Account Aggregator Consent Analysis System using CrewAI and Claude 4.1 Opus
Navigating complex financial consent workflows and diversification strategies requires automated analysis of user permission data. Build a multi-agent financial research team using CrewAI and Claude 4.1 Opus to analyze Account Aggregator consent logs, profile portfolio risks, and recommend compliant wealth management pathways.
What you are building
The core problem, expected build, and operating context for this challenge.
Orchestrate a CrewAI multi-agent workflow leveraging Claude 4.1 Opus to process Account Aggregator financial telemetry and compute portfolio risk profiles.
How work is evaluated
Evaluates the CrewAI squad's accuracy in assessing portfolio diversification and verifying AA consent validity.
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.
consent_expiry_check
Ensures expired consent tokens are flagged immediately.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
allocation_math_accuracy
Accuracy of percentage breakdown calculation. • target: 1 • 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 collaborative CrewAI multi-agent crews with assigned roles, tasks, and tools.
Integrate Anthropic Claude 4.1 Opus for high-accuracy financial data analysis.
Model Account Aggregator consent artifact handling and privacy-preserving data extraction.
Reference links and supporting material
Account Aggregator consent schemas and asset balance feeds with simulated bank, mutual fund, and equity statements.
Challenge setup
Evaluates the CrewAI squad's accuracy in assessing portfolio diversification and verifying AA consent validity.
Challenge input
JSON object with AA consent payload, asset allocation breakdown, and risk tolerance.
CrewAI
Role-playing autonomous agent team structure
Claude 4.1 Opus
High capability model for intricate financial data extraction
Evaluated output
JSON object with consent_valid, equity_exposure_pct, debt_exposure_pct, and recommendation.
- Ensures expired consent tokens are flagged immediately.
- Accuracy of percentage breakdown calculation. • target: 1 • range: 0-1
- Benchmark: Fintech Risk Profile Suite
- Allocation Math Accuracy target: 1
- 1 public reference case
- Python execution harness
- Python 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
- CrewAI · Required
- crewAI · Optional
- Claude 4.1 Opus · Optional
Evaluation contract
- consent_expiry_check · Weight 1
- allocation_math_accuracy · Weight 1
Metrics
- allocation_math_accuracy · Target 1
Benchmarks
- Fintech Risk Profile Suite
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with AA consent payload, asset allocation breakdown, and risk tolerance.
Output: JSON object with consent_valid, equity_exposure_pct, debt_exposure_pct, and recommendation.
Execution harness: Python
Evaluation tools
- CrewAI · agent_framework
- Claude 4.1 Opus · models
Recipe freeze marker
No recipe freeze time is recorded.