LangChain Real-Time UPI Fraud Triage System with Relicx Automation
High-volume UPI merchant ecosystem transactions face complex synthetic identity and velocity fraud. Build a real-time event-driven transaction monitoring pipeline using LangChain, Relicx, and graph risk analysis to achieve sub-100ms threat scoring and compliance logging.
What you are building
Build an automated UPI transaction risk triage agent using LangChain graph workflows and Relicx testing harness.
How work is evaluated
Evaluates transaction risk classification precision and response latency.
Datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
valid_output_structure
Ensures risk output contains correct keys.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
accuracy
Fraud detection precision score • target: 0.95 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Learning goals
Implement LangChain runnable chains with Graph risk context
Automate end-to-end transaction validation using Relicx
Process high-throughput UPI payment webhooks with latency constraints
Resources
Synthetic UPI transaction logs with tagged fraud indicators and device fingerprints.
Challenge setup
Evaluates transaction risk classification precision and response latency.
Challenge input
JSON transaction payload
LangChain
Mandatory framework requirement
Relicx
Assigned evaluation tool
Evaluated output
JSON risk assessment output
- Ensures risk output contains correct keys.
- Fraud detection precision score • target: 0.95 • range: 0-1
- Benchmark: UPI-Risk-Bench
- Accuracy target: 0.95
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Shows the first task, up to three tools, and up to three evidence items.
View technical recipe
Configured tools
- Relicx · Required
- Langchain · Optional
- Hamming · Optional
- Langchain · Optional
Evaluation contract
- valid_output_structure · Weight 1
- accuracy · Weight 1
Metrics
- accuracy · Target 0.95
Benchmarks
- UPI-Risk-Bench
- security
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON transaction payload
Output: JSON risk assessment output
Execution harness: Python
Evaluation tools
- LangChain · Agent Orchestration Framework
- Relicx · Automated Testing Tool
Recipe freeze marker
No recipe freeze time is recorded.