ScamGuardSecurities

ScamGuard in Securities Markets ScamGuard is an innovative, AI-driven platform designed to combat fraud in securities markets, focusing on the Fraud track of the Securities Market. It serves as a comprehensive shield for retail investors by proactively detecting, preventing, and educating against common scams such as pump-and-dump schemes, Ponzi operations, unregistered investment advisories, social media impersonations, and phishing attacks. By leveraging real-time data from social media, trading platforms, and investor reports, ScamGuard analyzes patterns to flag suspicious activities, alerts regulators like SEBI, and empowers users with verifiable tools for safe investing. Integrated with Digital Public Infrastructure (DPI) like Aadhaar for identity verification, it aims to reduce the staggering losses from investment frauds—projected at INR 20,000 crore in 2025 alone—while fostering a more transparent and accessible market ecosystem. Market Impact of Product/Process ScamGuard addresses critical pain points in India's securities markets, where fraud cases have surged amid the stock market boom, with over 150,000 reported stock fraud victims in the previous year. It enhances investor safety by employing real-time anomaly detection to identify pump-and-dump scams, which SEBI has targeted through AI surveillance in 2025 reforms. For market infrastructure, it introduces automated compliance checks that integrate with exchanges like BSE and NSE, reducing manipulative practices like insider trading and market abuse. Investor access is improved via a user-friendly mobile app featuring a scam verification chatbot, which educates retail participants on red flags such as "get-rich-quick" promises or fake social media tips, thereby building confidence and encouraging broader participation. Overall, it could deepen market stability by curbing losses from digital scams, which reached INR 4,245 crore in the first 10 months of FY 2024–25. By preventing Ponzi schemes and AI-enabled frauds like pig butchering, ScamGuard fosters a safer environment, potentially increasing retail investor inflows and compliance adherence across the ecosystem. Technology Stack ScamGuard harnesses advanced technologies with a focus on robust cybersecurity. At its core, Machine Learning (ML) models, including anomaly detection algorithms from libraries like scikit-learn and TensorFlow, analyze trading patterns for irregularities such as sudden volume spikes indicative of pump-and-dump. Large Language Models (LLMs) like Grok or similar open-source variants power Natural Language Processing (NLP) for scanning social media platforms (e.g., X, WhatsApp groups) to detect fraudulent language in investment tips or impersonations. Blockchain technology ensures immutable logging of transactions and audit trails, using platforms like Hyperledger for tamper-proof records that aid in fraud investigations. Integration with DPI, such as Aadhaar-based eKYC and UPI for secure payments, verifies user identities and prevents phishing via fake apps. Cybersecurity is prioritized with zero-trust architecture, end-to-end encryption (using AES-256), and AI-driven threat detection to counter emerging risks like deepfakes or AI scams. Data is processed on cloud infrastructure (e.g., AWS or Azure) with compliance to SEBI's data localization norms, ensuring privacy and resilience against breaches. Feasibility ScamGuard is designed for real-world deployability, building on existing SEBI initiatives like AI-based market surveillance and collaborations with agencies such as SFIO and NCRB for fraud detection. Implementation is eased by APIs from partners like BSE, CDSL, and NSDL for seamless data integration, requiring minimal new infrastructure—primarily cloud setup and model training on historical fraud data from SEBI's databases. A phased rollout starts with a pilot on social media monitoring, similar to SEBI's 'SEBI vs SCAM' drive, before expanding to full trading analysis. Development can be completed in 3-6 months using agile methodologies, with open-source tools reducing costs. Regulatory approval is straightforward as it aligns with SEBI's mandates for internal fraud mechanisms in brokers and AMCs, and no major legal hurdles exist given its non-intrusive, opt-in nature for investors. Scalability The platform is built for exponential growth, handling increased scope from individual investor protection to market-wide supervision. Cloud-native architecture allows auto-scaling to manage millions of users and transactions, processing high-velocity data streams (e.g., 1,000+ trades/second) using distributed computing like Apache Kafka for real-time ingestion. ML models can be retrained on expanding datasets, accommodating rising fraud volumes as seen in 2025's surge. It scales geographically by integrating with regional exchanges and internationally via blockchain interoperability. User growth is supported through a modular app design, with potential to expand features like predictive analytics for emerging threats. At full scale, it could monitor billions of social media posts annually, similar to SEBI's enhanced surveillance, while maintaining low latency through edge computing. Alignment with SEBI's Mandate ScamGuard directly supports SEBI's core pillars: investor protection by providing tools to verify investments and report scams, reducing exposure to fraudulent schemes like those on social media. For market development, it promotes transparency and efficiency, encouraging retail participation through education and safe access, aligning with SEBI's investor awareness initiatives. Supervision is enhanced via automated alerts to SEBI for proactive enforcement, building on existing frameworks like the new '1600' phone series for fraud combat and AI-driven detection. By collaborating with entities like ICAI and leveraging data science for anomaly detection, it strengthens regulatory oversight, ensuring a fair and orderly market. This concept catalyzes tech-led innovation, fully in line with SEBI's vision for a fraud-resilient ecosystem. ScamGuard, an AI-powered application designed to detect and prevent financial scams. ey factors that make ScamGuard impactful, based on the criteria outlined (market impact, technology stack, feasibility, scalability, and SEBI alignment)