In this paper, we propose a novel intelligent methodology to construct a Bankruptcy Prediction Computation Model, which is aimed to execute a company’s financial status analysis accurately. Based on the semantic data analysis and management, our methodology considers the Semantic Database System as the core of the system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and a Semantic Analysis Graph Database

Bolotov, A.

Yerashenia, N.

English

WestminsterResearch

Computational Modelling for Bankruptcy Prediction: Semantic dataAnalysis Integrating Graph Database and Financial OntologyNatalia Yerashenia1 Alexander Bolotov21 University of Westminster, London, United Kingdom, w1578366@my.westminster.ac.uk2 University of Westminster, London, United Kingdom, A.Bolotov@westminster.ac.ukAbstract: In this paper, we propose a novel intelligent methodology to construct a Bankruptcy PredictionComputation Model, which is aimed to execute a company’s financial status analysis accurately. Based on thesemantic data analysis and management, our methodology considers Semantic Database System as the core ofthe system. It comprises three layers: an Ontology of Bankruptcy Prediction, Semantic Search Engine, and aSemantic Analysis Graph Database.1 IntroductionWe propose a concept of an intelligent, analytical system toperform the prediction of the companies’ bankruptcy. Thesystem processes financial information of a company andundertakes a comprehensive investigation of companies’ fi-nancial activities during a particular designated time period.We aim at creating a Bankruptcy Prediction ComputationalModel (BPCM) which is capable of the automated construc-tion of an expert analytical report, where various data andinformation are presented reliably and objectively.The main feature of the proposed system is the consol-idation of the information management with the decision-making process to serve the prediction. This involves mod-ern methods of searching, processing and storing poten-tially large amount of heterogeneous data together with ad-vanced machine learning methods. In this paper, we de-fine the process of the Semantic Database System construc-tion, a novel development, which comprises an Ontologyof Bankruptcy Prediction (OBP), the a Semantic SearchEngine (SDS) and a Semantic Analysis Graph Database(SAGRADA).The remaining of the paper is organised as follows. InSection 2 we describe the problem set-up of the SDS. Sec-tion 3 describes the architecture of the Ontology and GraphDatabase and illustrates their functionality. Finally, in Sec-tion 4 we summarise the contributions of the research pro-vided, discuss future work, and draw conclusions.2 Problem Set-upWe argue that the financial dataset to be analysed for ourpurposes, figuratively speaking, can be characterised byfour ‘V’ and ‘R’. It shares most (four out of five big ‘V’) ofthe qualities of Big Data – Variety, Velocity, Veracity andValue [9] being not dependent on the Volume. However,we underline the fifth, ‘R’, feature of these financial data –an extremely high level of Relationships. Indeed, similarto big data, in our case, we have heterogeneous data, com-ing from different sources. These components of a com-pany’s financial system can be (and usually this is the mostcommon practice) described in the form of relational ta-bles (traditional database), e.g. it is easy to present a bal-ance sheet or income statement in such a way. However,to show the interconnections between all elements of thesetables, it is necessary to create a number of tables of a differ-ent structure containing thousands of objects. In this case,the efficiency of database management and search are sub-stantially affected. For example, it becomes problematicto formulate a general query to several databases, becauseof the difference in objects and attributes of the domain orchanges in objects over time. When the data are inserted,updated or deleted, the integrity constraints for the databasewith changing objects should be checked and assured thatthe data will be consistent after all modifications [7]. Also,there is a problem of the integration of new nodes into thesystem. When adding a new node, it is essential to checkthe data and the data schema for consistency with the infor-mation already available in the system [3].Although traditional relational databases still dominateamong data storage facilities, these systems would not besuitable for the purposes of our financial analysis being un-able to tackle the requirements of the ‘Big Four V + R’.There are NoSQL systems that extend the capabilities of tra-ditional databases by allowing to deal with the four ‘V’.3 Components of Semantic Database SystemDeveloping a Financial Ontology. The ontology presen-tation format defines the mechanisms to store concepts andtheir relationships in the library; it is a method of trans-mitting ontological descriptions to other consumers and amethod of processing its concepts. Specific ontology pre-sentation languages have been developed as ontological de-scription formats (OWL, RDF, KIF) [6].Ontologies are used as data sources for many softwareapplications such as information retrieval, text analysis,knowledge extraction, and other information technologies,allowing more efficient processing of complex and diverseinformation. This way of representing knowledge enablesapplications to recognise those semantic differences that areobvious to people but not known to the computer [2].The main and most crucial component of the financialrisk management of a company is the knowledge base.Our approach to building an ontology describes the basicconcepts of financial analysis, as well as the objects thatserve as sources of knowledge for predicting a company’sbankruptcy. It also contains the concepts and relationshipsrequired for the formation of a hierarchy of knowledgefields and the subsequent use of this hierarchy by variousapplications. In addition, expert rules and regulations canbe described in terms of ontology, which significantly in-creases their level of succinctness and transparency for theusers.The structure and the content of the OBP are based on theexperience of analysts specialising in the theory and prac-tice of bankruptcy prediction [1]. This hierarchy reflectsa number of the most popular indicators used to conducta financial analysis of a company, as well as their origin(documents and concepts to which they relate) and the rela-tionship of these indicators to each other. Financial analyticfactors form the penultimate row of the hierarchy, while theprincipal generalising object is the concept of Company’sFinancial Records. The last row in the hierarchy containslinguistic variables that will be later involved in the devel-opment of machine learning computational modules.The working version of the OBP is an informal concep-tual representation model, which is an initial step of the pro-posed approach.1Developing a Graph Database. Graph DB (for in-stance, Neo4J) are an example of NoSQL databases aimedat representing semantical data [4]. Graph databases areused for storing, processing and automated visualisation ofstandard structural elements. A typical Graph DB usuallycontains some reference information regarding objects [10].Therefore, the user/designer does not have to spend timesearching fo this information in the DB directories. It alsoreduces the number of possible human factor related errors.Graph DB enables to create standard elements automati-cally, which significantly reduces the design time [8].Neo4j2 is an open source Graph Database managementsystem implemented in Java. This Graph DB environmentstores data in a proprietary format specifically adapted forthe presentation of graph information; this approach, incomparison with the modelling of a graph database, usinga relational Databases Management Systems (DBMS), al-lows for additional optimisation in the case of data witha more complex structure. Neo4j uses its own query lan-guage, Cypher3, though the queries can be done in otherways, for example, directly through the Java API. Cypheris not only a query language but also a data manipulationlanguage, as it provides CRUD functions for graph storage.We emphasise that the OBP structure is an excellent basisfor the Semantic Analysis Graph Database which is used asa repository of the financial data for BPCM model. So, weintend to apply an existing solution of creating and man-1At the moment, our work concerns with supplementing the structureof this ontology, as well as with the development of its formal physicalrepresentation model utilising the OWL/RDF environment.2https://neo4j.com/product/3https://neo4j.com/developer/cypher/aging Graph Databases and integrate it into our novel ap-proach.We have implemented a prototype Graph DB,SAGRADA, in Neo4j. The basic concepts in a GraphDB are nodes (an object of the database), relations (graphedges) and their properties. In our case, the nodes of thegraph are financial ratios, financial indicators, and thedocuments containing them. Our graphical repository has29 nodes divided into three categories – Ratio, Criteria(financial indicator), Statement, and 52 relationshipsbetween them (of two types – direct and inverse).4 ConclusionsBased on the analysis of various modern approaches to theprocessing and storage of the heterogeneous data relatedto the financial analysis, we proposed a novel intelligentmethodology to construct a Bankruptcy Prediction Compu-tational Model. Our methodology is based upon the util-isation and integration of the semantic data managementmethods. Following this methodology, we have introduceda novel layered architecture for this Computational Model,which integrates the Semantic Database System and a setof modern machine learning algorithms. We have imple-mented the principles of the new Ontology of BankruptcyPrediction and the Semantic Analysis Graph Database onthe example of a company financial record.Further, we will improve the structure of the OBP Ontol-ogy creating its formal conceptual representation throughOWL / RDF languages. We will also work on further en-hancement of the SAGRADA itself. We will also tackle aproblem of the data exchange between the structural partsof the SDS finding a way to transfer data in various direc-tions automatically.References[1] Jodi L Bellovary, Don E Giacomino, and Michael DAkers. A review of bankruptcy prediction studies:1930 to present. Journal of Financial education,pages 1–42, 2007.[2] Helena Dudycz and Jerzy Korczak. Conceptual designof financial ontology. In 2015 Federated Conferenceon Computer Science and Information Systems (Fed-CSIS), pages 1505–1511. IEEE, 2015.[3] Michel Gagnon. Ontology-based integration of datasources. In 2007 10th International Conference onInformation Fusion, pages 1–8. IEEE, 2007.[4] Venkat N Gudivada, Dhana Rao, and Vijay V Ragha-van. Nosql systems for big data management. In2014 IEEE World congress on services, pages 190–197. IEEE, 2014.[5] Xiufeng Liu, Nadeem Iftikhar, and Xike Xie. Sur-vey of real-time processing systems for big data. InProceedings of the 18th International Database Engi-neering & Applications Symposium, pages 356–361.ACM, 2014.[6] Xiufeng Liu, Nadeem Iftikhar, and Xike Xie. Sur-vey of real-time processing systems for big data. InProceedings of the 18th International Database Engi-neering & Applications Symposium, pages 356–361.ACM, 2014.[7] Ghonia Pooja Mansukhlal and C Malathy. Semanticintegration with ontology based approach. In 2016International Conference on Electrical, Electronics,and Optimization Techniques (ICEEOT), pages 2257–2260. IEEE, 2016.[8] Jaroslav Pokorny`. Graph databases: their powerand limitations. In IFIP International Conference onComputer Information Systems and Industrial Man-agement, pages 58–69. Springer, 2015.[9] Xizhao Wang and Yulin He. Learning from uncer-tainty for big data: Future analytical challenges andstrategies. IEEE Systems, Man, and Cybernetics Mag-azine, 2(2):26–31, 2016.[10] Zuopeng Justin Zhang. Graph databases for knowl-edge management. IT Professional, 19(6):26–32,2017.

Creating an Intelligent System for Bankruptcy Detection: Semantic data Analysis Integrating Graph Database and Financial Ontology

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Creating an Intelligent System for Bankruptcy Detection: Semantic data Analysis Integrating Graph Database and Financial Ontology

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