What lies ahead developing a fraud prediction model: Application of artificial intelligence methods using firm-specific data and locational factors
Date of Publication
2014
Document Type
Bachelor's Thesis
Degree Name
Bachelor of Science in Accountancy
College
Ramon V. Del Rosario College of Business
Department/Unit
Accountancy
Thesis Adviser
Florenz C. Tugas
Defense Panel Member
Aeson Luiz C. Dela Cruz
Alger C. Tang
Alloysius Joshua S. Paril
Abstract/Summary
In light of the constant threat that fraudulent activities pose to various stakeholders, this study sought to develop a forecasting model that could predict the occurrence of fraud in companies based on publicly available financial and locational information, specifically: current ratio, total asset turnover, return on assets, debt to asset ratio, current asset to total asset ratio, corruption perception index (CPI) and gross domestic product (GDP). This study used logistic regression to analyze the independent variables in addition to creating a fraud prediction model, and the findings showed that all the variables, apart from total asset turnover and current asset to total asset ratio, were significant. Among them, current ratio, total asset turnover, debt to asset ratio, and current asset to total asset ratio have a positive effect on the probability of fraud occurrence while the return on assets, CPI, and GDP have a negative effect on fraud occurrence. Marginal effects (mfx) were used to measure the effect of a per unit increase/decrease in the independent variable on the dependent variable. Moreover, two artificial intelligence models using neural network and fuzzy logic were developed and compared to determine which is the most accurate and fit to be used by stakeholders. The results showed much promise, with the most accurate model having an accuracy rate of approximately 74% based on the F-score computed from the confusion matrix.
Abstract Format
html
Language
English
Format
Accession Number
TU18853
Shelf Location
Archives, The Learning Commons, 12F, Henry Sy Sr. Hall
Physical Description
xii, 212 leaves : illustrations (some colored) ; 28 cm. + ; 1 computer optical disc.
Recommended Citation
Apolinar, J. B., Kung, J. B., Ramirez, J. C., & Rebadomia, W. C. (2014). What lies ahead developing a fraud prediction model: Application of artificial intelligence methods using firm-specific data and locational factors. Retrieved from https://animorepository.dlsu.edu.ph/etd_bachelors/12188
