Semantic Data Pre-Processing for Machine Learning Based Bankruptcy Prediction Computational Model

This paper studies a Bankruptcy Prediction Computational Model (BPCM model) -a comprehensive methodology of evaluating a company's bankruptcy level, which combines storing, structuring and pre-processing of raw financial data using semantic methods with machine learning analysis techniques. Raw financial data are interconnected, diverse, often potentially inconsistent, and open to duplication. The main goal of our research is to develop data pre-processing techniques where ontologies play a central role. We show how ontologies are used to extract and integrate information from different sources, prepare data for further processing, and enable communication in natural language. Our Ontology of Bankruptcy Prediction (OBP Ontology) which provides a conceptual framework for a company's financial analysis, is built in the widely established Protégé environment. An OBP Ontology can be effectively described with a Graph database (DB). A Graph DB expands the capabilities of traditional databases by tackling the interconnected nature of economic data and providing graph-based structures to store information, allowing the effective selection of the most relevant input features for the machine learning algorithm. To create and manage the BPCM Graph DB, we use the Neo4j environment and Neo4j query language, Cypher, to perform feature selection of the structured data. Selected key features are used for the supervised Neural Network with a Sigmoid activation function. The programming of this component is performed in Python. We illustrate the approach and advantages of semantic data pre-processing, applying it to a representative use case.