Build a LangChain UPI Fraud Triage Agent with Deepchecks Monitoring
High-volume UPI payment networks face complex fraud patterns across merchant nodes and mobile devices. Build a LangChain-powered fraud triage agent that ingests transaction logs and evaluates device risk signals to achieve 95% fraud classification precision. Validate pipeline reliability using Deepchecks.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct an automated UPI fraud triage pipeline utilizing LangChain state graphs, graph-based risk scoring, and Deepchecks validation hooks.
How work is evaluated
Evaluates UPI fraud classification accuracy and Deepchecks drift suite integration.
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.
high_precision_check
Ensures fraud precision meets 95% threshold
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
precision_score
Ratio of true positive fraud alerts over total flagged transactions • target: 0.95 • 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 stateful LangChain agents for real-time UPI transaction risk scoring
Integrate Deepchecks for continuous model monitoring and data drift detection
Implement device fingerprinting and velocity-based fraud alert rules
Deploy fallback mechanisms for high-throughput transaction evaluation
Reference links and supporting material
Synthetic dataset of 10,000 anonymized UPI transaction logs containing VPA identifiers, device fingerprints, transaction amounts, and IP locations.
How this agent runs
Evaluates UPI fraud classification accuracy and Deepchecks drift suite integration.
Challenge input
JSON array of UPI transaction payloads
LangChain
Stateful workflow orchestration for payment routing
Deepchecks
Data validation and continuous pipeline checks
Evaluated output
JSON object with risk classifications and Deepchecks report
- Ensures fraud precision meets 95% threshold
- Ratio of true positive fraud alerts over total flagged transactions • target: 0.95 • range: 0-1
- Benchmark: Financial Fraud Agent Benchmark
- Precision Score target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox
View technical recipe
Configured tools
- deepchecks · Required
- Langchain · Optional
- Ellipsis · Optional
- Langchain · Optional
Evaluation contract
- high_precision_check · Weight 1
- precision_score · Weight 1
Recipe state
This is a preview. The configuration can change before the evaluation recipe is locked.