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Challenge

AI SDK & SambaNova Cloud UPI Fraud Triage Stream

High-volume digital brokers and fintech apps need ultra-low latency transaction fraud classification on high-throughput payment rails. You will construct a streaming agent that processes real-time device telemetry and transaction logs to flag anomalous money transfers with sub-50ms latency.

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Challenge brief

What you are building

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

Build a Vercel AI SDK real-time agent powered by SambaNova Cloud fast inference to evaluate UPI transaction streams against device risk graphs.

Delivery guide

How work is evaluated

Evaluation

Evaluates streaming fraud triage latency and classification accuracy on UPI transaction payloads.

Datasets

Shared data for this challenge

Review public datasets and any private uploads tied to your build.

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

Latency Threshold Test

Checks that the processing time remains below 50ms

Binary check

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

Dimension 2

Fraud Detection Precision

Precision of identifying fraudulent transfers • target: 0.97 • range: 0.92-1

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

  • Construct streaming agent pipelines using Vercel AI SDK streamText and tool calling

  • Integrate SambaNova Cloud ultra-fast inference API endpoints for sub-50ms response

  • Process real-time UPI payment signals, device fingerprints, and geolocation velocity

  • Implement continuous risk evaluation loops for dynamic payment blocking

Resources and assets

Reference links and supporting material

Dataset notes

Real-time simulated UPI transaction stream with embedded anomaly patterns such as velocity spikes and novel device logins.

How this agent runs

Evaluates streaming fraud triage latency and classification accuracy on UPI transaction payloads.

Challenge input

JSON transaction payload with sender, receiver, amount, and velocity score

AI SDK

Provides fast streaming tools for TypeScript/JS environments.

SambaNova Cloud

Delivers ultra-fast LLM inference for low latency requirements.

Evaluated output

JSON with action (ALLOW|BLOCK|FLAG), risk_score, and latency_ms

Checks for
  • Checks that the processing time remains below 50ms
  • Precision of identifying fraudulent transfers • target: 0.97 • range: 0.92-1
Proof of success
  • Benchmark: PaymentRisk-Bench
  • Fraud Detection Precision target: 0.97
  • 1 public reference case
Runtime evidence
  • JavaScript execution harness
  • javascript sandbox (unavailable on Versalist)
View technical recipe

Configured tools

Action Space
  • SambaNova Cloud · Required
  • RAI · Optional
  • Synthflow · Optional

Evaluation contract

  • Latency Threshold Test · Weight 1
  • Fraud Detection Precision · Weight 1

Recipe state

This is a preview. The configuration can change before the evaluation recipe is locked.

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