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Fintech • Active

Triage: Fintech

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

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

99.5%
Detection Rate
-70%
False Positives
< 100ms
Response Time
$2M+
Cost Savings
Free 20-min discovery + 48h ROI audit Production-grade, not a demo3 weeks to first value
✓
✓
✓
Real-time scoring <100ms • Conservative thresholds during model updates • Borderline flagged
Why this vs status quo
Do nothing
99.5% lost
Manual Fintech
Build in-house
6–12 months
Hire + maintain
Triage
-70%
Weeks, not months
Real-time Monitoring • Pattern Detection
Save ~10×

What ships

  • Real-time Monitoring — Stream processing of millions of transactions per second wit…
  • Pattern Detection — ML models identifying fraudulent patterns across transaction…
  • Anomaly Detection — Unsupervised learning detecting unusual patterns that deviat…
  • Investigation Workflow — Automated case management with evidence gathering and recomm…
  • Rule Engine — Configurable rule engine for business-specific fraud prevent…
  • Network Analysis — Graph-based analysis detecting fraud rings and organized fra…

Human signs every critical step • Audit trail included

Sample output & architecture

Triage — Production Blueprint
Dated 18 Aug 2026 • #TRI-2026-041
Detection Rate: 99.5%False Positives: -70%Response Time: < 100msFintech
Agents
Scoring Agent • Pattern Agent • Investigation Agent
Stack
Azure AI Foundry, Apache Kafka, Python, FastAPI
Workflow preview
1. Transaction Ingestion2. Real-time Scoring3. Pattern Analysis4. Decision Engine
Data flow: Transaction Stream → Real-time Scoring → Pattern Analysis → Decision Engine → Action Execution → Alert Generation → Investigation → Feedback Loop
Azure AI Foundry + LangChain • Azure AI Foundry provides real-time ML inference for transacv1.0 • Audit trail

Why Azure AI Foundry + LangChain wins

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

Alternatives: AWS Fraud Detector + Custom ML, Featurespace + Semantic Kernel

How Triage works — Real-time to first value

Transaction Ingestion → Real-time Scoring → Pattern Analysis → delivery. Full 8-step trail collapses below.

01 • Real-time
Transaction Ingestion
Real-time transaction data ingested from payment systems.
02 • < 100ms
Real-time Scoring
ML models score transaction fraud probability in real-time.
03 • < 500ms
Pattern Analysis
Analyze transaction patterns against historical behavior.
Full 8-step audit trail
1
Transaction Ingestion • Real-time
Real-time transaction data ingested from payment systems.
2
Real-time Scoring • < 100ms
ML models score transaction fraud probability in real-time.
3
Pattern Analysis • < 500ms
Analyze transaction patterns against historical behavior.
4
Decision Engine • < 200ms
Automated decision based on scores, patterns, and rules.
5
Action Execution • Instant
Execute fraud prevention actions — block, challenge, or approve.
6
Alert Generation • < 1 second
Generate alerts for suspicious activity requiring investigation.
7
Investigation • Minutes to hours
Automated investigation with evidence gathering and recommendations.
8
Feedback Loop • Ongoing
Learn from investigation outcomes to improve detection models.

Which Fintech use case is yours?

Tap your model — see the exact hinge we test.

Payment Fraud

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

Do nothing
99.5% wasted
  • • Manual Fintech process
  • • No audit trail
  • • No evidence for inspector/investor
Build in-house
6–12 months • Hire team
  • • Hire + train + maintain
  • • No re-run if product changes
  • • Hard to show investor quickly
Triage — -70%
8 steps • Agents • Evidence
  • Real-time Monitoring
  • Pattern Detection
  • Daily watch + re-run

Multi-agent mesh — orchestrated

Live system presentation

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

Objections, answered

If it’s not answered, you’ll hesitate. So we answer it here.

Real-time fraud detection — pattern analysis, anomaly detection, and automated investigation workflows for financial transactions. Delivered as 5 specialized agents + 8-step workflow. You get Real-time Monitoring + Pattern Detection and a dated evidence trail.
Production in 3 weeks • Audit trail

Get Triage live — without the Fintech drag.

Real-time fraud detection — pattern analysis, anomaly detection, and automated investigation workflows for financial transactions.… Implementation: Data Pipeline → Detection Models → Investigation UI → Production Hardening.

Questions? Talk to a human • Responses in < 24h • Human signs every critical step

Evidence + audit trail•Azure AI Foundry + LangChain•Deterministic — LLM only for rationale