Abstract
This study delves into the transformative potential of Artificial Intelligence techniques in the auditing landscape. The primary objective is to bolster audit efficiency and outcomes by
harnessing the power of the Contracts Analytics Framework. This framework offers a systematic approach to contract analysis, providing insights for risk identification, control assessment, and actionable recommendations to enhance audit processes.
To accomplish our objectives, we focus on the Document Management functional area of the Contracts Analytics Framework. To this end, we leverage on Natural Language Processing
(NLP) techniques, specifically text classification and summarization. These techniques empower financial statement preparers and auditors with rapid access to critical contract information, enabling efficient compliance evaluation and risk mitigation and the achievement of considerable time savings.
Empirical evaluation and data-driven insights substantiate the potential efficiency gains achievable in auditing practices facilitated by using a cutting-edge AI-driven document management commercial tool that significantly expedites document analysis, enhances classification precision, and enables rapid summarization of vital contract details.
The research focused solely on the Contracts Analytics Framework (CAF) without examining
other auditing frameworks, aligning with the study's specific objectives and limitations. Due to
the scope of the research, only certain components of the CAF were tested and analyzed,
prioritizing the most relevant aspects for the study's aims and objectives. This strategic approach ensured a thorough investigation within the defined parameters while acknowledging
the broader context of auditing frameworks.
We recommend that the tools be used in audit engagements in order to improve classification
and summarization. Additionally, we recommend that a study be explored for the use of AI
tools on other components of the framework to improve audit outcomes holistically.