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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.

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What you are building

Construct an automated UPI fraud triage pipeline utilizing LangChain state graphs, graph-based risk scoring, and Deepchecks validation hooks.

How work is evaluated

Evaluation

Evaluates UPI fraud classification accuracy and Deepchecks drift suite integration.

Datasets

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

How submissions are scored

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

high_precision_check

Ensures fraud precision meets 95% threshold

Binary check

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

Dimension 2

precision_score

Ratio of true positive fraud alerts over total flagged transactions • 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

  • 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

Resources

Dataset notes

Synthetic dataset of 10,000 anonymized UPI transaction logs containing VPA identifiers, device fingerprints, transaction amounts, and IP locations.

Challenge setup

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

Evaluation checks
  • Ensures fraud precision meets 95% threshold
  • Ratio of true positive fraud alerts over total flagged transactions • target: 0.95 • range: 0-1
Configured evaluation evidence
  • Benchmark: Financial Fraud Agent Benchmark
  • Precision Score target: 0.95
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • deepchecks · Required
  • Langchain · Optional
  • Ellipsis · Optional
Orchestration
  • Langchain · Optional

Evaluation contract

  • high_precision_check · Weight 1
  • precision_score · Weight 1

Metrics

  • precision_score · Target 0.95

Benchmarks

  • Financial Fraud Agent Benchmark
  • security

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON array of UPI transaction payloads

    Output: JSON object with risk classifications and Deepchecks report

    Execution harness: Python

Evaluation tools

  • LangChain · Agent Framework
  • Deepchecks · Evaluation

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Build a LangChain UPI Fraud Triage Agent with Deepchecks Monitoring