# A Semantic Model for Enhancing Data-Driven Open Banking Services
###### Abstract
A key challenge in this scenario is the definition and implementation of common data integration schemes, and merging them into knowledge graphs easily, hence allowing data reconciliation and leading to sophisticated analysis, e.g., by cross-checking information between different account statements to verify the coincidence of the company's balances. This can serve as an additional security measure against attempted fraud. In this sense, Semantic Web technologies constitute a suitable framework for the semantic integration of data that enables linking with external sources and enhances systematic querying. The development of new ontologies and their use for data integration is widely documented in the existing literature for different domains of application [3; 4; 5; 6]. In the scope of finance and banking, a series of approaches have been described [2; 7; 8; 9] in which ontologies are designed for describing the main concepts and relationships involving data governance and management activities in this sector. A comprehensive survey of this direction was recently presented in [10]. A concrete example is the well-known Financial Industry Business Ontology (FIBO) [8], which is still evolving since its initial publication in 2014 and has increasing support for use cases related to security master data management, reporting, risk analysis, and management. Nevertheless, although all of these initiatives constitute a starting point for future work, specific practical aspects and standards (such as the PSD2) should be considered to facilitate modern open banking data integration and digital applications. To cope with this issue, an ontology approach called OBO (Open Banking Ontology) is proposed in this work to operate as a semantic data mediator in real-world open banking operations. Following this ontological scheme, a new knowledge graph is generated and populated with data involved in actual PSD2 open banking transactions, which are aligned with information from invoices, according to semantic reconciliation mechanisms. To allow this, text processing techniques and semantic queries are adapted to extract two corpora of words. After this, they are compared with a fuzzy search method to establish a similarity score. This methodology follows similar approaches successfully applied in other fields such as biomedical informatics [11] or the recognition of company names featured in press news [12]. In addition, a series of semantic rules are established to show how financial solvency classification of entities and transaction concept suggestions can be inferred from the proposed semantic model. Concretely, the main contributions of this work can be summarized as follows:  
* An ontology is defined using Web Ontology Language (OWL 2) [13], for the first time for the semantic annotation of bank statement and invoice management operations in the context of PSD2 transactions. The proposal, called Open Banking Ontology (OBO), is used as a semantic integration data scheme for different information sources that enables data ingestion, querying, and reasoning in a structured and homogeneous way. * According to the OBO definition, a semantic model is also developed for knowledge graph generation, which is populated by means of specific mapping functions for each different data source. The resulting graph comprising all the data instances is integrated into a repository using Resource Description Framework (RDF) [14], which allows querying with the same language (SPARQL). A series of reconciliation mechanisms were tested with data from more than 70,000 invoices and 33,000 open banking customers through multiple PSD2 operations. * The proposal is validated through several real-world use cases, including intelligent matching bank statements with their corresponding invoices. Moreover, a set of SWRL reasoning rules is also specified for classifying the financial solvency of clients, including an automatic generation of concepts that would facilitate conciliation. The remainder of this paper is organized as follows.