Third-Party Litigation Funding Risk Agent with CrewAI
Legal developments in litigation funding impact insurer loss reserves and settlement strategies. Build a multi-agent crew using CrewAI to orchestrate legal research, docket analysis, and funder tracking across casualty claims portfolios. Achieve an accuracy of 90% in detecting third-party litigation funding involvement in claims.
What you are building
The core problem, expected build, and operating context for this challenge.
Build a multi-agent collaborative crew using CrewAI to analyze court filings and detect Third-Party Litigation Funding (TPLF) signatures.
How work is evaluated
Evaluates TPLF detection accuracy and reserve adjustment calculations.
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.
tplf_boolean_check
tplf_present must be a boolean value.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
accuracy
Accuracy of TPLF detection across test cases • target: 0.9 • range: 0.75-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Orchestrate multi-agent roles using CrewAI (Researcher, Legal Analyst, Reserve Risk Manager)
Build sequential and hierarchical crew task workflows
Detect Third-Party Litigation Funding (TPLF) indicators in legal dockets
Achieve 90% classification accuracy on TPLF involvement flags
Reference links and supporting material
Dataset of 600 civil litigation docket summaries from state and federal courts.
How this agent runs
Evaluates TPLF detection accuracy and reserve adjustment calculations.
Challenge input
JSON containing docket_text, law_firm_name, and motion_history
CrewAI
Enables role-playing agent delegation for multi-stage legal docket evaluation.
Evaluated output
JSON with tplf_present (boolean), funding_entity (string or null), and reserve_increase_pct (float)
- tplf_present must be a boolean value.
- Accuracy of TPLF detection across test cases • target: 0.9 • range: 0.75-1
- Benchmark: TPLFDetectionBench
- Accuracy target: 0.9
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- CrewAI · Required
- crewAI · Optional
- BentoML · Optional
Evaluation contract
- tplf_boolean_check · Weight 1
- accuracy · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.