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SIH26102SoftwareSmart Automation
₹1,50,000 Prize (₹75k Joint)
•
Deadline: 20 September 2026
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Submitted Ideas: 0/500

Development of an AI-powered system to detect anomalies, fraud, and inefficiencies in MPLAD Scheme implementation regd.

Ministry / Organization: MoSPI
Department: Data Informatics & Innovation Division (DIID)
SIH Official PortalDataset Repository
Technical Complexity
8/10
Dataset Accessibility
7/10
36h Hackathon Viability
9/10
Step 0 • Zero-Jargon Layman Summary

“This project involves building an AI watchdog for government development funds. It automatically scans project records to spot suspicious spending, delays, or fake work entries, helping officials ensure taxpayer money is actually used for its intended purpose.”

The DecodeX PS Decoder™

Deconstructed Problem Blueprint

Step 1

WHAT TO BUILD?

A centralized AI-powered analytics and monitoring dashboard that integrates financial, geographical, and project-completion data to flag irregularities.

Step 2

WHO IS IT FOR?

Primary: MoSPI officials and District Authorities. Secondary: Members of Parliament (MPs) and the general public (for transparency).

Step 3

WHY DOES IT EXIST?

Manual monitoring of thousands of small-scale projects across India is prone to human error, lack of real-time visibility, and potential for financial leakage or duplicate reporting.

Step 4

INPUT → PROCESS → OUTPUT ARCHITECTURE PIPELINE

01 • Inputs & Telemetry

MPLADS dashboard datasets (CSV/JSON/APIs), project sanction letters, expenditure reports, geo-tagged photos of assets, and satellite imagery (Bhuvan/ISRO).

02 • Processing & AI Core

Anomaly detection (Isolation Forests/Autoencoders), duplicate work detection (NLP/Fuzzy matching), predictive analytics for project delays, and risk scoring algorithms.

03 • Actionable Deliverables

Risk-based alert system, interactive heatmaps of fund utilization, automated compliance reports, and a decision-support dashboard for stakeholders.

Step 5

CONSTRAINTS

  • Data privacy and security of government financial records
  • Handling heterogeneous data formats from different districts
  • Interoperability with existing MoSPI legacy systems
  • Scalability to handle thousands of concurrent project entries
Step 6

JURY METRICS

  • Anomaly detection precision and recall
  • Reduction in manual audit time
  • Dashboard latency and UI/UX intuitiveness for non-technical officials
  • Accuracy of duplicate work detection
Step 7

CORE BOTTLENECK

Data quality and consistency; historical data is often fragmented, incomplete, or inconsistently formatted across different districts, making reliable training of ML models difficult.

Why You Should Choose This PS (Pros)

  • High social impact aligned with national digital governance goals
  • Clear, actionable deliverables that can be demonstrated via a dashboard
  • Strong potential for real-world deployment if the prototype is robust

Challenges & Risks to Watch (Cons)

  • Dependency on the quality of public datasets
  • Complexity in defining what constitutes 'fraud' without domain-specific rules
  • Risk of high false-positive rates in anomaly detection
Hackathon Blueprint

Recommended Tech Stack & Grand Finale Features

Frontend & Mobile
React.jsTailwind CSSRecharts for data visualization
Backend & APIs
FastAPIPythonCelery for background tasks
AI / ML & Models
Scikit-learn for anomaly detectionSentence-Transformers for duplicate project matchingXGBoost for risk scoring
Database & Cloud
PostgreSQLRedisSupabase

Killer Features to Impress the SIH Jury

Geo-fencing alerts: Automatically flag if a project's geo-tag is outside the sanctioned constituency
Predictive Delay Engine: Uses historical data to warn if a project is likely to miss its deadline
Explainable AI (XAI) Tooltips: Every alert shows exactly which data point triggered the suspicion
Public Transparency Portal: A simplified view for citizens to see progress in their area
View Official SIH 2026 Portal Raw Text
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•Background The Members of Parliament Local Area Development Scheme (MPLADS) is a Central Sector Scheme under which Hon'ble Members of Parliament recommend developmental works for creation of durable community assets and provision of basic civic amenities. The Scheme involves large-scale fund utilization and execution of thousands of works across the country through multiple implementing agencies and administrative authorities. Given the volume and complexity of financial and project-related data generated under the Scheme, there is a need for an AI-powered solution that can leverage machine learning and advanced analytics to detect trends and anomalies in expenditure patterns, fund utilization, cost estimates, and work execution, thereby enabling early identification of potential fraud, inefficiencies, and non-compliance while enhancing transparency, accountability, and effective monitoring of MPLADS works.
•Description Develop an AI-powered monitoring and analytics platform for MPLADS that leverages Machine Learning (ML), Artificial Intelligence (AI), and advanced data analytics to identify trend, anomalies, irregularities, and potential fraud in fund utilization and project execution. The solution should analyze data relating to sanctions,expenditures, cost estimates, work progress, payments, and asset creation to detect unusual patterns, cost overruns, duplicate works, delayed projects, and deviations from established norms. The system should generate risk-based alerts, predictive insights, and decision-support dashboards for Members of Parliament, State Nodal Authorities, District Authorities, and the Ministry. The platform should also facilitate automated compliance monitoring, trend analysis, and early warning mechanisms to improve transparency, accountability, and efficiency in the implementation of MPLADS works across the country.
•Expected Solution The proposed solution should be an AI-powered platform that helps monitor MPLADS works and fund utilization in a smarter and more efficient manner. By analyzing data related to project approvals, expenditures, payments, work progress, and completion status, the system should be able to identify unusual patterns, delays, cost overruns, duplicate works, and potential cases of misuse of funds. It should automatically generate alerts and highlight high-risk cases that require attention from the concerned authorities. The platform should provide easy-to-understand dashboards and insights to Members of Parliament, State Nodal Authorities, District Authorities, and the Ministry, enabling them to make informed decisions and take timely corrective action. By leveraging artificial intelligence and data analytics, the solution should enhance transparency, strengthen accountability, reduce manual monitoring efforts, and support more effective implementation of MPLADS works across the country.