project-ac3809f0
Mousoom SamantaAI for Digital Public Safety: Defeating Counterfeiting, Fraud & Digital Arrest Scams Theme: Smart Cities / Public Safety / Digital Trust / Geospatial Law Enforcement PROBLEM CONTEXT India registered 1.14 million cybercrime complaints in 2023 — up 60% from 2022 — and the trajectory has only steepened. The Ministry of Home Affairs reported that 'digital arrest' scams, where fraudsters impersonating CBI, ED, or Customs officers trap victims in multi-day psychological hostage situations over video call, defrauded citizens of over Rs 1,776 crore in just the first nine months of 2024. These are not opportunistic crimes — they are industrialised operations run from fraud compounds, often across borders, using spoofed numbers, AI-generated voices, and fake government portals. Separately, counterfeit currency remains a persistent threat: the RBI's Annual Report 2025 flagged record FICN (Fake Indian Currency Notes) seizures, with high-denomination Rs 500 fakes of sufficient quality to defeat manual detection in routine banking operations. What law enforcement lacks is not evidence after the fact — it is intelligence before mass victimisation occurs, and reliable tools to detect threats at the point of contact rather than the point of complaint. Solving this requires convergence of financial transaction intelligence, communication network analysis, physical security (counterfeit detection), and real-time public safety coordination — exactly the kind of multi-source, multi-agency intelligence problem where AI has the potential to be transformative. CHALLENGE STATEMENT Build an AI-powered Digital Public Safety Intelligence platform that equips law enforcement agencies, financial institutions, and citizens with proactive tools to detect, disrupt, and respond to digital fraud networks, counterfeit currency circulation, and organised scam operations — shifting from reactive case investigation to predictive threat neutralisation. WHAT YOU MAY BUILD Participants may explore areas such as: • Digital Arrest Scam Detection & Alerting — Real-time AI classifier trained on digital arrest scam patterns — call flow sequences, number spoofing signatures, script templates, video call metadata — that flags active scam sessions to telecom providers and potential victims before financial transfer occurs, with automated MHA alert generation. • Counterfeit Currency Identification Agent — Computer vision AI deployable on mobile devices, bank counting machines, and point-of-sale terminals that identifies fake notes through microprint analysis, security thread verification, serial number pattern validation, and UV feature simulation — providing field officers and bank tellers with instant, reliable identification across all denominations. • Fraud Network Graph Intelligence — Graph AI agent that analyses transaction metadata, call records, device fingerprints, and account linkages to map coordinated fraud campaigns — clustering victim reports, scammer infrastructure, and money mule networks into actionable intelligence packages that link across jurisdictions and can be submitted as court admissible evidence. • Geospatial Crime Pattern Intelligence — Geospatial AI layer for law enforcement that maps fraud complaint locations, counterfeit currency seizure points, and cybercrime hotspots — enabling patrol prioritisation, resource deployment, and inter-district intelligence sharing in near real time through a command centre interface. • Citizen Fraud Shield (Multi-channel) — Conversational AI accessible via WhatsApp, IVR, and mobile app that walks citizens through real-time fraud risk assessment for suspicious calls, payment requests, or messages — providing instant verdicts, guided reporting to NCRB portals, and advisory in 12 regional languages. These examples are illustrative only. SUGGESTED TECHNOLOGIES • Computer Vision (counterfeit detection, deepfake identification) • Graph AI & Network Analysis (fraud ring mapping) • NLP / LLMs (scam script and voice pattern classification) • Geospatial Intelligence (crime mapping, patrol optimisation) • Speech AI (voice spoofing and AI-voice detection) • Agentic AI for multi-source intelligence fusion EXPECTED DELIVERABLES • Working Prototype • Architecture Diagram • Presentation Deck • Demo Video Evaluation Focus Counterfeit detection accuracy across denominations and print quality, digital arrest scam detection precision and recall, fraud network detection lead time before mass victimisation, false positive rate for citizen-facing tools (must be very low), and auditability of intelligence packages for legal admissibility.
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System Requirements
project-ac3809f0
Introduction
The project-ac3809f0 aims to develop an AI-powered Digital Public Safety Intelligence platform. This platform will equip law enforcement agencies, financial institutions, and citizens with proactive tools to detect, disrupt, and respond to digital fraud networks, counterfeit currency circulation, and organized scam operations. The focus is on shifting from reactive case investigation to predictive threat neutralization.
System Overview
The system will integrate AI-driven tools for real-time detection and prevention of digital fraud, counterfeit currency, and scam operations. It will focus on predictive intelligence and multi-agency coordination to provide a comprehensive solution for public safety challenges in India.
Source Content Inventory
- Digital Arrest Scam Detection & Alerting: Real-time AI classifier for scam patterns.
- Counterfeit Currency Identification Agent: Computer vision AI for fake note detection.
- Fraud Network Graph Intelligence: Graph AI for mapping fraud campaigns.
- Geospatial Crime Pattern Intelligence: Geospatial AI for crime mapping.
- Citizen Fraud Shield: Conversational AI for fraud risk assessment.
Functional Requirements as Story Points
- As a law enforcement officer, I should be able to receive real-time alerts for digital arrest scams to prevent financial transfers.
- As a bank teller, I should be able to use a mobile device to identify counterfeit currency instantly.
- As a financial analyst, I should be able to map fraud networks using transaction metadata and call records.
- As a law enforcement officer, I should be able to visualize crime patterns geospatially for better resource deployment.
- As a citizen, I should be able to assess fraud risks through a conversational AI in multiple languages.
User Personas
- Law Enforcement Officer: Uses the platform for real-time alerts and crime pattern visualization.
- Bank Teller: Utilizes mobile devices for counterfeit currency detection.
- Financial Analyst: Analyzes fraud networks and provides actionable intelligence.
- Citizen: Engages with the platform for fraud risk assessment and reporting.
Core User Flows
- Law enforcement receives scam alert -> investigates -> prevents financial transfer.
- Bank teller scans currency -> AI identifies counterfeit -> alerts teller.
- Financial analyst inputs data -> AI maps fraud network -> generates intelligence report.
- Citizen receives suspicious call -> uses AI for risk assessment -> reports to NCRB.
Visuals Colors and Theme
- primary: #1A237E (Indigo)
- primary_light: #534BAE (Light Indigo)
- secondary: #D32F2F (Crimson)
- accent: #FFC107 (Amber)
- highlight: #FF9800 (Orange)
- bg: #F5F5F5 (Light Gray)
- surface: rgba(255, 255, 255, 0.8)
- text: #212121 (Dark Gray)
- text_muted: #757575 (Muted Gray)
- border: rgba(0, 0, 0, 0.1)
Signature Design Concept
Interactive Crime Network Map
The homepage will feature an interactive crime network map that visualizes fraud networks and crime hotspots. Users can click on nodes representing different scam operations, which will expand to show detailed information about each operation, including involved parties and geographical locations. The map will be dynamic, with nodes and connections animated using d3 for data visualization and motion/react for smooth transitions. This interactive experience will provide users with an engaging way to explore and understand the scope of digital fraud activities.
Landing Hero Motion Brief
The landing page will feature a continuously moving 2D composition illustrating the transformation of raw data into actionable intelligence. The animation will depict data points flowing into a central AI engine, which processes and outputs a clear, actionable intelligence report. The animation will loop every 10 seconds, with layers including data streams, the AI engine, and the resulting intelligence report. Users can hover over elements for additional information, and the animation will adapt to different screen sizes for a seamless experience.
Interaction Model & Motion Direction
- Intended Interaction Model: Animated
- The landing page will feature moderate scroll-triggered reveals and hover transitions, enhancing user engagement without overwhelming them.
- Each section of the landing page will have a distinctive core mechanic, such as morphing SVGs or kinetic typography, to maintain user interest and convey information effectively.
Non-Functional Requirements
- The system must maintain a low false positive rate for citizen-facing tools.
- Intelligence packages must be auditable for legal admissibility.
- The platform should support multi-agency coordination and data sharing.
Tech Stack
- Frontend: React for Web
- Backend: Python, FastAPI
- Database: MySQL or MariaDB
- AI Models: GPT 5.4, Claude Sonnet 5, Google Nano banana
- AI Tools: Litellm, Langchain
- Orchestration: Docker, Kubernetes
Assumptions and Constraints
- The platform will primarily serve Indian law enforcement and financial institutions.
- It must handle large volumes of data in real-time.
- Multi-language support is essential for citizen engagement.
Glossary
- AI: Artificial Intelligence
- NCRB: National Crime Records Bureau
- MHA: Ministry of Home Affairs
- FICN: Fake Indian Currency Notes
- CBI: Central Bureau of Investigation
- ED: Enforcement Directorate
Predict. Prevent.
Protect India.
Real-time intelligence to detect digital fraud, counterfeit currency, and organized scam networks — empowering law enforcement, banks, and citizens with predictive AI.
Five Pillars of
Digital Safety Intelligence
Purpose-built AI modules working in concert to detect, analyze, and neutralize digital fraud threats across India.
Scam Detection
Real-time AI classifier identifies digital arrest scams and fraud patterns before financial transfers occur.
Counterfeit Detection
Computer vision AI identifies Fake Indian Currency Notes instantly via mobile camera scan.
Network Intelligence
Graph AI maps fraud campaigns by linking transaction metadata, call records, and digital footprints.
Crime Mapping
Geospatial AI visualizes crime hotspots and patterns for optimized patrol deployment and resource allocation.
Citizen Shield
Conversational AI assesses fraud risks in multiple Indian languages, empowering citizens to report to NCRB.
Intelligence at Scale
Ready to Strengthen
India's Digital Defenses?
Join law enforcement agencies, financial institutions, and millions of citizens already protected by NCRB Intelligence Platform.
Predict. Prevent.
Protect India.
Real-time intelligence to detect digital fraud, counterfeit currency, and organized scam networks — empowering law enforcement, banks, and citizens with predictive AI.
Five Pillars of
Digital Safety Intelligence
Purpose-built AI modules working in concert to detect, analyze, and neutralize digital fraud threats across India.
Scam Detection
Real-time AI classifier identifies digital arrest scams and fraud patterns before financial transfers occur.
Counterfeit Detection
Computer vision AI identifies Fake Indian Currency Notes instantly via mobile camera scan.
Network Intelligence
Graph AI maps fraud campaigns by linking transaction metadata, call records, and digital footprints.
Crime Mapping
Geospatial AI visualizes crime hotspots and patterns for optimized patrol deployment and resource allocation.
Citizen Shield
Conversational AI assesses fraud risks in multiple Indian languages, empowering citizens to report to NCRB.
Intelligence at Scale
Ready to Strengthen
India's Digital Defenses?
Join law enforcement agencies, financial institutions, and millions of citizens already protected by NCRB Intelligence Platform.
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