Conclusion Harnessing the potential of graph techniques can highlight underlying data and its relationships, providing critical insights into seemingly unconnected events in a given use case.
In the banking industry, where fraud incurs high costs, financial services firms using graph database techniques have reported millions of dollars in savings due to the increased accuracy when using graph techniques. The strength of this network approach enables stakeholders to pinpoint and address critical areas in the network, broadening the possibilities for graph analytics and other computational applications. To build this capability, substantial investment in infrastructure is required, alongside the development of unique customer identifiers that can be used across various systems. Multiple tools are available today for creating graph databases and graph features, which can be subsequently integrated into machine learning models to increase prediction accuracy.
Authors
Supriya Panigrahi
Sray Agarwal
Ashna Taneja
Consultant, Fractal Dimension
Consultant, Fractal Dimension
Principal Consultant, Fractal Dimension
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