Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology

Yerashenia, Natalia; Bolotov, Alexander

doi:10.1109/cbi.2019.00017

Cited by 7 publications

(15 citation statements)

References 45 publications

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“…In finance, such data can come in the form of a knowledge graph, for example, built on the Financial Industry Business Ontology (FIBO) [64]. Graph neural networks have been applied in financial fraud detection tasks [65], [66].…”

Section: Dimensionality Reductionmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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Financial risk management avoids losses and maximizes profits, and hence is vital to most businesses. As the task relies heavily on information-driven decision making, machine learning is a promising source for new methods and technologies. In recent years, we have seen increasing adoption of machine learning methods for various risk management tasks. Machine-learning researchers, however, often struggle to navigate the vast and complex domain knowledge and the fast-evolving literature. This paper fills this gap, by providing a systematic survey of the rapidly growing literature of machine learning research for financial risk management. The contributions of the paper are four-folds: First, we present a taxonomy of financial-risk-management tasks and connect them with relevant machine learning methods. Secondly, we highlight significant publications in the past decade. Thirdly, we identify major challenges being faced by researchers in this area. And finally, we point out emerging trends and promising research directions.

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Section: Dimensionality Reductionmentioning

confidence: 99%

Machine Learning for Financial Risk Management: A Survey

et al. 2020

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“…The visualization techniques are applied on the knowledge graph which helps in providing the results of the different queries related to the poverty alleviation. Bankruptcy Prediction Computational Model (BPCM) is presented in [47], which is used to perform the bankruptcy predictions of the financial institutes or the companies. Ontology of the Bankruptcy Prediction (OBP) is constructed to uniformly extract the data from different data sources and to utilize the financial data of the companies.…”

Section: B Ontology Based Information Extractionmentioning

confidence: 99%

“…The addition of the new node or relations or deletion of the previous node has made no effect in the consistency and the information schema. It has the flexibility to gather, exchange, and update the information from different sources; the new nodes and the extracted information is easily adjustable in the existing format without disturbing the structure of the ontology [47].…”

Section: Introductionmentioning

confidence: 99%

Financial Knowledge Graph Based Financial Report Query System

et al. 2021

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Annual Financial Reports are the core in the Banking Sector to publish its financial statistics. Extracting useful information from these complex and lengthy reports involves manual process to resolve the financial queries, resulting in delays and ambiguity in investment decisions. One of the major reasons is the lack of any standardization in the format and vocabulary used in the reports. An automated system for resolution of intelligent financial queries is therefore difficult to design.Several works have been proposed to overcome these problems using Information Extraction; however, they do not address the semantic interoperability of the reports across different institutions. This work proposed an automated querying engine to answer the financial queries using Ontology based Information Extraction. For Semantic modeling of financial reports, a Financial Knowledge Graph, assisted by Financial Ontology, has been proposed. The nodes are populated with entities, while links are populated with relationships using Information Extraction applied on annual reports. Two benefits have been provided by this system to stakeholders through automation: decision making through queries and generation of custom financial stories. The work can further be extended to other domains including healthcare and academia where physical reports are used for communication.

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“…In this paper, we extend our analysis of the application of machine learning (ML) [1] to the dynamics of business. While our previous research set up the conceptual level, proposing a general computational model for bankruptcy prediction, here we address one of the most relevant aspects of data management -data pre-processing to ensure a more efficient application of ML-based prediction.…”

Section: Introductionmentioning

confidence: 99%

“…We apply an effective 'semantic data' analysis, developing further the 'Semantic Database System' introduced in [1]. Here, ontologies play a central role [6].…”

Section: Introductionmentioning

confidence: 99%

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

Yerashenia

Bolotov

Chan

et al. 2020

2020 IEEE 22nd Conference on Business Informatics (CBI)

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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 preprocessing, applying it to a representative use case.

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Computational Modelling for Bankruptcy Prediction: Semantic Data Analysis Integrating Graph Database and Financial Ontology

Cited by 7 publications

References 45 publications

Machine Learning for Financial Risk Management: A Survey

Machine Learning for Financial Risk Management: A Survey

Financial Knowledge Graph Based Financial Report Query System

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

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