A hybrid knowledge base system for fraud detection using accounting data

Ou Liu; Duanning Zhou

A hybrid knowledge base system for fraud detection using accounting data

Ou Liu, Duanning Zhou

Operations & Information Management

Research output: Unpublished contribution to conference › Unpublished Conference Paper › peer-review

Abstract

Fraudulence is one of the most popular threats to a company. It is important for a company to detect frauds accurately as soon as possible, so as to protect investors' and customers' interests, and assure the company's revenue. Accounting information systems contain large volume of data that can be analyzed and provide clues for fraud detection. Data mining can be used to analyze users' behavior and patterns, and gives hints to managers about potential frauds. This paper proposes a hybrid knowledge base system for fraud detection, in which ant colony optimization and artificial neural networks are used and combined with experts' knowledge to improve the performance of fraud detection. A system prototype is designed and tested, which illustrates the effectiveness of the approach.

Original language	English
Publication status	Published - 1 Jan 2014
Event	20th Americas Conference on Information Systems, AMCIS 2014 - Savannah, GA, United States Duration: 7 Aug 2014 → 9 Aug 2014

Conference

Conference	20th Americas Conference on Information Systems, AMCIS 2014
Country/Territory	United States
City	Savannah, GA
Period	7/08/14 → 9/08/14

Keywords

Accounting information systems
Ant colony optimization
Fraud detection
Knowledge base system

Cite this

@conference{13449b7295bf4459a44bd1a56485fc54,

title = "A hybrid knowledge base system for fraud detection using accounting data",

abstract = "Fraudulence is one of the most popular threats to a company. It is important for a company to detect frauds accurately as soon as possible, so as to protect investors' and customers' interests, and assure the company's revenue. Accounting information systems contain large volume of data that can be analyzed and provide clues for fraud detection. Data mining can be used to analyze users' behavior and patterns, and gives hints to managers about potential frauds. This paper proposes a hybrid knowledge base system for fraud detection, in which ant colony optimization and artificial neural networks are used and combined with experts' knowledge to improve the performance of fraud detection. A system prototype is designed and tested, which illustrates the effectiveness of the approach.",

keywords = "Accounting information systems, Ant colony optimization, Fraud detection, Knowledge base system",

author = "Ou Liu and Duanning Zhou",

year = "2014",

month = jan,

day = "1",

language = "English",

note = "20th Americas Conference on Information Systems, AMCIS 2014 ; Conference date: 07-08-2014 Through 09-08-2014",

}

TY - CONF

T1 - A hybrid knowledge base system for fraud detection using accounting data

AU - Liu, Ou

AU - Zhou, Duanning

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Fraudulence is one of the most popular threats to a company. It is important for a company to detect frauds accurately as soon as possible, so as to protect investors' and customers' interests, and assure the company's revenue. Accounting information systems contain large volume of data that can be analyzed and provide clues for fraud detection. Data mining can be used to analyze users' behavior and patterns, and gives hints to managers about potential frauds. This paper proposes a hybrid knowledge base system for fraud detection, in which ant colony optimization and artificial neural networks are used and combined with experts' knowledge to improve the performance of fraud detection. A system prototype is designed and tested, which illustrates the effectiveness of the approach.

AB - Fraudulence is one of the most popular threats to a company. It is important for a company to detect frauds accurately as soon as possible, so as to protect investors' and customers' interests, and assure the company's revenue. Accounting information systems contain large volume of data that can be analyzed and provide clues for fraud detection. Data mining can be used to analyze users' behavior and patterns, and gives hints to managers about potential frauds. This paper proposes a hybrid knowledge base system for fraud detection, in which ant colony optimization and artificial neural networks are used and combined with experts' knowledge to improve the performance of fraud detection. A system prototype is designed and tested, which illustrates the effectiveness of the approach.

KW - Accounting information systems

KW - Ant colony optimization

KW - Fraud detection

KW - Knowledge base system

UR - http://www.scopus.com/inward/record.url?scp=84905970032&partnerID=8YFLogxK

M3 - Unpublished Conference Paper

AN - SCOPUS:84905970032

T2 - 20th Americas Conference on Information Systems, AMCIS 2014

Y2 - 7 August 2014 through 9 August 2014

ER -

A hybrid knowledge base system for fraud detection using accounting data

Abstract

Conference

Keywords

Other files and links

Fingerprint

Cite this