AI Tools For Monitoring Fraudulent Activities

👁 Soon enterprise solution architects will be able to design complex System-Knowledge-Human-AI system for any existing enterprise use cases. In the below fraud detection & prevention use case in financial services, we designed four AI Agents interacting with human, systems, and knowledge : 1️⃣ AI Agent #1: Pattern Recognition Agent Role: Accelerate fraudulent activity identification for analysts. Knowledge & Memory: Fraud patterns and user behavior knowledge. Integrated Systems: Interfaces with transaction monitoring systems. Specificities: Specializes in real-time pattern and anomaly detection. 2️⃣ AI Agent #2: Investigation Assistant Agent Role: Supports analysts in verifying flagged transactions. Knowledge & Memory: Transaction history and known fraud methods. Integrated Systems: Taps into core banking and digital platforms. Specificities: Extracts data, provides context, and assesses risk. 3️⃣ AI Agent #3: Resolution Suggestion Agent Role: Proposes solutions for confirmed fraud cases. Knowledge & Memory: Past resolutions and related outcomes. Integrated Systems: Connects to incident management and customer platforms. Specificities: Analyzes scenarios, assesses impacts, and recommends actions. 4️⃣ AI Agent #4: Fraud Prevention Education Agent Role: Educates on fraud prevention. Knowledge & Memory: Fraud tactics and effective prevention methods. Integrated Systems: Works with customer channels, internal learning systems, or knowledge management systems. Specificities: Curates personalized content, updates dynamically, and nudges behavior. #llm #generativeai #multiagents

For decades, auditing has been reactive by design. You review the books, investigate inconsistencies, and flag issues. After the damage is done. But in today’s digital-first financial landscape, waiting for fraud to surface is no longer an option. By the time traditional audits uncover internal fraud, the losses have already mounted, and the trail may have gone cold. That’s where Agentic AI is flipping the script. Instead of forensic accounting after the fact, AI enables continuous, real-time auditing - catching irregularities as they happen, not days or months later. AI is your auditor assistant that never sleeps. It is always analyzing transactions, cross-referencing anomalies, and flagging inconsistencies as they come up. And not only is your AI assistant not sleeping, it’s actively learning along the way. It doesn’t just alert you to potential fraud - it understands patterns, predicts risks, and prioritizes critical cases before they escalate. This shift isn’t just about efficiency - it’s about fundamentally changing how we approach financial oversight. The companies that embrace proactive AI-driven audit systems will have a distinct advantage: lower fraud exposure, stronger internal controls, and a financial ecosystem built for transparency. What’s holding you back? #FinancialIntegrity #AIForFinance #InternalFraud #Audit #AgenticAI

Long before the AI copilot hype, SEON was built on a simple idea: Let fraud teams move fast, stay in control, and win. From day one, we combined layers of transparent and adaptive machine learning with an advanced no-code rules engine, because companies shouldn’t have to choose between: 1️⃣ Hiring a full in-house team to manually build rules 2️⃣ Blindly trusting a ML black-box score they can’t explain Both come with tradeoffs: cost, control, transparency. That’s why over 2 years ago, we launched a hybrid approach, bringing the best of both worlds. SEON customers go live fast (think days, not weeks), using industry best practices plus tailored last-mile config with our team. Then AI kicks in: • Detecting anomalies • Suggesting patterns • Surfacing deployable rules your fraud team can test, tweak, and launch The result? Less firefighting, more time for fraud teams to focus on strategic, revenue-generating work. And real fraud stopped. 💡 Just in February: • 427 SEON customers tested and deployed an AI-suggested rule • 70.3% of all fraud checks (~295 million) on our platform were influenced by an AI-suggested fraud rule • $1.27B in fraud attempts stopped, in real time While others are adding flashy LLM copilots to make their UX look smarter, we’re focused on making your fraud decisions better. 👉 Want to see how? https://lnkd.in/dFxSCP-x Or check this out: https://lnkd.in/dGrPm8_s