependency lockfile. Versioned input data, data dictionary, acquisition instructions, and rights notes. Calculation traces, source references, assumption records, and exported review artifacts. A deterministic baseline and tests for normal, incomplete, contradictory, and failure-recovery cases. A recorded complete workflow with a reproducible manifest and explicit limitations.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
Learning goals
Validate risk probabilities and operational cost inputs.
Calculate expected net review benefit under explicit assumptions.
Optimize the review selection under capacity constraints.
Export queue proposals and sensitivity to costs and calibration.
Resources
2 attached datasets, including two visible synthetic starter cases. The fixture ZIP supplies synthetic exercise inputs. It does not supply independently verified real-world records.
Challenge setup
Challenge input
Input details are not configured.
Agent execution
No agent tool is listed for this challenge.
Evaluated output
No output checks are configured.
- Rubric dimension 1
- Rubric dimension 2
- Rubric dimension 3
- 2 public reference cases
- No runtime settings are configured.
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
No tool records are attached.
Evaluation contract
- Rubric dimension 1 · Weight 30
- Rubric dimension 2 · Weight 25
- Rubric dimension 3 · Weight 20
- Rubric dimension 4 · Weight 15
- Rubric dimension 5 · Weight 10
Reference cases
- 2 public reference cases
Workflow tasks
No workflow tasks are configured.
Recipe freeze marker
No recipe freeze time is recorded.