A Review of Financial Accounting Fraud Detection based on Data Mining Techniques

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.

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Performance Analysis and Prediction Student Performance to build effective student Using Data Mining Techniques

UHD Journal of Science and Technology ◽

10.21928/uhdjst.v3n2y2019.pp10-15 ◽

2019 ◽

Vol 3 (2) ◽

pp. 10

Author(s):

Ardalan Husin Awlla

Keyword(s):

Data Mining ◽

Performance Analysis ◽

Student Performance ◽

Fraud Detection ◽

Classification Model ◽

Data Mining Techniques ◽

Incipient Stage ◽

The Everyday ◽

Using Data

In this period of computerization, schooling has additionally remodeled itself and is not restrained to old lecture technique. The everyday quest is on to discover better approaches to make it more successful and productive for students. These days, masses of data are gathered in educational databases, however it stays unutilized. To be able to get required advantages from such major information, effective tools are required. Data mining is a developing capable tool for examination and expectation. It is effectively applied in the field of fraud detection, marketing, promoting, forecast and loan assessment. However, it is in incipient stage in the area of education. In this paper, data mining techniques have been applied to construct a classification model to predict the performance of students.

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Survey of Data-mining Techniques used in Fraud Detection and Prevention

Information Technology Journal ◽

10.3923/itj.2011.710.716 ◽

2011 ◽

Vol 10 (4) ◽

pp. 710-716 ◽

Cited By ~ 8

Author(s):

Sheela Thiruvadi ◽

Sandip C. Patel

Keyword(s):

Data Mining ◽

Fraud Detection ◽

Data Mining Techniques

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Researches of Detection of Fraudulent Financial Statements Based on Data Mining

Journal of Computational and Theoretical Nanoscience ◽

10.1166/jctn.2017.6119 ◽

2017 ◽

Vol 14 (1) ◽

pp. 32-36

Author(s):

Dan Han

Keyword(s):

Data Mining ◽

Academic Research ◽

Financial Statements ◽

Bayesian Belief Networks ◽

Financial Accounting ◽

Accounting Fraud ◽

Financial Statement Fraud ◽

Business World ◽

Modern Business ◽

Fraudulent Financial Statements

Financial statement fraud has been one of the biggest challenges in the modern business world. Financial accounting fraud detection (FAFD) has become an emerging topic of great importance for academic, research and industries. In this paper, the effectiveness of Data Mining (DM) classification techniques in detecting firms that issue fraudulent financial statements (FFS) and deals with the identification of factors associated to FFS are explored. Our study investigates the usefulness of Data Mining techniques including Decision Trees, Neural Networks and Bayesian Belief Networks in the identification of fraudulent financial statements. At last, we compare the three models in terms of their performances.

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