Skip to main content

Dynamic Dashboards

  • 485 Accesses

Abstract

Dynamic dashboards can help to bring transparency into complex model outcomes. The authors present and discuss different alternatives (both open-source and standard software) and show implementation examples. The authors highlight the importance of this toolset in the continuously changing world.

Keywords

  • Dynamic dashboarding
  • Use cases dynamic dashboarding
  • Microsoft—Power BI
  • SAP—SAC
  • MicroStrategy
  • Plotly
  • R Shiny

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-78829-2_9
  • Chapter length: 26 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-78829-2
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1

(© ifb SE)

Fig. 2

(© ifb SE)

Fig. 3

(© ifb SE)

Fig. 4

(© ifb SE)

Fig. 5

(© ifb SE)

Fig. 6

(© ifb SE)

Fig. 7

(© ifb SE)

Fig. 8

(© ifb SE)

Fig. 9

(© ifb SE)

Fig. 10

(© ifb SE)

Fig. 11

(© ifb SE)

Fig. 12

(© ifb SE)

Fig. 13

(© ifb SE)

Fig. 14

(© ifb SE)

Fig. 15

(© ifb SE)

Fig. 16

(© ifb SE)

Fig. 17

(© ifb SE)

Fig. 18

(© ifb SE)

Fig. 19

(© ifb SE)

Fig. 20

(© ifb SE)

Fig. 21

(© ifb SE)

Notes

  1. 1.

    For projection of the risk situation see (Liermann and Viets, Predictive Risk Management 2019).

  2. 2.

    The state in which an enterprise positions itself regarding environmental, social and governance aspects (ESG).

  3. 3.

    D3 “Data-Driven Documents”, see (Bostock et al. 2011) or (Bostock 2019).

  4. 4.

    Which could even include AR (augmented reality) and VR (virtual reality).

  5. 5.

    The transformation process often includes manual steps.

  6. 6.

    Often used in planning processes, if the institute is not using value-driver-oriented planning (see Valjanow et al. 2019).

  7. 7.

    Auto-Regressive Integrated Moving Average (for an introduction see Shumway and Stoffer (2017) or Box et al. (2016).

  8. 8.

    Recurrent neural network (for an introduction see Sect. 3.2 in [Liermann et al., Deep Learning—An Introduction 2019]).

  9. 9.

    VUCA is an acronym which stands for “volatility”, “uncertainty”, “complexity” and “ambiguity”. The term was first used by the U.S. military—United States Army War College (USAWC) see U.S. Army Heritage and Education Center (2019).

  10. 10.

    Sometimes, the adaptation leads to the simultaneous changing of a combination of parameters.

  11. 11.

    Like in the historical simulation in the Value at Risk.

  12. 12.

    Profit and loss.

  13. 13.

    Key risk indicators.

  14. 14.

    If you have tried out a dynamic dashboard, you will agree that moving a slider is a different experience to just entering figures. It has a haptic component.

  15. 15.

    Inverse stress tests (or reverse stress tests) first became popular after the financial crisis in 2008/09 and have found their way into the standard risk management toolset (for details see Liermann and Klauck, Verbessertes Risk Management: Banken im Stresstest 2009; Grundke 2011).

  16. 16.

    The use case can be extended to asset managers for banks and insurers.

  17. 17.

    The lifetime expected loss is the (present) value of the potential credit loss over the full term of the asset.

  18. 18.

    In the US Gaap with CECL (Current Expected Credit Losses) 2016 improvements, the P&L becomes even more dynamic, because (to simplify a bit) CELC is like IFRS 9 just without a stage 1 (the lifetime expected loss has to the calculated for all assets).

  19. 19.

    GDP—Gross domestic product.

  20. 20.

    The Financial Navigator (see Thiele 2021) is an example of this projection of the risk situation. More details of the idea of projecting the risk situation (in the context of financial risk) can be found in (Liermann and Viets, Predictive Risk Management 2019).

  21. 21.

    TypeScript is a strict syntactical superset of JavaScript and is a proprietary programming language of Microsoft (see Microsoft 2020).

  22. 22.

    D3.js can produce dynamic, interactive data visualizations in web browsers. It is written in JavaScript (for details see Bostock 2019).

  23. 23.

    The business requirements of an intraday stress test can be found in Sect. 2 in (Liermann et al., Intraday Liquidity—Forecast Using Pattern Recognition 2019).

  24. 24.

    plotly.py is an open-source library for Python and can be used in a browser (see Plotly, Inc. 2019).

  25. 25.

    Dash is a Python framework designed for developing analytic web applications. There is an open-source option, which can be executed on a local computer (see Plotly, Inc. 2020). Plotly Inc. offers a fee-based server version.

  26. 26.

    A free and open-source web framework, based on Python. It follows the model-template-views (MTV) paradigm (see Django Software Foundation 2020).

  27. 27.

    For D3.js see Bostock (2019).

  28. 28.

    Flask is a web framework written in Python by Armin Ronacher, see.

  29. 29.

    React is a JavaScript software library providing a framework for user interface components (see Facebook Inc. 2020). React was developed by Facebook and is also used by Instagram. It is available under the MIT open-source license.

  30. 30.

    KPISeeAlsoSeeAlsoKey performance indicator—Key Performance Indicator.

  31. 31.

    KRI—Key Risk Indicator.

Literature

  • Bostock, Mike. 2019. D3.js—Data-Driven Documents. Accessed August 18, 2020. https://d3js.org/.

  • Bostock, M., V. Ogievetsky, and J. Heer. 2011. “D3 Data-Driven Documents.” IEEE Transactions on Visualization and Computer Graphics 17 (12): 2301–2309. https://doi.org/10.1109/TVCG.2011.185.

    CrossRef  Google Scholar 

  • Box, George E. P., Gwilym M. Jenkins, Gregory C. Reinsel, and Greta M. Ljung. 2016. Time Series Analysis: Forecasting and Control. Hoboken, NJ: Wiley.

    Google Scholar 

  • Django Software Foundation. 2020. Django. Accessed December 15, 2020. https://www.djangoproject.com/.

  • Facebook Inc. 2020. React. Accessed December 15, 2020. https://reactjs.org/.

  • Grundke, Peter. 2011. “Reverse Stress Tests with Bottom-Up Approaches.” Journal of Risk Model Validation: 71–90.

    Google Scholar 

  • IASB. 2014. “IFRS 9.” IFRS. Accessed September 24, 2020. https://www.ifrs.org/issued-standards/list-of-standards/ifrs-9-financial-instruments/.

  • Kahneman, Daniel. 2011. Thinking, Fast and Slow. New York: Farrar, Straus and Giroux.

    Google Scholar 

  • Liermann, Volker, and Kai-Oliver Klauck. 2009. “Verbessertes Risk Management: Banken im Stresstest.” Die Bank: 52–55.

    Google Scholar 

  • Liermann, Volker, and Nikolas Viets. 2019. “Predictive Risk Management.” In The Impact of Digital Transformation and Fintech on the Finance Professional, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

    Google Scholar 

  • Liermann, Volker, Sangmeng Li, and Johannes Waizner. 2021. “Distributed Calculation Credit Portfolio Models.” 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.

    Google Scholar 

  • Liermann, Volker, Sangmeng Li, and Norbert Schaudinnus. 2019. “Deep Learning—An Introduction.” In The Impact of Digital Transformation and Fintech on the Finance Professional, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

    Google Scholar 

  • Liermann, Volker, Sangmeng Li, and Victoria Dobryashkina. 2019. “Intraday Liquidity—Forecast Using Pattern Recognition.” In The Impact of Digital Transformation and Fintech on the Finance Professional, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

    Google Scholar 

  • Microsoft. 2020. “Home for Microsoft Documentation and Learning.” Tutorial: Develop a Power BI Circle Card Visual, Febuary 9. Accessed October 15, 2020. https://docs.microsoft.com/en-us/power-bi/developer/visuals/develop-circle-card.

  • ———. 2020. TypeScript. Accessed October 15, 2020. https://www.typescriptlang.org/.

  • Plotly, Inc. 2019. plotly.py. Accessed October 15, 2020. https://github.com/plotly/plotly.py.

  • ———. 2020. dash. Accessed October 15, 2020. https://github.com/plotly/dash.

  • Shumway, Robert, and David Stoffer. 2017. “ARIMA Models.” In Time Series Analysis and Its Applications, by Robert H. Stoffer and David S. Shumway, 75–163. Springer International Publishing.

    Google Scholar 

  • Thiele, Markus. 2021. “Financial Navigator—A Modern Approach to Analytical Banking.” In The Digital Journey of Banking and Insurance, Volume IDisruption and DNA, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

    Google Scholar 

  • U.S. Army Heritage and Education Center. 2019. “Who First Originated the Term VUCA.” U.S. Army Heritage and Education Center—FAQ, May 7. Accessed October 15, 2020. https://usawc.libanswers.com/faq/84869.

  • Valjanow, Simon, Philipp Enzinger, and Florian Dinges. 2019. “Value-Driver-Oriented Planning—Management-Oriented Design and Value Driver Identification.” In The Impact of Digital Transformation and Fintech on the Finance Professional, edited by Volker Liermann and Claus Stegmann. New York: Palgrave Macmillan.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Volker Liermann .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Liermann, V., Li, S. (2021). Dynamic Dashboards. 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_9

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-78829-2_9

  • Published:

  • Publisher Name: Palgrave Macmillan, Cham

  • Print ISBN: 978-3-030-78828-5

  • Online ISBN: 978-3-030-78829-2

  • eBook Packages: Economics and FinanceEconomics and Finance (R0)