Back to challenges
Challenge

Automating Finance Audits with Gemini & CrewAI

Develop an AI-powered auditing system for financial transactions using Gemini 2.5 Pro and CrewAI. The system should leverage Gemini's advanced reasoning capabilities to analyze financial data and identify anomalies. CrewAI will orchestrate a team of specialized agents, each responsible for a specific audit task (e.g., fraud detection, compliance checks, data reconciliation). The system should integrate with enterprise financial systems via MCP and incorporate extended thinking for complex case analysis.

Workflow AutomationHosted by Vera
Sign in free to start
Challenge brief

What you are building

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

Build a hierarchical agent system using Gemini 2.5 Pro and CrewAI for automated financial auditing.

Delivery guide

How work is evaluated

Evaluation

Evaluate the system's accuracy in detecting financial anomalies, its efficiency in processing large datasets, and its ability to handle complex cases.

Datasets

Shared data for this challenge

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

Loading datasets...
Learning goals

What you should walk away with

  • Develop an extended thinking module for complex case analysis.

  • Implement Gemini 2.5 Pro's advanced reasoning for anomaly detection.

  • Evaluate system performance using real-world financial datasets.

  • Design a CrewAI agent hierarchy for financial audit tasks.

  • Integrate MCP for secure access to enterprise financial systems.

Resources and assets

Reference links and supporting material

Dataset notes

Sample data for 1 tasks

How this agent runs

Evaluate the system's accuracy in detecting financial anomalies, its efficiency in processing large datasets, and its ability to handle complex cases.

Challenge input

CSV file of financial transactions

Agent execution

The configured agent processes the input under the challenge policy.

Evaluated output

JSON report with identified anomalies and explanations.

Checks for
  • Checks if the system correctly identifies known anomalies.
Proof of success
  • Precision target: 0.9
Runtime evidence
  • Python execution harness
View technical recipe

Configured tools

No tool records are attached.

Evaluation contract

  • The evaluation module defines the checks.

Recipe state

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

Frequently Asked Questions about Automating Finance Audits with Gemini & CrewAI