Transparency in financial reporting is crucial for maintaining trust in financial markets, yet
fraudulent financial statements remain challenging to detect and prevent. This study …

With the increasing complexity of financial statement manipulation, relying solely on
quantitative financial data may not effectively detect financial fraud. While textual analysis …

We present a novel approach for analysing the qualitative content of annual reports. Using
natural language processing techniques we determine if sentiment expressed in the text …

For some time, there has been significant disagreement as to whether financial measures
that do not conform to the Generally Accepted Accounting Principles (GAAP) should be used …

Extensive research has been done on the analytical and empirical examination of financial
data in annual reports to detect fraud; however, there is scant research on the analysis of …

Despite legislative attempts to curtail financial statement fraud, it continues unabated. This
study makes a renewed attempt to aid in detecting this misconduct using linguistic analysis …

Corporate fraud can lead to significant financial losses and cause immeasurable damage to
investor confidence and the overall economy. Detection of such frauds is a time-consuming …

Existing researches have always focused on numerical data or textual contents in financial
statements to expose a firm's fraudulent behavior. To the best of our knowledge, this study …

In this study, we employ Natural Language Processing (NLP), a subdomain of artificial
intelligence (AI), to predict the sentiments while analyzing 3729 annual 10-k financial reports …

High profile cases of fraudulent financial reporting such as those that occurred at Enron and
WorldCom have shaken public confidence in the US financial reporting process and have …