Dynamic Sanctions Compliance Screening Pipeline with Mastra AI & Lovable
Sanctions list updates following foreign policy shifts require instantaneous realignment of cross-border payment screening rules. Build a real-time transaction screening pipeline using Mastra AI and Lovable to dynamically parse updated sanctions lists, evaluate transaction payload context, and flag restricted entities with 99% recall and zero false negatives on sanctioned entities.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an adaptive cross-border payment screening pipeline using Mastra AI framework workflows combined with Lovable UI prototyping.
How work is evaluated
Evaluates dynamic sanctions list parser and transaction blocker recall rate.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
zero_false_negatives
Verifies that no sanctioned entity transaction passed through unblocked.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
sanctions_recall
Recall metric for detecting restricted entities. • target: 0.99 • 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
Construct Mastra AI workflows for processing dynamic regulatory update notifications
Utilize Lovable to generate full-stack compliance management interfaces
Implement fuzzy name matching and entity resolution for dynamic SDN sanctions lists
Guarantee zero false negatives on confirmed sanctioned entities across multi-currency flows
Reference links and supporting material
Synthetic dataset of 500 cross-border transfers combined with OFAC SDN list deltas representing recent sanctions policy adjustments.
Challenge setup
Evaluates dynamic sanctions list parser and transaction blocker recall rate.
Challenge input
JSON containing sender details, beneficiary details, routing bank, and updated sanctions list
Mastra AI
TypeScript agent workflow engine with integrated memory.
Lovable
AI engineering tool for rapidly scaffolding frontends and interface layers.
Evaluated output
JSON screening status object containing blocked flag, match confidence, and match rule
- Verifies that no sanctioned entity transaction passed through unblocked.
- Recall metric for detecting restricted entities. • target: 0.99 • range: 0-1
- Benchmark: SanctionsGuard-Bench-v1
- Sanctions Recall target: 0.99
- 1 public reference case
- JavaScript execution harness
- javascript sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- Mastra AI · Required
- Lovable · Optional
- Mastra AI · Required
Evaluation contract
- zero_false_negatives · Weight 1
- sanctions_recall · Weight 1
Metrics
- sanctions_recall · Target 0.99
Benchmarks
- SanctionsGuard-Bench-v1
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing sender details, beneficiary details, routing bank, and updated sanctions list
Output: JSON screening status object containing blocked flag, match confidence, and match rule
Execution harness: JavaScript
Evaluation tools
- Mastra AI · Agent Workflow Framework
- Lovable · AI Tooling / Application Generator
Recipe freeze marker
No recipe freeze time is recorded.