Probe - Accounting, Auditing and Taxation

Enhancing audit risk analysis through python-based algorithmic innovation in audit data analytics

Wenyue Tan

College of Engineering and Technology, Chengdu University of Technology

DOI: https://doi.org/10.59429/paat.v7i2.10281

Keywords: Audit risk analysis; Audit data analytics; Python-based; Algorithmic innovation

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Abstract

In the context of the digital era, the expansion which has high speed of audit data has introduced both significant challenges and valuable opportunities for audit risk analysis. This paper investigates the potential of Python-driven algorithmic advancements to improve the effectiveness of audit risk analysis within the realm of audit data analytics. Through the analysis of the strengths of Python in the fields that include data processing, visualisation, and machine learning, alongside the introduction of novel algorithms, the purpose of this research is to provide a new perspective and methodology which could intensify the efficiency and accuracy, based on audit risk assessment. The results demonstrate that algorithmic innovations which are based on Python could substantially aid in the identification of latent audit risks, streamline the auditing process, and would likely elevate the overall quality of audit outcomes.

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References

[1]Abergel R , Durand S , Frapart M Y . PyEPRI: A CPU & GPU compatible python package for electron paramagnetic resonance imaging [J]. Journal of Magnetic Resonance, 2025, 376 107891-107891.

[2]Saraceno G , Mukhopadhyay R , Markatou M . QuadratiK: A Python and R package for clustering on the sphere and goodness-of-fit tests [J]. SoftwareX, 2025, 31 102155-102155.

[3]Punnapathiran T , Angsuchotmetee C , Kaewkarndee P , et al. CGNLib: A Python library for Girvan–Newman community detection with customizable node-based centrality metrics [J]. SoftwareX, 2025, 31 102193-102193.

[4]Xia S . Application of Python Automation Tool Combined with OCR Technology in “Financial Checkup” of Universities [J]. Accounting and Corporate Management, 2023, 5 (12).