Semantic Automation of Basel III Liquidity Reporting: Utilizing Ontological Knowledge Graphs for Real-Time Regulatory Compliance and Auditability

Abstract

The growing complexity of worldwide financial regulations, especially Basel III framework has effectively increased the operation pressure on financial establishments. The liquidity risk management, particularly the adherence to the metrics like the Liquidity Coverage Ratio (LCR) and Net Stable Funding Ratio (NSFR), requires high-frequency data to be aggregated, semantically consistent, and audited. Conventional reporting designs - which are mostly based on siloed data warehouses and rule-based data transformation pipelines - do not support real-time reporting, do not have semantic interoperability, and have traceability problems. These inadequacies can lead to late reporting, inconsistency in regulatory filings and high compliance costs. The paper suggests a new semantic automation system, which uses ontological knowledge graphs to convert Basel III liquidity reporting into real-time, auditing, and intelligent processes. The framework provides semantic alignment of heterogeneous data sources by embedding domain ontologies in graph-based data models and thereby allows automated reasoning and compliance verification on demand. The proposed architecture is built with ontology-based data ingestion, knowledge graph building, inference engines based on rules, and real-time reporting dashboards. The study outlines the application of ontologies in the modeling of complex financial tasks including liquidity buffers, cash flow mismatches and counterparty risk. The framework supports unified data representation and supports more elaborate querying through the use of semantic web technologies, including RDF (Resource Description Framework), OWL (Web Ontology Language), and SPARQL query processing. Also, all these features are enhanced by the processing of streams so that the liquidity indicators can be updated in almost real time that will guarantee constant monitoring of compliance. One such crucial contribution made by this work is the provision of auditability by provenance tracking and explainable reasoning. All of the reported metrics can be provenanced and their transformation logic can be verified by the regulators and the internal auditors with limited effort. Moreover, automated anomaly detection and predictive analytics are assisted by the graph-based machine-learning method in the system. The implementation outcomes show the accuracy of the reporting, less latency and regulatory transparency. Analytical comparison reveals that the number of mistakes in data reconciliation was greatly decreased and the time of processing was reduced compared to the traditional systems. The framework can also be associated with the current enterprise data strategies such as the cloud-native architecture and big data ecosystems. Conclusively, ontological knowledge graph semantic automation can be considered as a transformative approach to Basel III liquidity reporting. It does not only improve the effectiveness of compliance but also creates a base of smart regulatory ecosystems that can respond to new financial regulations.