Public evaluation

Fraud Detection Accuracy

The system is evaluated based on its ability to correctly identify fraud in a synthetic UPI dataset and the latency of its multimodal analysis.

Evaluation type

task based

Challenge

Scalable UPI Merchant Fraud Triage Agent using Google ADK and Galileo

Difficulty

Advanced

Rigor

Unspecified

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Evaluation overview

How the linked challenge is judged: tasks, benchmarks, and criteria count.

Tasks

Benchmarks

Criteria

Task templates

Inputs and expected outputs.

Task 1

Fraud Detection Accuracy

Evaluate the agent's ability to flag suspicious merchant accounts based on transaction spikes and inconsistent KYC photos.

Input format

JSON containing merchant_id, transaction_history (list), and kyc_image_url

Output format

JSON containing risk_score (0-1), reasoning_string, and recommended_action

Linked challenge

Scalable UPI Merchant Fraud Triage Agent using Google ADK and Galileo

With UPI transactions hitting 23.7 billion monthly, the volume of potential merchant-side fraud has scaled exponentially. This challenge requires building an automated fraud triage agent that processes high-velocity transaction logs and merchant metadata to identify high-risk patterns. You will leverage the Google Agent Development Kit (ADK) to build a multimodal agent that uses Gemini 1.5 Pro to analyze both structured transaction data and unstructured merchant verification documents (KYC images, storefront photos). The agent must utilize Galileo's monitoring suite to implement real-time guardrails, ensuring that automated fraud flags meet high precision standards before triggering account freezes. Your solution will need to handle India-specific context, such as diverse merchant categories and regional transaction behaviors, while maintaining the performance required for national-scale Digital Public Infrastructure.

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