This task focuses on developing an AI-powered fraud detection system to safeguard the integrity of academic records submitted to the Academic Bank of Credits (ABC) platform. As more institutions upload bulk student records digitally, the risk of fraudulent entries—such as duplicate certificates, invalid student identifiers, and tampered grades—rises significantly.

The goal is to leverage artificial intelligence to automatically scan, validate, and identify suspicious patterns, anomalies, and inconsistencies in academic datasets. These may include duplicate APAAR IDs, identical scores across different students, mismatches in issuance dates, or invalid document numbers. The system should flag potentially fraudulent entries and provide evidence-backed insights for verification teams to review.

AI models should be trained to detect known patterns of manipulation while also learning to identify new fraud signals using unsupervised or semi-supervised learning techniques.

• Design and implement an AI-based tool that:

  • Parses academic data (CSV, XML, or JSON formats).

  • Detects:

    • Duplicate student IDs, certificate numbers, or entries.

    • Tampering patterns (e.g., inconsistent formats, sudden GPA spikes).

    • Invalid or fake APAAR IDs or institution codes.

    • Document anomalies like overlapping issue dates or identical content.

  • Applies statistical and ML techniques for:

    • Outlier detection

    • Pattern recognition

    • Rule-based and AI-based anomaly classification

  • Provides a risk score and explanation for each flagged entry.

  • Generates a summary dashboard with fraud likelihood metrics and heatmaps.

• Optional: Integrate the module with the ABC dashboard or admin panel for real-time flagging and manual review interface.

• A robust AI-driven fraud detection system that:

  • Automatically flags suspicious academic record entries.

  • Highlights anomalies based on learned and configured rules.

  • Provides a fraud risk score and evidence for each flagged case.

  • Supports formats like CSV, XML, or JSON uploaded by institutions.

  • Generates summary reports for nodal authorities with filtering and sorting features.

  • Enhances trust in the ABC platform’s data pipeline by catching fraud before ingestion.

  • Includes a README with setup instructions, fraud scenarios covered, and sample outputs.

• Clone the Repository: Begin by cloning the project's official Git repository using the provided URL.
• Create a Branch: Use Git to create a new branch with a clear and relevant name for your task (e.g., ai-template-suggestion-tool).
• Implement the Solution: Complete the development work as per the task requirements. Ensure proper code documentation, commit messages, and testing.
• Push Your Changes: Push the code changes to your branch in the repository.
• Generate a Pull Request (PR): Submit your completed task by creating a pull request from your branch to the main/master branch of the repository.
• Include Documentation: Make sure your PR includes:
• A detailed README (if applicable)
• Usage instructions
• Sample input/output or test data
• Dependencies and setup steps
• Notify the Mentor/Reviewer: Once the PR is created, tag the assigned mentor or reviewer for review and approval.

https://openforge.gov.in/plugins/git/abc-collabwithgov/abc-fraud-detections.git

Organization Type: Government Publisher Name : Academic Bank of Credits (Digital India Corporation)