Expert Analysis
Six AI fraud detection patterns for fintech that SQL rules miss
May 16, 2026
SQL rules catch known fraud shapes. AI fraud detection catches adaptive rings, account takeovers, and behavioral drift. Six patterns for fintech teams.
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Latest Articles
How to Evaluate an AI Assistant for Genetic Test Results: A Practical Checklist for Genomics Teams
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A no-nonsense checklist for genomics teams evaluating AI assistants for genetic test results. Covers accuracy, safety, privacy, and integration.
Why Genetic Testing Companies Need Conversational AI Now
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Genetic testing companies losing leads because customers cannot understand their results. Conversational AI turns complex genomic reports into conversations.
How to Build an Offshore AI Development Team from India: A Practical Guide for US Companies
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A practical framework for US companies building offshore AI teams in India, covering cost structures, team assembly, and common pitfalls.
Your AI Project Needs a Vector Database. Here is How to Pick the Right One
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A practical framework for choosing vector databases based on your project stage, scale, and infrastructure requirements.
Introduction to Model Context Protocol
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MCP is an open protocol that standardizes how AI applications connect to external tools and data sources.
Gemma 4 E4B Fine-Tuning: Complete Guide for Enterprise Edge AI
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Google released a 4.5B parameter multimodal model that runs on phones. Here is why this changes the calculus for enterprise AI deployments.
How AI Agents Replace Manual Business Processes: 360K Hours Saved at JPMorgan
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AI agents autonomously handle multi-step business tasks, replacing manual handoffs. JPMorgan saved 360K hours yearly. Here is how your enterprise can do the same.
From Fragmented Health Records to Unified AI Queries
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How a genomic testing specialist rebuilt their health data infrastructure with AI-powered document processing to enable natural language queries
Scaling a RAG Pipeline with Qdrant for Production
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Learn how to scale a RAG pipeline with Qdrant for production AI applications, retrieval quality, and reliability.