SIH26184: Development of a Predictive Analytics Framework for Cybercrime Complaints to For — SIH 2026 | Wynit

SIH26184: Development of a Predictive Analytics Framework for Cybercrime Complaints to For — SIH 2026 | Wynit

What they'll ask you

11

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

What historical dataset format or schema will be provided?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
What specific machine learning models or accuracy thresholds are expected?
The brief never answers this, so a panel will. Whatever you decide, say it the same way twice.
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.

8 morethe ones a panel gets to if there is time

The brief asks for predictive analytics engine. How would you build that?
Decoded from SIH26184 itself: An AI or machine learning system that analyzes historical data to predict potential cash withdrawal hotspots. The brief asks for it by name.
The brief asks for risk heatmap dashboard. How would you build that?
Decoded from SIH26184 itself: A GIS-enabled dashboard that visualizes real-time and potential risk zones with filters. The brief asks for it by name.
The brief asks for law enforcement interface. How would you build that?
Decoded from SIH26184 itself: A secure interface for investigators to access alerts, intelligence reports, and evidence documentation. The brief asks for it by name.
The brief asks for alert and notification system. How would you build that?
Decoded from SIH26184 itself: A system that sends real-time notifications to law enforcement, banks, and officers via SMS, email, API, or dashboard triggers. The brief asks for it by name.
Where does your data come from: a published source, one you collect, or one you generate?
No dataset is attached to this problem statement, so sourcing it is part of the work and nobody told you that.
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 evaluators will check if your predictive analytics engine can forecast cash withdrawal locations, if your dashboard includes GIS risk mapping, and if the alert system sends real-time notifications to law enforcement and banks.
This is the evaluator read for your problem statement, decoded from the brief's own wording.

The government's original description (344 words)

• Background The National Cybercrime Reporting Portal is the centralized Portal, which is serving the whole country. Currently, the Portal facilitates citizens in filing complaints, LEAs act on complaints, Banking/Financial Institutions for their actions along with reports/graphs being pulled on daily basis. Presently, the Portal is receiving approximately 8000 complaints on daily basis. The number of complaints has increased manifold during the past months, and this will continue to rise in future. To address the issue of increasing cybercrimes, the proactive approach shall be adopted. • Description This framework focuses on the mitigation of cybercrimes by adopting a proactive approach. The framework's output will enable the prediction of likely cash withdrawal locations, which, in turn, will allow law enforcement agencies (LEAs) at the state and local levels, coordinated by I4C, to implement proactive interventions. These interventions could include deploying special teams or alerting local banks and ATMs in high-risk areas. The intelligence generated would also help banks and financial institutions (FIs) through the Citizen Financial Cyber Fraud Reporting and Management System, enabling faster fund blocking and increasing the chances of recovery. By supporting real-time actionable intelligence sharing across jurisdictions, law enforcement agencies and Banks/FIs will be able to respond faster and more effectively to cyber threats. This approach goes beyond merely reacting to complaints and creates a powerful, data-driven defense against financial cyber frauds, strengthening India's overall cybersecurity posture. Enhancing coordination between law enforcement and financial entities will ensure better detection and prevention of financial crimes, creating a more unified and efficient approach to combating cybercrime. • Key Deliverables Component:- Description a. Predictive Analytics Engine :-AI/ML-based system to analyse historical cybercrime and financial data to predict potential withdrawal hotspots. Features include pattern detection, geospatial risk modelling, and real-time alerts. b. Risk Heatmap Dashboard:-GIS-enabled dashboard visualizing real-time and potential risk zones with drill-down filters by time, location, and crime category etc. c. Law Enforcement Interface:-Secure interface for investigators to access alerts, intelligence reports, and evidence documentation. d. Alert & Notification System:-Real-time notifications to law enforcements, banks, and I4C officers via SMS,email, API, or dashboard triggers.