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Predictive model building for driver-based budgeting using machine learning

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  • Kunnathuvalappil Hariharan, Naveen

Abstract

Budgeting in the traditional sense is simply too slow and rigid to keep pace with the swiftly changing business environment. At the moment, there is far too much volatility, complexity, and uncertainty. A driver-based planning and budgeting model is more data-driven than a traditional budget model. This budgeting strategy shortens the time it takes to create a budget. Most driver-based planning and budgeting models center on predictions. One of the most difficult aspects of using driver-based planning, however, is identifying appropriate business drivers and predicting the impact of these drivers. Machine learning can assist driver-based budgeting processes in identifying the key drivers and predicting the impacts of these drivers. This study discusses the building of predictive modeling using machine learning. It illustrates stages from quantifying the budgeting issues to determining the best predictive mode for driverbased budgeting.

Suggested Citation

  • Kunnathuvalappil Hariharan, Naveen, 2017. "Predictive model building for driver-based budgeting using machine learning," MPRA Paper 109516, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:109516
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    File URL: https://mpra.ub.uni-muenchen.de/109516/8/MPRA_paper_109516.pdf
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    References listed on IDEAS

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    1. Canhoto, Ana Isabel & Clear, Fintan, 2020. "Artificial intelligence and machine learning as business tools: A framework for diagnosing value destruction potential," Business Horizons, Elsevier, vol. 63(2), pages 183-193.
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    More about this item

    Keywords

    Driver-based budgeting; Machine learning; Model construction; Modelvalidation; Predictive model;
    All these keywords.

    JEL classification:

    • G00 - Financial Economics - - General - - - General
    • G3 - Financial Economics - - Corporate Finance and Governance

    Statistics

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