Build an Autonomous Digital Banking Onboarding Agent with Mastra AI and o3
Digital banks require real-time KYC verification and anti-fraud checks to safely onboard remote customers without adding friction. Build a Mastra AI workflow leveraging OpenAI o3 to perform cross-document entity verification and identity fraud detection, achieving under 2% false rejection rate.
What you are building
The core problem, expected build, and operating context for this challenge.
Implement an automated onboarding pipeline using Mastra AI workflows and o3 reasoning models to cross-verify document data against watchlist databases.
How work is evaluated
Evaluates the accuracy and speed of identity verification across synthetic KYC submissions containing fraud traps.
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.
Synthetic Fraud Trap Rejection
Ensures mismatched identity records are properly rejected.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
False Rejection Rate
Percentage of valid users wrongly rejected. • target: 0.01 • range: 0-0.02
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Design a stateful KYC onboarding workflow in Mastra AI with deterministic fallback steps.
Integrate OpenAI o3 reasoning capabilities for fine-grained identity document discrepancy analysis.
Implement real-time watchlist screening against sanctions datasets with entity resolution.
Maintain compliance traceability by outputting audit-ready decision logs.
Reference links and supporting material
Synthetic database of Egyptian and international identity documents with embedded fraud patterns.
Challenge setup
Evaluates the accuracy and speed of identity verification across synthetic KYC submissions containing fraud traps.
Challenge input
JSON containing applicant profile, passport text, and proof of address string
Mastra AI
Orchestrates step-by-step KYC state flow in TypeScript.
OpenAI o3
Provides structured logical extraction over ambiguous text.
Evaluated output
JSON with verification status, risk_score (0.0 - 1.0), and reasoning trace
- Ensures mismatched identity records are properly rejected.
- Percentage of valid users wrongly rejected. • target: 0.01 • range: 0-0.02
- Benchmark: FintechKYCBench
- False Rejection Rate target: 1%
- 1 public reference case
- TypeScript 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
- OpenAI · Optional
- o3 · Optional
- Mastra AI · Required
Evaluation contract
- Synthetic Fraud Trap Rejection · Weight 1
- False Rejection Rate · Weight 1
Metrics
- False Rejection Rate · Target 0.01
Benchmarks
- FintechKYCBench
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON containing applicant profile, passport text, and proof of address string
Output: JSON with verification status, risk_score (0.0 - 1.0), and reasoning trace
Execution harness: TypeScript
Evaluation tools
- Mastra AI · Agent Framework
- OpenAI o3 · Reasoning Model
Recipe freeze marker
No recipe freeze time is recorded.