Scalable Integration Architectures for Heterogeneous Multi-Source Data Extraction in Financial Services

Authors

  • Laxmi Pratyusha Konda

Keywords:

Data Extraction; Heterogeneous Integration; Multi-Source Aggregation; Microservices Architecture; Adapter Pattern; Financial Services; Validation Engine

Abstract

Financial services applications frequently integrate with complex heterogeneous digital ecosystems. Integrating financial services sources with REST APIs, SFTP file downloads, web portals, and other services often leads to quadratic point-to-point integration complexity as the number of source systems increases, high engineering effort, and brittle integrations that break due to interface changes, API deprecations, or changing authentication schemes in third-party integrations. The solution consists of a scalable architecture that abstracts heterogeneous data extraction from heterogeneous data sources using an API-first integration approach, file-based extraction, and smart web navigation through headless browsers. The architecture's central contribution is the adapter pattern, using which new data sources can be connected without the need to modify the extraction logic, thus speeding up the integration process. The multi-strategy extraction framework uses standardized adapter interfaces for various source types. Centralized four-layer validation checks format, cross-reference statistical deviations, and user-defined business rules for all extracted data. The containerized microservices implementation allows horizontal scaling and high availability using auto-scaling parameters and deployment across availability zones. The web navigation domain-specific language allows users to write extraction scripts without needing to understand how browser automation works. Confidence scoring allows records to be routed along semantic processing pipelines based on the results of validation. The architectural patterns outlined above apply to any organization that needs to aggregate data or information from many external, heterogeneous sources subject to high-quality constraints.

DOI: https://doi.org/10.17762/ijisae.v14i1s.8229

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Published

14.02.2026

How to Cite

Laxmi Pratyusha Konda. (2026). Scalable Integration Architectures for Heterogeneous Multi-Source Data Extraction in Financial Services. International Journal of Intelligent Systems and Applications in Engineering, 14(1s), 656–667. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/8229

Issue

Vol. 14 No. 1s (2026)

Section

Research Article

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