Neural Networks and Rules-based Systems used to Find Rational and Scientific Correlations between being Here and Now with Afterlife Conditions
February 17, 2026
Neural Networks and Rules-based Systems used to Find Rational and
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This article describes the integration from big data environment in the management of products and services from a banking entity with optimizing financial products and decisionmaking. Actually, there are many financial entities where their different business areas have isolated databases, causing greater consumption of computer resources, maintainability and, in many cases, process delays. This problem becomes critical specially if there is a transnational company because data needs can vary geographically despite being the same functional area. The Data Architecture area proposed guidelines such as centralizing information in a big data environment, ensuring progressive accessibility from users for new financial analytics initiatives and thereby reducing isolated data. The agile, Scrum framework supported the advanced analytics pilot which comprising developments in the data ingestion layer (data lake) through the distributed processing from Apache Spark; and information consumption through Sandboxes, which one, users performing the analysis, visualization and prediction from data.
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Ulises Roman Concha. 2026. \u201cIntegration of the Big Data Environment in a Financial Sector Entity to Optimize Products, Services and Decision Making\u201d. Global Journal of Computer Science and Technology - C: Software & Data Engineering GJCST-C Volume 22 (GJCST Volume 22 Issue C2): .
Crossref Journal DOI 10.17406/gjcst
Print ISSN 0975-4350
e-ISSN 0975-4172
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This article describes the integration from big data environment in the management of products and services from a banking entity with optimizing financial products and decisionmaking. Actually, there are many financial entities where their different business areas have isolated databases, causing greater consumption of computer resources, maintainability and, in many cases, process delays. This problem becomes critical specially if there is a transnational company because data needs can vary geographically despite being the same functional area. The Data Architecture area proposed guidelines such as centralizing information in a big data environment, ensuring progressive accessibility from users for new financial analytics initiatives and thereby reducing isolated data. The agile, Scrum framework supported the advanced analytics pilot which comprising developments in the data ingestion layer (data lake) through the distributed processing from Apache Spark; and information consumption through Sandboxes, which one, users performing the analysis, visualization and prediction from data.
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