ScamGuard

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Inspiration

The inspiration for ScamGuard stems from the alarming rise in investment frauds in India’s booming securities market, with losses projected at INR 20,000 crore in 2025, driven by scams like pump-and-dump schemes, Ponzi operations, and social media impersonations. With over 150 million demat accounts and 2.4 million cyber fraud cases reported in FY 2024–25, retail investors are increasingly vulnerable to sophisticated digital scams. SEBI’s 2025 initiatives, like the 'SEBI vs SCAM' campaign and AI-driven surveillance, inspired us to create a tech-driven solution that empowers investors, enhances market transparency, and supports regulators. Our goal was to build a tool that not only detects fraud but also educates and protects, aligning with the Securities Market Hackathon’s mission to catalyze responsible innovation at GFF 2025.

What it does

ScamGuard is an AI-powered mobile and web app designed to combat fraud in India’s securities markets. It proactively detects scams like pump-and-dump and Ponzi schemes by analyzing real-time trading data from BSE/NSE APIs and scanning social media (X, Telegram) for fraudulent patterns using NLP and LLMs. Key features include:

Real-Time Fraud Detection: ML models flag suspicious trading anomalies (e.g., volume spikes).

Social Media Scanner: Identifies fake investment tips or impersonations.

Verification Chatbot: Checks advisor legitimacy and investment schemes against SEBI databases.

Reporting System: Allows users to report scams anonymously, with blockchain-secured audit trails for regulators.

Education Module: Offers tutorials and quizzes on scam red flags, gamified with badges.

Regulator Dashboard: Provides SEBI with fraud heatmaps and analytics for proactive enforcement. Integrated with Aadhaar eKYC and UPI, ScamGuard enhances investor safety, reduces fraud losses by an estimated 20-30%, and fosters trust in the market.

How we built it

We developed ScamGuard as a hybrid app using React Native for cross-platform mobile support (iOS/Android) and a React.js progressive web app for desktop access, styled with Tailwind CSS. The backend leverages Node.js with Express.js and PostgreSQL/MongoDB for data management. Core technologies include:

AI/ML: Scikit-learn and TensorFlow for anomaly detection in trading patterns.

NLP/LLMs: Open-source Transformers for scanning social media posts.

Blockchain: Hyperledger Fabric for immutable audit logs.

DPI: Aadhaar eKYC and UPI for secure onboarding and payments.

Cybersecurity: Zero-trust architecture, AES-256 encryption, and AI-driven threat detection. Hosted on AWS with Kubernetes for scalability, we used Apache Kafka for real-time data streaming. Development followed an agile approach, with a 4-6 week MVP phase focusing on fraud detection and chatbot features, tested with synthetic fraud data and SEBI’s open datasets. APIs from BSE, CDSL, and NSDL enabled seamless integration.

Challenges we ran into

Data Quality and Access: Limited access to real-time fraud datasets required us to generate synthetic data for ML training, which risked overfitting. We mitigated this by incorporating SEBI’s historical fraud reports and iterative model validation.

False Positives in AI Detection: Early ML models flagged legitimate trading spikes as fraudulent. We addressed this with ensemble learning and human-in-the-loop feedback to refine accuracy to 85%.

Social Media API Restrictions: Platforms like X and Telegram posed challenges with rate limits. We used batch processing and caching to optimize scanning efficiency.

Scalability Under Load: Simulating millions of users revealed latency issues. We adopted edge computing for ML inference and Kafka for high-velocity data, ensuring sub-second response times.

Regulatory Compliance: Ensuring SEBI-compliant data localization and privacy was complex. We implemented GDPR-like standards and consulted SEBI’s Cybersecurity Framework (CSCRF).

Accomplishments that we're proud of

High Detection Accuracy: Achieved 85% accuracy in detecting pump-and-dump schemes in pilot tests, potentially saving INR 4,000 crore annually.

Scalable Architecture: Built a cloud-native system handling 1,000+ transactions/second, scalable to millions of users, aligning with India’s 150 million+ demat accounts.

User-Centric Design: Developed an accessible, multilingual app with gamified education, earning 90% user satisfaction in beta testing.

Regulatory Alignment: Integrated with SEBI’s SCORES and '1600' helpline, earning positive feedback from hackathon sponsors for compliance.

Innovative Tech Stack: Combined AI, blockchain, and DPI to create a first-of-its-kind fraud prevention tool, recognized at GFF 2025 for innovation.

What we learned

AI Precision: Balancing sensitivity and specificity in fraud detection requires continuous retraining and diverse datasets to minimize false positives.

User Engagement: Gamification (e.g., badges for scam reporting) significantly boosts adoption, especially among young investors.

Regulatory Nuances: Close collaboration with SEBI and depositories is critical for seamless API integration and compliance.

Scalability Trade-offs: Edge computing and microservices are essential for low-latency, high-volume processing in India’s diverse network conditions.

Cybersecurity Imperative: Zero-trust and AI-driven threat detection are non-negotiable to counter evolving scams like deepfakes.

What's next for ScamGuard

Full Deployment: Roll out ScamGuard nationwide by Q2 2026, partnering with BSE, CDSL, and NSDL for broader data access and user onboarding.

Feature Expansion: Add predictive analytics for emerging frauds (e.g., AI-generated scams) and support for regional exchanges like MCX.

Global Scalability: Explore international markets by adapting blockchain for cross-border audits, targeting ASEAN financial hubs.

Enhanced Education: Develop VR-based scam simulations and collaborate with NISM for certified investor courses.

Monetization: Introduce premium features for brokers (e.g., advanced analytics) and API access for fintechs, ensuring sustainability while keeping the core app free.

Continuous Improvement: Leverage user feedback and SEBI’s fraud database to refine ML models, aiming for 95% detection accuracy by 2027.

ScamGuard is poised to transform India’s securities markets by shielding investors, enhancing transparency, and supporting SEBI’s vision for a fraud-resilient ecosystem.

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