• Predictive model building for driverbased budgeting using machine learning

    Author(s):
    Naveen Kunnathuvalappil Hariharan (see profile)
    Date:
    2017
    Subject(s):
    Machine learning, Business writing
    Item Type:
    Article
    Tag(s):
    finance, financial decision-making
    Permanent URL:
    http://dx.doi.org/10.17613/hxcq-ct26
    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 driver-based budgeting.
    Metadata:
    xml
    Status:
    Published
    Last Updated:
    3 months ago
    License:
    Attribution-NonCommercial
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