SIH26102: Development of an AI-powered system to detect anomalies, fraud, and inefficienci — SIH 2026 | Wynit

SIH26102: Development of an AI-powered system to detect anomalies, fraud, and inefficienci — SIH 2026 | Wynit

What they'll ask you

12

Generated from the brief's own wording and the competition's published rules, never from a guess about what this ministry prefers.

The exact schema and format of the MPLADS dataset provided by the organisers?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Specific machine learning models or algorithms required?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
Threshold definitions for what constitutes a high-risk alert?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.

9 morethe ones a panel gets to if there is time

Has any part of this been shown at a previous event, hackathon or college project?
The guidelines are explicit: your solution must not have appeared in any previous event or programme, of any sort. A recycled project is what a team under time pressure reaches for.
The brief asks for data ingestion pipeline. How would you build that?
Decoded from SIH26102 itself: Collects and processes records related to sanctions, expenditures, cost estimates, and work progress. The brief asks for it by name.
The brief asks for anomaly detection engine. How would you build that?
Decoded from SIH26102 itself: Applies machine learning to identify unusual spending patterns, cost overruns, and duplicate works. The brief asks for it by name.
The brief asks for automated compliance monitor. How would you build that?
Decoded from SIH26102 itself: Tracks project timelines and flags deviations from established scheme norms. The brief asks for it by name.
The brief asks for risk alert system. How would you build that?
Decoded from SIH26102 itself: Generates automated warnings for high-risk cases that require intervention. The brief asks for it by name.
The brief asks for role-based dashboards. How would you build that?
Decoded from SIH26102 itself: Provides decision-support insights and visual reports for MPs, state authorities, and the ministry. The brief asks for it by name.
Why not use what already exists? Name the closest thing to this that is already running.
A team that has not named the alternative themselves is answering this for the first time in the room.
Which single thing will you demonstrate end to end, start to finish, with nothing skipped?
Ours, not a rule: a narrow thing that fully works survives questioning better than a broad thing that half works. If nobody on the team can name it, that is the finding.
Show me this working: the jury will check whether your system can successfully ingest financial and project data, accurately flag unusual patterns like cost overruns or duplicate works, and display clear dashboards for different administrative roles.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (381 words)

• 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.