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All Journal Journal of Applied Finance & Accounting International Journal of Computing Science and Applied Mathematics-IJCSAM
Kenneth Gunawan
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Economics, Econometrics & Finance Mathematics

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THE EFFECT OF AUDITOR COMPETENCE, PROFESSIONAL SKEPTICISM, RED FLAG, AND INTERNAL CONTROL SYSTEM ON FRAUD DETECTION Kenneth Gunawan; Muhammad Fauzan Riyanal; Bambang Leo Handoko
Journal of Applied Finance & Accounting Vol. 9 No. 2 (2022): Publish on December 2022
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/jafa.v9i2.8972

Abstract

Today more and more cases of fraud occur. In each of these cases, there is always an auditor's failure to detect fraud. It makes the fraud going on for a long time before being discovered. Based on this, we intend to research the factors influencing fraud detection. This study aims to determine the effect of auditor competence, professional skepticism, red flags, and internal control systems on fraud detection. The data collection technique in this study used primary data by distributing questionnaires to auditors working at the Public Accounting Firm (KAP) in the DKI Jakarta area using a purposive sampling method. The data analysis method uses a partial least square model using the SmartPLS 3.3 software. The results of this study indicate that auditor competence, red flag, and internal control system variables significantly affect fraud detection. In contrast, the professional skepticism variable has no significant effect on fraud detection.
A Predictive Framework for Transition ProbabilityShifts Using Environmental Data Via Lee-CarterModel Extension Kenneth Gunawan; Nicholas Andrew Tanubrata; Rex Aaron Holindo; Ferry Vincenttius Ferdinand; Helena Margaretha
(IJCSAM) International Journal of Computing Science and Applied Mathematics Vol. 12 No. 2 (2026)
Publisher : LPPM Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j24775401.ijcsam.v12i2.10834

Abstract

This study addresses the challenge of incorporatingenvironmental data, particularly air pollution, into practicalactuarial models. This study extends the Lee-Carter modelusing environmental variables and employs LASSO regressionto address potential multicollinearity among pollution indicators.The results are closed-form approximations for estimating theexpected changes in transition probabilities from the previoustransition probabilities forecast within both multiple-state andmultiple-decrement models, which are calculated using currentenvironmental data. These approximations enable actuaries toquantify the expected difference in transition probabilities fromthe initial forecast by utilizing the newest environmental data thatwas previously unavailable. The framework is empirically testedusing a case study on a dementia-related multiple-state modelin the UK. Results show significant improvements in predictiveaccuracy, measured by reductions in Mean Absolute PercentageError (MAPE), reducing MAPE by over 40% in several agegroups. These results emphasize the value of environmentalintegration in actuarial modeling, particularly in the context ofhealth-related insurance products
Co-Authors Bambang Leo Handoko Ferry Vincenttius Ferdinand Helena Margaretha Muhammad Fauzan Riyanal Nicholas Andrew Tanubrata Rex Aaron Holindo