Abstract: Financial statement fraud is a deliberate misstatement of material facts by the management in the books of accounts of a company with the aim of deceiving investors and creditors. This illegitimate task performed by management has a severe impact on the economy throughout the world because it significantly dampens the confidence of investors. Financial statements are a company's basic documents to reflect its financial status. A careful reading of the financial statements can indicate whether the company is running smoothly or is in crisis. If the company is in crisis, financial statements can indicate if the most critical thing faced by the company is cash or profit or something else. Financial statements are records of financial flows of a business. Generally, they include balance sheets, income statements, cash flow statements, statements of retained earnings, and some other statements. In a nutshell, the financial statements are the ‘mirrors of a company’s financial status. This paper presents implementation of two data mining techniques namely K-Means Clustering Algorithm and Multi-Level Feed Forward Network (MLFF). Performance of both the techniques is analysed and results are presented comprehensively.
Keywords: K-Means clustering, Multi-Level Feed Forward Network, Probabilistic Neural Network, Data pre-processing.