About This Architecture

Entity-relationship model for an AI-powered banking system integrating fraud detection with core transactional data. Customer entities own Accounts, which perform Transactions analyzed in real-time by an AI_Engine that generates Fraud_Alerts with risk scoring. This architecture demonstrates how machine learning models integrate directly into relational database schemas for financial institutions requiring sub-second fraud detection. Fork this ER diagram on Diagrams.so to customize entity attributes, add compliance audit tables, or adapt the AI_Engine schema for your fraud detection pipeline.

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How do I design a database schema that integrates AI fraud detection with banking transactions?

Use an ER model where Transaction entities are analyzed by an AI_Engine that generates Fraud_Alerts with risk levels. This diagram shows the foreign key relationships between Customer, Account, Transaction, AI_Engine, and Fraud_Alert tables for real-time fraud scoring.