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Seyhun Akyurek
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Fintech

Triage

Real-time fraud detection — pattern analysis, anomaly detection, and automated investigation workflows for financial transactions.

Azure AI Foundry
Apache Kafka
Python
FastAPI
Azure Database for PostgreSQL
Azure Cache for Redis
Azure Kubernetes Service
99.5%
Detection Rate
-70%
False Positives
< 100ms
Response Time
$2M+
Cost Savings

Project presentation

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Triage · Agent Mesh

ORCHESTRATORFintechScoringACTIVEPatternNODE 02InvestigationNODE 03NetworkNODE 04AlertNODE 0512345678
Step 1Real-time

Transaction Ingestion

Real-time transaction data ingested from payment systems.

Stream processingData normalizationFeature extractionEnrichment

Technical Design

Real-time fraud detection system with stream processing and ML scoring. AI agents analyze transactions, detect patterns, and manage investigation workflows.

System Components

Stream Processor

Real-time transaction ingestion

Scoring Engine

ML-based fraud probability scoring

Pattern Detector

Behavioral analysis and anomaly detection

Investigation Agent

Automated case management

Network Analyzer

Graph-based fraud ring detection

Data Flow

Transaction Stream
Real-time Scoring
Pattern Analysis
Decision Engine
Action Execution
Alert Generation
Investigation
Feedback Loop

Technology Choice

Azure AI Foundry + LangChain

Azure AI Foundry provides real-time ML inference for transaction scoring. LangChain orchestrates investigation workflows with evidence gathering.

Alternatives Considered

AWS Fraud Detector + Custom ML, Featurespace + Semantic Kernel

Core Frameworks

Azure AI Foundry

Real-time ML scoring and pattern detection

LangChain

Investigation workflow orchestration

Apache Kafka

Real-time transaction streaming

FastAPI

Fraud detection API endpoints

Implementation Plan

Data Pipeline

3 weeks
  • Transaction streaming
  • Feature store
  • Historical data

Detection Models

4 weeks
  • Pattern detection
  • Anomaly scoring
  • Rule engine

Investigation UI

3 weeks
  • Case management
  • Evidence tools
  • Reporting

Production Hardening

3 weeks
  • Real-time scoring
  • Alert system
  • Model monitoring

Key Milestones

Real-time scoring <100ms
Detection rate >99.5%
False positives -70%
Investigation automation working

Risk Mitigation

Conservative thresholds during model updates
Human review for high-value transactions
Regular model retraining with confirmed fraud cases

Key Features

Real-time Monitoring

Stream processing of millions of transactions per second with sub-100ms fraud scoring.

Pattern Detection

ML models identifying fraudulent patterns across transaction history and behavioral signals.

Anomaly Detection

Unsupervised learning detecting unusual patterns that deviate from normal customer behavior.

Investigation Workflow

Automated case management with evidence gathering and recommendation engine.

Rule Engine

Configurable rule engine for business-specific fraud prevention policies.

Network Analysis

Graph-based analysis detecting fraud rings and organized fraud networks.

How It Works

1

Transaction Ingestion

Real-time

Real-time transaction data ingested from payment systems.

Stream processing
Data normalization
Feature extraction
Enrichment
2

Real-time Scoring

< 100ms

ML models score transaction fraud probability in real-time.

Model inference
Feature computation
Score generation
Threshold check
3

Pattern Analysis

< 500ms

Analyze transaction patterns against historical behavior.

Behavioral baseline
Pattern matching
Velocity check
Anomaly detection
4

Decision Engine

< 200ms

Automated decision based on scores, patterns, and rules.

Rule evaluation
Score combination
Decision routing
Action selection
5

Action Execution

Instant

Execute fraud prevention actions — block, challenge, or approve.

Transaction block
Customer challenge
OTP verification
Approval
6

Alert Generation

< 1 second

Generate alerts for suspicious activity requiring investigation.

Alert creation
Priority assignment
Evidence attachment
Routing
7

Investigation

Minutes to hours

Automated investigation with evidence gathering and recommendations.

Case assembly
Evidence collection
Timeline creation
Recommendation
8

Feedback Loop

Ongoing

Learn from investigation outcomes to improve detection models.

Outcome tracking
Model retraining
Rule tuning
Performance monitoring

Multi-Agent Architecture

Scoring Agent

Real-time transaction scoring using ML models and rule engines.

  • Feature extraction
  • Model inference
  • Rule evaluation
  • Score generation
  • Threshold management

Pattern Agent

Identifies fraudulent patterns across transaction history.

  • Pattern matching
  • Behavioral analysis
  • Velocity checks
  • Cross-account analysis
  • Historical comparison

Investigation Agent

Automates fraud investigation workflows and evidence gathering.

  • Case creation
  • Evidence collection
  • Timeline construction
  • Recommendation generation
  • Escalation routing

Network Agent

Analyzes transaction networks to detect fraud rings and organized crime.

  • Graph analysis
  • Cluster detection
  • Entity resolution
  • Link analysis
  • Risk propagation

Alert Agent

Manages alert generation, prioritization, and customer notification.

  • Alert generation
  • Priority scoring
  • Customer notification
  • Action routing
  • Feedback collection

Use Cases

Payment Fraud

Detect and prevent fraudulent card-not-present and card-present transactions.

Account Takeover

Identify unauthorized account access and prevent credential-based attacks.

Identity Fraud

Detect synthetic and stolen identity usage in account opening.

Wire Fraud

Prevent business email compromise and wire transfer fraud.

Insurance Fraud

Detect fraudulent insurance claims and staged accidents.

AML Monitoring

Automated anti-money laundering transaction monitoring and reporting.

Interested in Triage?

Get in touch to discuss how this solution can be tailored to your needs.