How We Built AI-Powered Fraud Detection for Under $200/Month

February 1, 2026

How We Built AI-Powered Fraud Detection for Under $200/Month

When a financial services client approached us about their fraud detection challenges, they were clear about their constraints: tight budget, strict data security requirements, and a need to prove value fast.

They were manually reviewing thousands of credit card and bank transactions each month. High-risk patterns were getting missed. Duplicate vendor payments slipped through. By the time anomalies were caught, the damage was done.

The question: Could we build something that actually worked without the six-figure price tag of enterprise fraud detection platforms?

The Pilot Project Approach

We follow the MIT research-backed principle: most companies don't see ROI on AI because they start too big. The organizations that succeed start with a narrow, measurable pilot project and build from there.

For this client, we scoped a 4-week proof of concept:

Prove the technology works on their real transaction data
Demonstrate measurable value by catching patterns they were missing
Stay within budget using Azure infrastructure they already owned

The entire POC investment: $6,000. Monthly operating costs after deployment: ~$200.

The Solution

We built an AI-powered outlier detection system that runs entirely in the client's Azure environment using:

n8n workflow automation (open source, no licensing costs)
Azure OpenAI for intelligent transaction analysis
PostgreSQL database for historical baseline calculations
Microsoft Graph API for automated alerting

Every morning, the finance team receives a digest email highlighting high-risk transactions with risk scores (1-10) and plain-English explanations.

How it works:

Calculate statistical baselines (category averages, vendor patterns, spending norms)
Identify anomalies (transactions 2-3x typical amounts, unusual merchants, missing documentation)
AI evaluates each anomaly in business context
Generate daily digest with risk scores and recommended actions

The entire workflow runs automatically—no manual intervention required.

Real Results from Historical Testing

When we tested the system on 6 months of historical data, it flagged legitimate high-risk patterns that had been missed during manual review:

High-value luxury purchases - Charges 2-3x category averages with missing documentation
Same-day clustering patterns - Multiple charges to the same high-end retailer within hours, totaling significant amounts
Duplicate payment detection - Identical vendor and amount within the same billing cycle
Unusual discretionary spending - Transactions significantly above historical norms with incomplete business justification
Round-number transactions - Payments in suspiciously even amounts ($5,000, $10,000) requiring verification

Each flagged transaction included actionable intelligence, not just alerts. For example: "This charge is 2.8 times the category average and has incomplete documentation. Recommend: Request itemized receipt and business justification from cardholder."

The system identified patterns across multiple categories that would have taken dozens of hours to find manually—and likely would have been missed entirely.

Quantifying the Return on Investment

Let's look at the actual business value:

Time Savings:

Manual transaction review: ~20 hours/month (finance team reviewing thousands of transactions)
Automated review: ~2 hours/month (reviewing only AI-flagged items)
Time saved: 18 hours/month = $3,600/month (at $200/hour loaded labor cost)

Fraud Detection Value: During the 6-month historical analysis, the system identified approximately $25,000 in questionable transactions that required investigation. Even if only 20% of flagged items represent actual fraud or errors:

Potential fraud/errors caught: ~$5,000 over 6 months
Annualized value: ~$10,000/year

Total Annual Value:

Time savings: $43,200/year
Fraud prevention: $10,000/year (conservative estimate)
Total: ~$53,000/year

Cost:

Implementation: $6,000 (one-time)
Operating costs: $2,400/year ($200/month)
First-year total: $8,400

First-Year ROI: 530%

And this is just the POC with limited scope (credit cards only). Phase II will expand to GL transactions, expense reports, and vendor payments—where the fraud risk and potential savings are significantly higher.

The real value isn't just catching fraud—it's the peace of mind. Finance leaders can now trust that unusual patterns won't slip through the cracks, auditors see documented controls, and the team focuses on high-value work instead of manual data review.

The Magic Is in the Tuning

AI doesn't work out of the box for fraud detection. We spent hours with the client's finance team calibrating risk thresholds to match their business patterns:

Higher sensitivity for luxury goods and entertainment spending
Lower sensitivity for known recurring business expenses
Category-specific thresholds (travel spending patterns differ from office supplies)
Vendor history context (new vendors flagged differently than established relationships)
Transaction clustering detection (multiple charges to same vendor in short timeframes)

This collaborative tuning—combining their business knowledge with our technical expertise—is what transformed a generic AI model into a valuable business tool. Without this calibration, the system would either miss important patterns or flood them with false positives.

Security and Compliance Built In

For financial services firms, data security isn't negotiable:

Deployed in client's Azure tenant - Their data never leaves their environment
No third-party SaaS platforms - Full control over infrastructure
Enterprise authentication - Azure AD integration, MFA support
Audit trails - Comprehensive logging of all system actions
Compliant infrastructure - Built on Azure's SOC 2/SOX-compliant platform

The client's IT team had full admin access from day one. Transparency builds trust.

From POC to Production

After proving value with the POC, the client is moving forward with Phase II:

POC delivered:

Basic infrastructure (~$90/month Azure costs)
2 data sources (credit cards)
Up to 10K transactions/month
Email alerts only
30 days support

Phase II production will include:

Enterprise architecture with auto-scaling and disaster recovery
99.5% uptime SLA
General ledger transaction analysis
Receipt matching with OCR technology
Integration with case management systems
Web portal and Power BI dashboards
24/7 monitoring and managed services

The POC proved the concept. Phase II delivers the enterprise solution.

Cost Comparison

Traditional enterprise fraud detection platforms:

Implementation: $50,000 - $150,000
Annual licensing: $25,000 - $75,000

Our approach:

POC implementation: $6,000
Monthly POC operating costs: ~$200
Phase II implementation: ~$25,000
Monthly managed services: $2,000

Even with Phase II, the total first-year investment is a fraction of traditional platforms—while maintaining full control over infrastructure and data.

Key Lessons for Finance Leaders

1. Start with a Pilot Project

Don't commit to a major platform before proving the technology works with your actual data. A 4-6 week POC will tell you everything you need to know.

2. Prioritize Data Security

If you're in financial services, your data cannot leave your environment. Insist on solutions that deploy in your Azure or AWS tenant, not third-party SaaS platforms.

3. Budget for Tuning, Not Just Technology

The AI model is only part of the solution. Plan for collaborative tuning with your finance team. Generic platforms can't calibrate to your specific business context without expensive professional services.

4. Understand POC vs. Production

A proof of concept proves value—it's not designed for mission-critical production use. Know the difference:

POC: Development-grade infrastructure, limited scale, basic monitoring, 30-day support
Production: Enterprise architecture, high availability, disaster recovery, ongoing optimization

5. AI Needs Continuous Optimization

The model doesn't "learn" on its own. False positive rates drift over time. Plan for ongoing prompt engineering and threshold adjustments—this is why managed services often make sense for production.

Why This Works for Wealth Managers and Family Offices

Financial services firms face unique challenges:

Regulatory compliance - SOX controls, audit trails, change management
Data sensitivity - Client information cannot be exposed to third parties
Limited IT resources - Small teams need low-maintenance solutions
Budget constraints - Not every firm can justify six-figure investments

Our pilot-first approach addresses all of these:

Prove ROI before major investment (4-6 weeks to real results)
Deploy in your environment (your Azure, your control)
Start simple, expand strategically (cards first, then GL, then receipts)
Managed services available (we handle optimization and support)

The Bottom Line

AI-powered fraud detection doesn't require a six-figure budget or multi-year platform commitments. With the right approach—focused scope, modern tools, and collaborative tuning—you can deploy a working solution in weeks, not months.

This client proved it: from concept to working system in 4 weeks, catching fraud patterns they were missing, for under $200/month in operating costs.

The technology is ready. The question is whether your organization is ready to start small and build from there.

Ready to Explore AI for Your Organization?

If your firm is struggling with manual transaction review, fraud detection, or other repetitive financial processes, let's talk about a pilot project.

We'll help you identify the right starting point, design a solution that works with your Azure environment, prove value before major investment, and build a roadmap from pilot to production.

Book a consultation to discuss your specific challenges.

About the Author: Adam Daum is the founder of West Stack, specializing in AI implementation for wealth managers and family offices. He helps financial services firms adopt AI solutions that respect data privacy, integrate with existing workflows, and deliver measurable ROI.