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Integration and Development of Enterprise Internal Audit and Big Data Based on Data Mining Technology.

¹School of Accountancy, Xijing University, Xi'an 710123, Shaanxi, China.

Computational Intelligence and Neuroscience

|May 2, 2022

Summary

Big data auditing enhances internal audit resource perfection by 17.4%. An improved data mining algorithm boosts accuracy by 31.4% and clustering by 20.7% for better enterprise audits.

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Area of Science:

Accounting and Information Systems
Data Science and Analytics

Background:

The evolution of information technology and digitized business operations necessitates a shift from traditional paper-based auditing.
Big data presents both challenges and opportunities for modern auditing practices, driving the need for advanced analytical techniques.

Purpose of the Study:

To investigate the integration of enterprise internal audit with big data analytics.
To develop and evaluate a novel big data auditing system utilizing data mining techniques.

Main Methods:

Development of a big data auditing system.
Improvement and optimization of a clustering algorithm within data mining.
Experimental design and performance analysis of the enhanced system and algorithm.

Main Results:

The enhanced big data audit system improved internal audit resource perfection by 17.4%.
The optimized clustering algorithm demonstrated a 31.4% increase in accuracy.
The improved algorithm showed a 20.7% enhancement in clustering ability.

Conclusions:

The proposed big data audit system, powered by an optimized data mining algorithm, significantly enhances internal audit capabilities.
The system's improved performance metrics indicate its suitability for application in modern enterprise internal auditing.
This research supports the trend towards big data-driven auditing for future audit development.