Key points are not available for this paper at this time.
This study addresses the auditors need to categorize financial statement lines from an extensive collection of charts of accounts, and to automatize their mapping from natural language to the framework specified by the European Union’s Accounting Directive. Central to the study is the assembly and analysis of an extensive dataset comprising 945 charts of accounts, each representing the financial statements of a unique company with distinct accounting practices. The dataset’s variability reflects the diverse accounting methods employed by these companies.To address document heterogeneity, the research involves the creation and deployment of a specialized algorithm for data extraction and normalization. This algorithm systematically processes the natural language text of the charts of accounts, extracting relevant financial data and normalizing it to a consistent format suitable for analysis. Following normalization, the refined dataset is used to fine-tune OpenAI’s GPT-3 model. This deep learning natural language model is adapted to map natural language statements to the specific codes defined by the European Accounting Directive, ensuring each financial statement item is accurately categorized according to the regulatory framework. The mapping process involves linking trial balance figures to their corresponding financial statement lines, ensuring the ending balances in the trial balance align with the line items in the financial statements. The specialized algorithm enhances this process by automating the extraction and normalization steps, which traditionally require significant manual effort and are prone to human error.The novel application of GPT-3 in this context and the model’s ability to handle complex, hierarchical data structures inherent in financial statements further underscores its potential in transforming traditional auditing practices.
Carollo et al. (Wed,) studied this question.