Page 593 - AI for Good Innovate for Impact

AI for Good Innovate for Impact

2�3 Future Work

The future work for this AI-driven fraud detection use case in NGO operations involves several
key areas aimed at enhancing its effectiveness, scope, and real-world impact. The immediate
next steps involve data collection and proof of concept development, with an initial phase 4.6: Finance
focused on gathering relevant datasets from the identified sources (NGO databases, financial
transaction records, regulatory bodies, geospatial data providers, social media platforms,
news outlets) and building a functional prototype of the AI system. The proof of concept will
involve testing the core AI/ML models for anomaly detection, duplicate identification, and initial
compliance checks on a limited dataset to validate their feasibility and performance. Beyond
this initial phase, future work will concentrate on:
• Advanced Model Development and Refinement: This includes exploring more
sophisticated AI/ML techniques beyond initial anomaly detection, such as predictive
modelling to anticipate potential fraud, and developing more nuanced algorithms
for identifying complex patterns indicative of shell NGOs or sophisticated financial
manipulation. Continuous fine-tuning of existing models based on new data and feedback
from investigators will be crucial to maintain accuracy and adapt to evolving fraud tactics.
• Enhanced Data Integration and Interoperability: Expanding the system's ability to
seamlessly integrate with a wider range of data sources, including more granular financial
data, project implementation reports, and beneficiary databases, will provide a more
holistic view of NGO operations and improve detection accuracy. This may involve
developing robust APIs and data pipelines for efficient data exchange.
• Real-time Monitoring and Alerting Capabilities: Enhancing the system to provide more
sophisticated real-time monitoring and alerting mechanisms will be a priority. This could
involve developing customizable alert thresholds, integrating with existing notification
systems, and providing more contextual information with alerts to aid in faster analysis.
• Visualization and Reporting Tools: Developing user-friendly dashboards and reporting
tools will be essential for analysts and decision-makers to effectively interpret the
AIgenerated insights, visualize potential fraud networks, and generate comprehensive
reports for investigations and policy recommendations.
• Ground-Level Validation Enhancement: Improving the ground-level validation capabilities
by incorporating more diverse data sources beyond satellite imagery, such as drone
footage analysis, integration with on-the-ground reporting mechanisms, and potentially
even citizen-sourced data (with appropriate safeguards), could provide more robust
verification of project existence and impact.

Project Timeline

Timeline Milestone

Q2 2025 Data collection and PoC development using synthetic data.
Q3 2025 Pilot deployment in Maharashtra with 50 NGOs.

Q4 2025 Model refinement and integration with additional data sources (e.g., MCA,
ISRO).
2026 Nationwide rollout and dashboard development.

Sample Dashboard Design & Report Format

Proposed Dashboard Design:

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