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Challenge

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.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Build an automated UPI transaction risk triage agent using LangChain graph workflows and Relicx testing harness.

Delivery guide

How work is evaluated

Evaluation

Evaluates transaction risk classification precision and response latency.

Datasets

Challenge datasets

Public datasets, plus the private datasets you have access to.

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

valid_output_structure

Ensures risk output contains correct keys.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

accuracy

Fraud detection precision score • target: 0.95 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

What you should walk away with

  • 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 and assets

Reference links and supporting material

Dataset notes

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

Evaluation checks
  • Ensures risk output contains correct keys.
  • Fraud detection precision score • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: UPI-Risk-Bench
  • Accuracy target: 0.95
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • Relicx · Required
  • Langchain · Optional
  • Hamming · Optional
Orchestration
  • 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

  1. 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.

Frequently Asked Questions about LangChain Real-Time UPI Fraud Triage System with Relicx Automation