SIH26184/Blockchain & Cybersecurity

Cybercrime cash withdrawal prediction dashboard

A web dashboard and analytics tool for law enforcement that predicts likely cash withdrawal locations from cybercrime complaints and sends real-time alerts.

Ministry of Home Affairs · Indian Cyber Crime Coordination Centre (I4C),CIS Division · Software

Teams already in: 2/500almost everyone who enters · read 1 hour ago
Buildable in 36 hours: ModerateOur estimate, from their wording
Hard blockers: 1No dataset given
Submissions close: 15 Sept9 days left

Make this one of our two Compare with others

Signing in saves it for your whole team — everyone on your invite link sees the same two entries. Anything you shortlisted while signed out comes with you.

Submit on sih.gov.in

Whether this suits your team

Why this fits you

This organisation declared winners on 10 problem statement(s) in SIH 2025. It has awarded 4 joint winner(s), so it sometimes splits a prize between two teams.

What argues against it

Potential concern: no dataset provided.

What you would have to build

Decomposed from what the description asks for. Nothing added.

01
Predictive Analytics Engine
An AI or machine learning system that analyzes historical data to predict potential cash withdrawal hotspots.
02
Risk Heatmap Dashboard
A GIS-enabled dashboard that visualizes real-time and potential risk zones with filters.
03
Law Enforcement Interface
A secure interface for investigators to access alerts, intelligence reports, and evidence documentation.
04
Alert and Notification System
A system that sends real-time notifications to law enforcement, banks, and officers via SMS, email, API, or dashboard triggers.

What they ask for, and what they leave open

Both columns are read off the brief's own wording. Nothing here is inferred from the ministry's name.

They state

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.

They never say

what historical dataset format or schema will be provided
what specific machine learning models or accuracy thresholds are expected

A jury can still ask about these. Decide them deliberately rather than by accident.

What they'll ask you

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 more— the 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.

Before you commit

Each one is quoted from a gap in the brief, not a guess about your team.

No dataset provided
The description depends on real data, and the organisers have not attached a dataset link.

The data

What the organisers attached, and what the brief assumes you can get.

Dataset from the organisers: None attached
Does the brief need data?: Yes, and none is attached — finding or generating it is part of the work.

Also from Ministry of Home Affairs

Same organisation, same year. Reading two of theirs tells you more about what they care about than reading one.

Take this to an AI

Pick what you are about to do and copy the prompt. It carries the organisers' own wording, the constraints they never spell out, and an instruction not to invent requirements they never set.

Who has this problem, what already exists, and what you would have to find out.

# Context: India's Smart India Hackathon 2026

I am on a student team entering SIH 2026. Problem statement SIH26184, set by Ministry of Home Affairs.
Its official title, word for word: "Development of a Predictive Analytics Framework for Cybercrime Complaints to Forecast Likely Cash Withdrawal Locations in Advance, Enabling Generation of Actionable Intelligence for Timely and Proactive Cybercrime Intervention."
Theme: Blockchain & Cybersecurity. Category: software.

In plain terms: A web dashboard and analytics tool for law enforcement that predicts likely cash withdrawal locations from cybercrime complaints and sends real-time alerts.

## The organisers' full description, verbatim

• 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.

## What the brief never says

- what historical dataset format or schema will be provided
- what specific machine learning models or accuracy thresholds are expected
## Constraints that are not negotiable

- The judged build is a prototype demonstrated in roughly 36 hours by students, not a product.
- A jury evaluates it against this problem statement's own wording, not against general merit.
- We submit on sih.gov.in; the problem statement number and title above must match exactly.
- Your solution must not have appeared in any previous event or programme, of any sort.
- No dataset was attached, and the brief depends on data. Finding or generating it is part of the work.

## What I want from you

1. Who actually has this problem in India, and what they do about it today.
2. What already exists — government systems, open-source projects, published research — that we should not rebuild.
3. For each item under 'what the brief never says', what the realistic answer is likely to be and how I could verify it.
4. The two or three things about this domain that a student team would get wrong without local knowledge.
## How to answer

- Everything above between quotation marks is the organisers' own wording. Treat it as the requirement.
- Do NOT invent requirements the brief does not state. If something matters and is not stated, say so and ask me, rather than assuming.
- Where you are inferring rather than reading, mark it as your inference.
- Do not guess what this ministry wants from its name. Its wording above is the only evidence.

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Problem statement facts assembled by Wynit from Ministry of Home Affairs's published brief for SIH 2026.
The plain-English summary, the list of parts and the evaluator read are our interpretation of that text; the quoted title and the full description are theirs.

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.