Scalable Risk-Aware ETL Pipelines for Enterprise Subledger Analytics

Abstract

The big data world has offered business organizations with an ever increasing volume of transactional data that requires scalable analytics that are risk averse and provide actionable insights that can be exploited to make a decision. The paper comprises a design and development of scalable risk-aware Extract, Transform, Load (ETL) pipelines with a specific focus on supporting enterprise subledger analytics. The overall goal will be developing a functional system which will be in a position to process high amounts of financial data, integrity of data and respond to risk exposure in real time.

 The model suggests a modular structure of the ETL pipeline which integrates the concepts of risk management into the conventional data transformation operations. It is comprised of the fact that the potential inconsistencies or errors in the data flow can be identified with the help of advanced means of data preprocessing, such as anomalies detection and predictive risk modeling. The framework also provides scalability with the use of distributed computing that enables the system to handle an increasing amount of data loads without impairing the performance of the system. The data mining of various data sources, the dynamic transformation of data, as per the quantitative analysis of the risk, and loading into subledger systems are the major elements of the pipeline, and the suitable control over the validation is followed.

 Also in the paper, the use of machine learning algorithms to predict risks of financial transactions and their effects on the subledger are discussed. The study provides the examples of implementation in the implementation of large-scale businesses and demonstrates how this method streamlines the efficiency of data processing and reduces risks when integrating the data.

Finally, the study will offer a solid approach to the creation of risk-conscious ETL pipes that will contribute to better subledger analytics of organizations, which will offer a higher quality of financial reporting and a better risk management system.