Expense Discrepancy Detector for Company X: An Excel Macro- Based Tool for Identifying Excess Billing in Energy Invoices
Borisova, Anastasia (2023)
2023
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2023113033098
https://urn.fi/URN:NBN:fi:amk-2023113033098
Tiivistelmä
This Bachelor’s thesis examines the development of an innovative Excel macro-based tool designed to detect billing anomalies in energy invoices for Company X. The primary objective was to enhance the accuracy and efficiency of monitoring energy expenditures, particularly in gas and electricity billing. Improving Company X's financial processes and finding any possible billing errors were the motivating factors behind this investigation.
The thesis is made up of a theory section and an empirical section that deals with the case company. The theory section explains how to critically evaluate energy billing and anomaly detection based on literature and academic sources, as well as providing a foundation for applying statistical and analytical techniques in Excel and VBA. Emphasis is placed on the strategic importance of accurate energy expense management and the role of systematic invoice analysis in corporate finance. The empirical phase combined data analysis and tool development for Company X. First, energy invoices between January and September 2023 were collected into a structured Excel data framework tool. Second, the tool was developed to identify anomalies in energy billing by highlighting irregularities in the data by analyzing historical patterns. Third, to assess the effectiveness of the script, its results were compared against a manual analysis of the same data sets. This comparative analysis helped in determining the tool’s accuracy and reliability in identifying billing anomalies. The effectiveness of the automated analysis was thus validated through this direct comparison with traditional methods.
The findings of the thesis revealed several types of anomalies in energy invoices, which were subsequently categorized. The tool underwent a series of tests to evaluate its function as a support tool for corporate financial monitoring operations. The results suggested that the tool could assist automate operations as well as improve the accuracy of energy billing evaluations.
The study concludes with the tool’s significant implications for Company X, emphasizing its role in facilitating more informed decision-making and operational efficiency. Therefore, future recommendations include the integration of artificial intelligence for enhanced automation and adaptation of the tool for various billing formats and organizational needs.
The thesis is made up of a theory section and an empirical section that deals with the case company. The theory section explains how to critically evaluate energy billing and anomaly detection based on literature and academic sources, as well as providing a foundation for applying statistical and analytical techniques in Excel and VBA. Emphasis is placed on the strategic importance of accurate energy expense management and the role of systematic invoice analysis in corporate finance. The empirical phase combined data analysis and tool development for Company X. First, energy invoices between January and September 2023 were collected into a structured Excel data framework tool. Second, the tool was developed to identify anomalies in energy billing by highlighting irregularities in the data by analyzing historical patterns. Third, to assess the effectiveness of the script, its results were compared against a manual analysis of the same data sets. This comparative analysis helped in determining the tool’s accuracy and reliability in identifying billing anomalies. The effectiveness of the automated analysis was thus validated through this direct comparison with traditional methods.
The findings of the thesis revealed several types of anomalies in energy invoices, which were subsequently categorized. The tool underwent a series of tests to evaluate its function as a support tool for corporate financial monitoring operations. The results suggested that the tool could assist automate operations as well as improve the accuracy of energy billing evaluations.
The study concludes with the tool’s significant implications for Company X, emphasizing its role in facilitating more informed decision-making and operational efficiency. Therefore, future recommendations include the integration of artificial intelligence for enhanced automation and adaptation of the tool for various billing formats and organizational needs.