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| Format: | Recurso digital |
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Zenodo
2025
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| Online Access: | https://doi.org/10.5281/zenodo.16936221 |
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Table of Contents:
- <p><span>Graph databases represent a transformative technology in the financial sector, offering a relationship-centric architecture that naturally aligns with the complex interconnections inherent in financial systems. This technology enables institutions to model intricate networks of accounts, transactions, and entities through nodes, edges, and properties, providing powerful capabilities for fraud detection, anti-money laundering, risk management, and customer insights. By prioritizing relationships as first-class elements of the data model, graph databases overcome the limitations of traditional relational systems when analyzing multi-hop connections. The implementation of graph-based approaches enhances financial crime prevention through network analysis, strengthens risk assessment through more accurate modeling of systemic interdependencies, and improves customer intelligence through relationship-based segmentation and influence analysis. Despite implementation challenges, including data integration complexity and privacy considerations, graph databases continue to evolve as essential components of modern financial technology platforms, offering unprecedented capabilities for extracting value from the interconnected nature of financial ecosystems.</span></p>