Automated Cash Forecasting: Practical Insights from CRISP-DM - Streamlining analytics projects using CRISP-DM's six-phase approach
Journal
Strategic Finance
Type
journal article
Date Issued
2025-06
Author(s)
Abstract
In an era of increasing market volatility and the need for rapid adaptation, data analytics presents game-changing opportunities across industries, significantly boosting productivity and decision-making. Yet, finance and management accounting have been slower to embrace advanced analytics compared to fields like marketing or supply chain management. While the potential for transformation is clear, implementation remains complex, often due to a lack of practical guidance on how to approach such projects.
The Cross-Industry Standard Process for Data Mining (CRISP-DM) has become a leading framework for structuring analytics projects. Its adaptability and proven success across industries make it a valuable tool for finance professionals looking to integrate data-driven decision-making. However, while many studies outline theoretical applications of CRISP-DM, this article takes a different approach—showcasing how it plays out in the real world.
Through an in-depth examination of an automotive retailer’s finance function, this article explores the practical application of CRISP-DM in cash forecasting. By walking through each phase of the process, from data understanding to deployment, it highlights key insights, unexpected challenges, and practical lessons that often go unnoticed in abstract frameworks. The goal is to provide finance professionals with a relatable, hands-on perspective on how analytics can enhance forecasting accuracy and efficiency, bridging the gap between theory and implementation.
The Cross-Industry Standard Process for Data Mining (CRISP-DM) has become a leading framework for structuring analytics projects. Its adaptability and proven success across industries make it a valuable tool for finance professionals looking to integrate data-driven decision-making. However, while many studies outline theoretical applications of CRISP-DM, this article takes a different approach—showcasing how it plays out in the real world.
Through an in-depth examination of an automotive retailer’s finance function, this article explores the practical application of CRISP-DM in cash forecasting. By walking through each phase of the process, from data understanding to deployment, it highlights key insights, unexpected challenges, and practical lessons that often go unnoticed in abstract frameworks. The goal is to provide finance professionals with a relatable, hands-on perspective on how analytics can enhance forecasting accuracy and efficiency, bridging the gap between theory and implementation.
Language
English
HSG Classification
contribution to practical use / society
Refereed
No
Volume
107
Subject(s)
Division(s)
File(s)![Thumbnail Image]()
Name
Automated Cash Forecasting_ Practical Insights from CRISP-DM.pdf
Type
Main Article
Size
365.81 KB
Format
Adobe PDF
Checksum (MD5)
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