Munich Personal RePEc Archive

Predictive model building for driver-based budgeting using machine learning

Kunnathuvalappil Hariharan, Naveen (2017): Predictive model building for driver-based budgeting using machine learning. Published in: Journal of Emerging Technologies and Innovative Research (JETIR) , Vol. 4, No. 6 (June 2017): pp. 567-575.

[img]
Preview
PDF
MPRA_paper_109516.pdf

Download (190kB) | Preview

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.

Logo of the University Library LMU Munich
MPRA is a RePEc service hosted by
the University Library LMU Munich in Germany.