Fraud Detection System

The Challenge: Small Business Fraud Detection

This portfolio demonstrates how IntelXData helped a small financial services company transform their fraud detection capabilities through machine learning and advanced analytics.

Business Problem

The company experienced escalating fraud losses totaling $492,000 annually, with a legacy rule-based system generating excessive false positives (8.3% rate) that overwhelmed the small 2-person fraud investigation team. With limited resources, the company needed an automated solution that could:

• Accurately identify fraud without requiring manual rule updates
• Reduce false positives to manageable levels for small team
• Process transactions in real-time (<500ms latency)
• Provide clear explanations for flagged transactions
• Operate within small business budget constraints

IntelXData's Solution: We delivered a custom machine learning system that reduced fraud losses by 69%, cut false positives by 67%, and achieved a 4.2-month ROI payback period—all within a small business budget.

This project demonstrates IntelXData's commitment to rapid, high-quality delivery for small business clients:

• Dataset Scope: Analyzed 12 months of transaction activity (800,000 transactions)
• Delivery Timeline: Results ready and demonstrated to decision makers within 4 weeks
• Client Satisfaction: Achieved 4.98/5.0 rating from the company
• Ahead of Schedule: Delivered one week earlier than the agreed deadline

Why This Matters: Small businesses need solutions fast. We delivered production-ready fraud detection with full documentation in under a month—proving that enterprise-grade analytics don't require enterprise timelines.