The applications of exogenous data and emerging technologies in accounting and auditing
Description
TitleThe applications of exogenous data and emerging technologies in accounting and auditing
Date Created2022
Other Date2022-10 (degree)
Extent160 pages : illustrations
DescriptionAdopting new data sources and advanced emerging technologies in the current business environment has significantly transformed business measurement, assurance, and government operations. Newly emerged technologies allow users to collect and analyze nontraditional data sources, conduct audit procedures and business operations toward a more predictive analytical approach, and perform the tasks in an efficient and effective way. Technologies such as artificial intelligence, machine learning, text mining, and process mining have been applied in various business and government operations and audit functions. Utilizing these advanced emerging technologies, users can examine different exogenous data, perform full population testing, conduct advanced data analytics, and enhance the reporting quality. This dissertation consists of three essays that explore the value of exogenous data and emerging technologies in accounting and auditing, including machine learning, text mining, and process mining techniques to improve government reporting and enhance audit quality. The first essay demonstrates a way of bringing an innovative data source, social media information, to the government accounting information systems to support accountability to stakeholders and managerial decision-making. Future accounting and auditing processes will heavily rely on multiple forms of exogenous data. As an example of the techniques that could be used to generate this needed information, the study applies text mining techniques and machine learning algorithms to Twitter data. The information is developed as an alternative performance measure for NYC street cleanliness. It utilizes Naïve Bayes, Random Forest, and XGBoost to classify the tweets, illustrates how to use the sampling method to solve the imbalanced class distribution issue, and uses VADER sentiment to derive the public opinion about street cleanliness. This study also extends the research to another social media platform, Facebook, and finds that the incremental value is different between the two social media platforms. This data can then be linked to government accounting information systems to evaluate costs and provide a better understanding of the efficiency and effectiveness of operations. The second essay conducts a pilot test analyzing reports from New York, New Jersey, and California and uses textual analytics to reengineer government performance audit reporting. It advocates a performance audit database that could facilitate easier access and extract relevant information from lengthy reports in a timely manner. The study presents a framework to identify the commonalities and differences of terminologies used by sampled states, evaluates and extracts relevant content from the reports according to GAGAS requirements, and constructs a taxonomy specific to government performance audits. Furthermore, this study investigates the disclosure quality by examining linguistic and similarity features, such as report length, specificity, readability, comprehensibility, and content similarity. This paper raises attention to a key legislative task that requires reporting reforms. The third essay proposes an internal control evaluation model (PROMIMAL) that incorporates process mining and machine learning to systematically evaluate controls. PROMIMAL consists of four components: process mining analysis, rule-based evaluation, control risk assessment, and anomaly detection. It utilizes process mining to identify the transactions that deviate from standard business process flows, assesses the underlying associated controls with these deviations, and applies machine learning algorithms to identify the high- anomalous transactions for further investigation. The results indicate that the model can comprehensively evaluate controls, identify control weaknesses and missing controls, and direct investigations toward high-risk areas. This study enhances overall audit quality by improving audit risk assessment, control testing, and substantive testing through a practical-oriented approach. These three essays aim to provide insight into utilizing exogenous data and emerging technologies to enhance audit quality and improve government reporting. This dissertation contributes to current innovative and novel research in accounting and auditing and provides illustrations on adopting nontraditional data sources and emerging technologies in various settings.
NotePh.D.
NoteIncludes bibliographical references
Genretheses
LanguageEnglish
CollectionGraduate School - Newark Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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