Graph technologies continue to show increased demand globally, albeit focused on specific industries such as finance or retail. Established AI techniques (such as Bayesian networks) are increasing the power of knowledge graphs and the usefulness of graph analytics through further nuance in representational power. Graph databases are ideal for storing, manipulating and analyzing the widely varied perspectives in the graph model due to their graph-specific processing languages and capabilities, scalability and computational power. Graph technologies help insurers to solve problems that would otherwise require manual interrogation and analysis, such as investigating complex fraud networks involving multiple parties, combating money laundering and reserve reporting. The additional scalability and computational power of graph databases also help enable next best action and product recommendations based on life events, and will eventually underpin future business models such as panoptic personalization. However, insurance companies must avoid misuse of customer information, digital creepiness, misselling, and breaking ethics and privacy laws (see Panoptic Personalization: An Insurance Trend for 2022).
Recommended Actions:
Prioritize insurance customer use cases that improve short-term claims processing, shorten SLAs and improve customer satisfaction by focusing on analytical query support for process optimization and straight-through processing. ■ Focus in the long term on applying graph technologies to future insurance business models where customer product and channel interactions feel personalized to a customer’s individual needs, taking into account the relationship with the closest entities. ■
Recommended Reading:
Understanding When Graph Analytics Are Best for Your Business Use Case ■
Tabular Synthetic Data Back to Top
Analysis by: James Ingham, Alys Woodward, Vibha Chitkara, Benjamin Jury
Gartner, Inc. | G00786204
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