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🏦 Banking🏗️ 4 Tech Levels🚀 Production-Grade

Fraud Detection AI

See 4 architectures solve real problems: MCP → RAG → Multi-Tool → Multi-Agent

September 12, 2025
demointeractivefraud-detectionbankingmulti-agent
🤖Demo 1 of 4

Advanced Multi-Agent Fraud System

BOFU: Production LangGraph with Router → Specialized Agents → Real-time Decision

1. Select Analysis Scenario

Transaction triggers multiple fraud signals: velocity spike, new device, unusual amount, foreign merchant. Router invokes all specialist agents in parallel.
Competitors: Transaction: $3,200 at electronics store in Singapore, Customer: Business consultant, travels to Asia quarterly, Context: 7th transaction today, new device, but merchant category matches travel pattern

2. Monday's Prompt

This positioning analysis prompt from Monday's insight will be processed by Tuesday's agents in Wednesday's workflow.

3. Run Multi-Agent Workflow

You'll see 5 AI agents: Data Collector → Messaging Analyzer → Position Mapper → Strategy Advisor → Insight Generator
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🔌Demo 2 of 4

MCP Tool Use Pattern

TOFU: LLM calls fraud detection tools via Model Context Protocol

Automation Task

User query: 'Is this transaction suspicious? Customer 84729, $450 purchase' → LLM decides to check velocity → Executes check_velocity(84729, 60min) → Returns 'No velocity issue' → LLM responds with fraud assessment

Monitoring:Customer 84729
ai-agent-terminal

Terminal ready. Run simulation to begin.

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🔍Demo 3 of 4

RAG-Based Fraud Intelligence

MOFU: Search 10,000+ historical fraud cases with vector similarity

Search Query

"Search Historical Fraud Cases for Similar Patterns"

Embed query → Search 10,000+ fraud case vectors → Retrieve top 5 matches (cosine similarity >0.75) → LLM analyzes patterns → Identifies common fraud signals

Document Sources:Case #8472: Card-not-present fraud, velocity 8 txns/2hrs, electronicsCase #9201: Stolen card, new device, Singapore merchant, $2,400Case #7834: Account takeover, device change, rapid transactionsCase #10483: Legitimate business travel, false positive, SingaporeCase #6729: Fraud ring, coordinated attacks, electronics merchants

RAG Vector Search Network

Embedding
Searching
Generating
Query
Vector
Documents
Result

Search Results

Run visualization to see results

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🛠️Demo 4 of 4

Multi-Tool Fraud Orchestration

MOFU: Multiple fraud tools working together in real-time stream processing

ROI Analysis

Orchestrator receives transaction from Kafka → Calls check_velocity() + get_customer_history() + analyze_merchant_risk() in parallel → Risk scorer aggregates (weighted: velocity 30%, customer 40%, merchant 30%) → Decision engine applies rules (auto-approve <0.3, review 0.3-0.7, decline >0.7) → Alert tool notifies fraud team if needed

Integrations:Transaction Stream: 127,453 transactions processed todayAuto-Approved: 89,217 (70%) - Low risk (<0.3)Flagged for Review: 31,864 (25%) - Medium risk (0.3-0.7)Auto-Declined: 6,372 (5%) - High risk (>0.7)Avg Decision Time: 47ms per transaction
Manual Process

Time to Complete

240 min

Cost per Analysis

$850

Data Points Analyzed

150

Accuracy Rate

73.0%

Reports per Day

2

Error Rate

18.0%

Ready for Production Fraud Detection System?

We'll build your custom multi-agent fraud system: MCP tools (TOFU - try tool calling) → RAG search (MOFU - search 10K+ cases) → Multi-tool orchestration (MOFU - 100K+ txns/day real-time) → Multi-agent LangGraph (BOFU - adaptive routing, 99.7% fraud catch rate, 88% false positive reduction). From demos to deployment in 8-10 weeks.

Book Architecture ReviewStart with MCP Tools (TOFU)
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