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Use Case: NFR—Using GraphDB for Impact Graphs

Abstract

Graphs are an outstanding tool to model impact graphs in the context of NFR. The authors show how the different risk categories can be analyzed in a connected view, including risk driver analysis and compact management presentation. The article also shows a practical application of graph databases and graph algorithms.

Keywords

  • GraphDB
  • Impact graph
  • Cypher
  • Graph algorithms
  • NFR

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Notes

  1. 1.

    Environmental, Social, Governance.

  2. 2.

    Operational risks must be covered by capital and are therefore distinguished from non-financial risk. Operational risks are a subset of the non-financial risk.

  3. 3.

    Gray finance is the term used by the United Nations for financing environmentally polluting industries (Carbon.intensive or gray industries).

  4. 4.

    Vulnerabilities have a similar level of importance in the framework.

  5. 5.

    Each node should be assigned to a risk category.

  6. 6.

    Components of the PCP situation and reputation (PCP stands for financial position, cash flows and financial performance).

  7. 7.

    Although approaches to operational risk existed before the consultation paper to Basel II, regulatory demand accelerated the development as an independent risk category.

  8. 8.

    HILFE—High-impact low-frequency events make the estimate extremely challenging, because there is normally no historical data available.

  9. 9.

    There are many ways to assign attributes to nodes and edges.

  10. 10.

    We assume a 10% variance for the loss amount.

  11. 11.

    With high frequency normally comes reliable data, which can be used to build models.

  12. 12.

    This is only possible in a small number of relevant events.

Literature

  • Bajer, Krystyna, Sascha Steltgens, Anne Seidlitz, and Bastian Wormuth. 2021. “Graph Databases.” In The Digital Journey of Banking and Insurance, Volume III—Data Storage, Processing, and Analysis, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

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  • Enzinger, Philipp, and Stefan Grossmann. 2019. “Managing Internal and External Network Complexity.” In The Impact of Digital Transformation and Fintech on the Finance Professional, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

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  • Liermann, Volker, Nikolas Viets, and Davin Radermacher. 2021. “Breaking New Grounds in Non-Financial Risk Management.” In The Digital Journey of Banking and Insurance, Volume I—Disruption and DNA, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

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  • Neo4J. n.d. Neo4J Developer Guide. Accessed December 17, 2020. https://neo4j.com/developer/cypher/guide-sql-to-cypher/.

  • Schmüser, Arne, Farah Skaf, and Harro Dittmar. 2021. “Use Case—NFR—HR Risk.” In The Digital Journey of Banking and Insurance, Volume II—Digitalization and Machine Learning, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

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Correspondence to Volker Liermann .

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Liermann, V., Tieben, M. (2021). Use Case: NFR—Using GraphDB for Impact Graphs. In: Liermann, V., Stegmann, C. (eds) The Digital Journey of Banking and Insurance, Volume II. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-78829-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-78829-2_6

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  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-78828-5

  • Online ISBN: 978-3-030-78829-2

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