Data Models

Reusable Neo4j data models that power industry use cases across financial services, insurance, and other domains. These standardised models provide the foundation for solving complex business challenges with graph technology.

Overview

Data models are the backbone of effective graph database implementations. The models documented here represent battle-tested patterns that have emerged from real-world industry deployments. Each model is designed to be:

  • Reusable - Can be adapted across multiple use cases and industries

  • Scalable - Tested with enterprise-grade data volumes

  • Standards-compliant - Follows Neo4j best practices and industry conventions

  • Production-ready - Includes constraints, indices, and performance optimisations

These models serve as technical specifications for building robust graph solutions, whether you’re implementing fraud detection, entity resolution, transaction monitoring, or other graph-powered applications.

Available Data Models

Financial Services

Transaction and Account Data Model

Transaction and Account Data Model Diagram

Key Features:

  • Complete customer identity modelling (documents, biometrics, contact information)

  • Transaction flows with movement decomposition for complex payment scenarios

  • Digital access patterns (devices, IP addresses, sessions)

  • Account relationships and counterparty management

  • Comprehensive constraint definitions ensuring data integrity

Supported Use Cases:

  • Account takeover fraud detection

  • Transaction monitoring and AML compliance

  • Entity resolution across customer touchpoints

  • Deposit analysis and money laundering detection

  • Synthetic identity fraud prevention

Using These Models

Each data model includes:

  1. Node and relationship definitions with complete property specifications

  2. Constraint and index recommendations for performance and data quality

  3. Executable demonstration code for testing and validation

  4. Integration guidance for connecting to existing systems

  5. Query patterns for common business scenarios

These models can be implemented directly or adapted to fit your specific data sources and business requirements.