Название: Machine Learning Applications for Accounting Disclosure and Fraud DetectionАвтор: Stylianos Papadakis, Alexandros Garefalakis
Издательство: IGI Global
Год: 2021
Страниц: 291
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
The prediction of the valuation of the quality of firm accounting disclosure is an emerging economic problem that has not been adequately analyzed in the relevant economic literature. While there are a plethora of Machine Learning (ML) methods and algorithms that have been implemented in recent years in the field of economics that aim at creating predictive models for detecting business failure, only a small amount of literature is provided towards the prediction of the actual financial performance of the business activity.
Data mining technique helps us to extract useful data from a large dataset of any raw data. It is used to analyse and identify data patterns and to find anomalies and correlations within dataset to predict outcomes. Using a broad range of techniques, we can use this information to improve customer relationships and reduce risks. Data mining and supervised learning have applications in multiple fields of science and research. Machine Learning looks at patterns of data and helps to predict future behaviour by learning from the patterns. Data mining is normally used as a source of information on which machine learning can be applied to solve some of problems in our daily life. Supervised learning is one type of machine learning method which uses labelled data consisting of input along with the label of inputs and generates one learned model (or classifier for classification type work) which can be used to label unknown data. Financial accounting fraud detection has become an emerging topic in the field of academic, research and industries.
Machine Learning Applications for Accounting Disclosure and Fraud Detection is a crucial reference work that uses Machine Learning techniques in accounting disclosure and identifies methodological aspects revealing the deployment of fraudulent behavior and fraud detection in the corporate environment. The book applies Machine Learning models to identify quality characteristics in corporate accounting disclosure, proposing specific tools for detecting core business fraud characteristics. Covering topics that include data mining; fraud governance, detection, and prevention; and internal auditing, this book is essential for accountants, auditors, managers, fraud detection experts, forensic accountants, financial accountants, IT specialists, corporate finance experts, business analysts, academicians, researchers, and students.
Скачать Machine Learning Applications for Accounting Disclosure and Fraud Detection