DETECTION OF FINANCIAL INFORMATION MANIPULATION BY AN ENSEMBLE-BASED MECHANISM

Abstract

Complicated financial information manipulation, involving heightened offender knowledge of transactional procedures, can be damaging to the reputa- tions of corporations and the auditors, as well as cause serious turbulence in finan- cial markets. Unfortunately, most incidents of financial information manipulation involve higher level managers who are truly knowledgeable and comprehend the limitations of standard auditing procedures. Thus, there is an urgent need for ad- ditional detection mechanisms to prevent financial information manipulation. To address this problem, the author proposes an ensemble-based mechanism (EM) con- sisting of feature selection and extraction ensemble and extreme learning machine (ELM). The model not only counters the redundancy-removing problem, but also gives direction to auditors who need to allocate limited audit resources to abnor- mal client relationships during the auditing procedure and protect the CPA firms' reputation. The experimental results demonstrate that the model is a promising al- ternative for detecting financial information manipulation, and one that can ensure both the confidence of investors and the stability of financial markets.