Earticle

초록

영어

Using Hidden Markov Model (HMM), this paper detects some fraudulent financial information of a company. The research index MBSPM (Main Business Service Profit Margin) ratio of company is used to test the effectiveness of HMM. Due to the shortage of the fraudulent data, based on the MBSPM index data we generate the artificial fraudulent financial information data by Poisson process and Uniform distribution, that is, we use the Poisson process to simulate the arrival times of the fraudulent financial information and the Uniform distribution to simulate their fraudulent sizes. By embedding the artificial fraudulent financial information data into the non-fraudulent MBSPM data, we form the sample data series. Applying Henderson filter method, we derive the research sample data by getting rid of the sample data series trends. We do some experiments to test the validity of HMM for detecting the fraudulent financial information, and further make some comparisons with other detecting techniques: logistic regression and ANNs. The detected results show that the HMM approach can significantly improve the identification accuracy.

목차

Abstract
 1. Introduction
 2. HMM Reviews
 3. Detecting Method using HMM
  3.1. Selection of Samples
  3.2. Data Collection and Preprocessing
  3.3. HMM Training
  3.4. Likelihood Update and Fraud Detection
 4. Numerical Experiments
  4.1. Data Preprocessing Numerical Analysis
  4.2. Numerical Analysis
  4.3. Comparison Analysis
 5. Conclusions
 Acknowledgements
 References

키워드

저자

• Ruicheng Yang [ Inner Mongolia University of Finance and Economics, Hohhot, 010051, China ]
• Ailing Zuo [ Inner Mongolia University of Finance and Economics, Hohhot, 010051, China ]
• Qing Shen [ Inner Mongolia University of Finance and Economics, Hohhot, 010051, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605