Cognitive Shields fraud detection system

From: aidotengineer

Cognitive Shield is presented as a next-generation platform designed to protect financial ecosystems against sophisticated, AI-driven fraud threats [00:12:02]. The system’s core premise is that the same artificial intelligence (AI) used to commit fraud can be trained to detect, defend, and protect against it [00:11:07].

The Challenge: AI-Driven Fraud

The current fraud landscape is characterized by synthetic identities, deep fake onboarding, and AI-driven scams that appear more human than actual humans [00:01:39]. These threats don’t “break in”; they get verified and walk through the front door undetected [00:01:50]. The challenge has shifted from detecting old-school fraud to detecting intelligence itself [00:02:10].

Real-Life Examples of AI-Driven Scams

Cognitive Shield illustrates the problem with three real-life stories:

• Anthony’s Voice Cloning Scam Anthony, a retired father in California, received a panicked phone call from a voice undeniably his son’s, claiming an accident and needing $50,000 for bail [00:03:34]. A second man, claiming to be his son’s lawyer, urged immediate wire transfer [00:04:06]. Anthony, unfamiliar with deep fake technology but trusting his son’s voice, wired his entire retirement savings [00:04:23]. It was later discovered to be an AI-generated voice clone, created using publicly available TikTok videos of his son [00:04:40].

• Lisa’s Pig Butchering Scam Lisa, a 45-year-old woman from Ohio, was messaged on Instagram by a man claiming to be a famous Australian TV star [00:05:05]. Over 18 months, he promised marriage and called her his soulmate, always citing visa and money issues for not meeting [00:05:31]. Lisa sent nearly $40,000 of her savings [00:05:48]. The man’s face was AI-generated, and the scam, known as “pig butchering,” involved building a fake relationship to steal money, often using AI and crypto to hide tracks [00:05:56].

• Xavier’s Crypto Rugpull Scam Xavier, a financially savvy accountant, invested $60,000 of his personal savings and his entire 401k into “ZipMax Pro,” a flashy new cryptocurrency project [00:06:44]. The project featured a professional website, investor testimonials, a white paper with AI and blockchain jargon, an active Discord channel, synthetic avatars, and even deep fake videos of Elon Musk endorsing it [00:07:22]. The pitch promised up to 35% annual returns [00:08:05]. The creators eventually performed a “rugpull,” crashing the coin’s value, and Xavier lost everything [00:08:34]. Every element of this scam was powered by AI, from fake ID verification to AI-written smart contracts and synthetic influencers [00:09:04].

Escalating Threat Statistics

• AI-powered scams surged by 375% since 2023 [00:09:32].
• 76% of synthetic identities now bypass traditional fraud detection [00:09:47].
• Americans reported a record $9.3 billion in losses from crypto-related crime, a 66% jump in one year [00:09:54].
• These are intelligent, emotionally engineered attacks built by machines to exploit trust at scale [00:10:10].

The Paradox: AI Against AI

While AI can be used to deceive, defraud, and exploit, it can also be used to detect, defend, and protect [00:10:46]. The same AI trained to commit fraud can be retrained to stop it, recognizing and shutting down manipulated behaviors [00:11:10].

Cognitive Shield: A Three-Layer Defense System

Cognitive Shield operates as a simple three-layer defense system, addressing different aspects of the fraud problem from prevention to real-time detection and intelligent response [00:12:16].

Layer 1: Secure User and Regulatory Management

This foundational layer manages user data, licensing, examination cases, and payment data securely [00:12:41]. AI is deeply integrated to guide users through complex processes, flag potential risks, check for missing information, and offer real-time guidance [00:13:00]. It also reviews responses and documents for unusual patterns, clarifies fines and deadlines, and provides a smart built-in assistant for queries [00:16:26]. Everything is presented in role-specific dashboards [00:17:25].

Layer 2: Real-time Fraud Detection Engine

This core layer is engineered to identify and mitigate sophisticated fraud attempts in real time using state-of-the-art AI technologies [00:18:03].

• Eight Specialized Detection Modules [00:18:20]:

  • Deep fake detection utilizes Generative Adversarial Network (GAN)-based systems to identify manipulated media [00:18:31].
  • Bot detection employs machine learning classifiers and gradient boosting machines to discern automated bot activities in blockchain transactions [00:18:45].
  • Fishing detection analyzes communication patterns using Natural Language Processing (NLP) to detect AI-generated fishing attempts [00:18:58].
  • Crypto scam generation applies Graph Neural Networks to analyze transaction networks and identify anomalies [00:19:13].
  • Additional modules include detection for synthetic identities and more [00:13:43].

• Advanced AI Technologies [00:19:30]:

  • Deep Learning: Analyzes images and audio to detect deep fake and voice cloning quickly [00:19:41].
  • Graph Neural Networks (GNN): Tracks connections between users, devices, and transactions to spot hidden fraud rings [00:19:52].
  • Natural Language Processing (NLP): Reads and interprets text to detect phishing attempts, social engineering tricks, and unusual language [00:20:07].
  • Multimodal Signal Processing: Combines text, voice, and metadata for a comprehensive picture of threats [00:20:19].

Graph-powered AI for Hidden Fraud

Fraud often involves a network of connected people, accounts, and devices [00:20:51]. Cognitive Shield addresses this by focusing on connections, not just individual events [00:21:03].

1. Building the Graph: Agentic workflows built using Crew AI and Large Language Models (LLMs) extract entities and relationships from unstructured data (text, PDF, documents, forms, emails, logs) to create a structured knowledge base [00:21:16]. This data is enriched with information from internal PostgreSQL databases to create a complete real-time view of the fraud landscape [00:22:00]. GNN models then find hidden connections like synced accounts or reused devices [00:22:18].
2. Neo4j for Persistence: Neo4j is used as the graph persistent mechanism to store all nodes and relationships [00:22:55].
3. Asking Graph-Smart Questions: A Neo4j-based Retrieval Augmented Generation (RAG) system integrates with LLMs to convert natural language user queries into Cypher language for Neo4j, enabling real-time exploitation of graph relationships and surfacing patterns missed by traditional relational systems [00:23:16].

Layer 3: Intelligent Response and Compliance Hub

This layer turns alerts into actions, providing smarter, faster, and more coordinated responses [00:24:30].

• Unified Fraud Intelligence Console: A “mission control” that consolidates insights from across the system, featuring AI-powered natural language search for investigations [00:24:52].
• Real-time Dashboard and Adaptive Analytics: Provides a live view of fraud hotspots, trending tactics, and connected actors, enabling faster and more informed decisions [00:25:31].
• Case Escalation and Alerting System: Automatically analyzes the severity of open cases and routes them to the correct person or team using a mix of rule-based and LLM-based logic [00:26:09]. All actions are logged with role-based access and a full audit trail [00:26:41].
• Compliance-Ready Reporting: All investigations are traceable, and reports can be exported in PDF or CSV formats, ensuring clarity and documentation for regulators, auditors, and internal teams [00:26:54].

Cognitive Shield Application Walkthrough

The Cognitive Shield application offers a main dashboard displaying recent searches, emerging threats, and alerts [00:28:02]. Its features include:

• User management (account, security, contact management) [00:28:45].
• Case management (creation, AI-powered analysis, search) [00:29:08].
• Examination management (scheduling, risk assessment, business rules, analytics, reporting) [00:29:21].
• Invoice management (creation, export, AI analysis) [00:30:05].
• Processing flow (organization linking, calculations, AI discrepancy detection) [00:30:36].
• Payment portal (pending payments, history, receipts) [00:30:49].
• Multimodal chat assistant (voice output/input, web search, data browsing) [00:31:02].
• Deep fake fraud detection (images, videos, audios, threat intelligence) [00:31:27].
• Payment fraud monitoring (suspicious transactions, cases, response times, trends, analytics) [00:31:53].
• Web fraud and social media fraud monitoring [00:32:23].
• Cryptocurrency fraud (suspicious wallets, smart contracts, blockchain activity, financial impact, trends) [00:32:50].
• Fishing detection (campaigns, domain analysis, targeted organizations) [00:33:15].
• Bot detection (networks, behavior, threat analytics) [00:33:31].
• Graph-based fraud detection knowledge graph builder (Neo4j connection, text-to-graph, PDF-to-graph, query generator, graph RAG) [00:33:43].
• Investigation and enforcement (searches, domain regulation, analytics dashboard, AI insights, alerting system) [00:34:29].
• API-driven architecture for data extraction and management [00:35:46].

Architecture

Cognitive Shield’s architecture is built for real-time fraud detection [00:37:44]:

• Front End: Streamlit for user-friendly, real-time dashboards [00:36:44].
• API Layer: Fast API handles incoming data such as logins, transactions, and document uploads [00:36:54].
• AI Layer: Powered by Crew AI, it serves as the system’s brain, running multiple collaborative AI agents [00:37:05].
• Data Layer: PostgreSQL database for general data and Neo4j for graph analysis [00:37:21].
• AI Agents: Supported by Graph RAG and LangChain [00:37:33].

Key Learnings in Building the System

The development of Cognitive Shield emphasized several principles for effective AI fraud defense [00:38:10]:

• Security First: Trust must be ingrained from day one, not patched in later [00:38:20].
• Multiple Specialized Agents: Don’t rely on a single AI model; use multiple specialized agents for different tasks, allowing them to collaborate [00:38:31].
• Think in Graphs: Graphs help detect hidden connections missed by relational databases [00:39:06].
• Microservices Architecture: Build with microservices (e.g., using Fast API) for scalability instead of monolithic systems [00:39:27].
• Observability: Monitor AI models for uptime, false positives, and false negatives, tracking everything to ensure explainable decisions [00:39:41].
• Privacy by Design: Encrypt everything and assume nothing to build privacy from the start [00:40:06].

Conclusion

Cognitive Shield’s three-step system focuses on data trust and security (Layer 1), real-time fraud detection powered by deep learning and graph AI (Layer 2), and an intelligence hub for responses and compliance (Layer 3) [00:40:44].

Key takeaways include:

• AI is not optional; it is the future of fraud defense [00:41:59].
• Graph analysis is essential for uncovering network connections missed by traditional tables [00:42:10].
• Multi-agent LLMs provide speed, clarity, and context [00:42:29].

The urgency to act is underscored by projections that by 2027, 90% of cyber attacks will be AI-driven, and fraud losses will surpass $100 billion per year [00:42:42]. Cognitive Shield aims to stop fraud before it starts [00:43:08].

Dedication

The presentation is dedicated to Jeremy Howard, the visionary behind Fast.AI, whose teachings on deep learning and belief in open, ethical, and accessible AI profoundly influenced the development of Cognitive Shield [00:43:43].