Constructing and training a deep learning model for detecting fraudulent activities and manipulation attempts in financial statements involves several key methodologies, from data collection and preprocessing to model selection, training, and evaluation. The first crucial step is data collection. Financial statement data from a variety of sources, such as publicly available databases like EDGAR, company-specific filings, and proprietary data sets, is gathered. This data includes historical balance sheets, income statements, cash flow statements, as well as notes to the financials, management discussions, and audit reports. It is essential to gather data from both fraudulent and non-fraudulent companies, if possible, to provide a training set with sufficient differentiation. Specifically, for fraudulent examples, there could be a dataset containing information from companies that have been caught with fraudulent statements, but due to the difficulty of obtaining labeled data, datasets are often constructed using simulations, or using statistical properties to label the data as ‘suspicious’ rather than fraudulent. The dataset often requires considerable cleaning as different companies use different formats or have missing data.
Preprocessing is the next step and involves transforming the raw financial data into a format suitable for deep learning models. This may include tasks such as handling missing data using methods such as imputation or simply filling with a default value, and normalizing or standardizing the features to ensure that no single feature dominates the learning process. For instance, features like total assets, revenue, and net income might vary significantly in scale across different companies so a standardization technique is needed, such as z-score scaling. Feature engineering also plays a critical role. Raw data might not be directly indi....
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