Data-Driven Predictive Analytics and Decision-Making in FinTech Using MongoDB and High-Throughput Data Pipelines

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Year : 2025 | Volume :03 | Issue : 01 | Page : –
By
vector

Anil Kumar Bayya,

  1. Testworx, Chicago, Cook County, , United States of America

Abstract

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This paper examines the implementation of MongoDB and high-throughput data pipelines within the FinTech sector to drive data-informed predictive analytics and decision-making. The study focuses on the architectural components, scalability, and challenges of integrating NoSQL databases into real-time data ingestion and analytics pipelines. The transformative potential of these technologies in modern financial systems is highlighted through practical use cases such as fraud detection, credit scoring, and personalized financial services. A key area of focus is MongoDB’s schema flexibility and document-oriented architecture, which enable dynamic data modeling and iterative development within the volatile and fast-paced FinTech environment. Additionally, the integration of MongoDB with streaming platforms like Apache Kafka and Apache Flink is explored to emphasize the importance of seamless data flow and low-latency processing in supporting real-time decision-making. The study also addresses critical concerns around data consistency, security, and regulatory compliance, which are paramount in financial applications. The paper further investigates the deployment of MongoDB in hybrid and multi-cloud environments, emphasizing scalability, fault tolerance, and cost efficiency. Advanced analytics applications, including risk management, algorithmic trading, and customer segmentation, are analyzed to illustrate the role of machine learning models in deriving actionable insights from high-throughput pipelines. The research concludes with an analysis of emerging trends, such as the integration of artificial intelligence, the potential impact of quantum computing on database technologies, and the evolving regulatory landscape that shapes innovation in financial technology. This study underscores the pivotal role of MongoDB and data pipelines in advancing the digital transformation of financial services, providing a foundation for future advancements in the industry.

Keywords: Financial Technology (FinTech), MongoDB, NoSQL (Not Only SQL), Extract, Transform, Load (ETL), Application Programming Interfaces (APIs), Artificial Intelligence (AI), Machine Learning (ML), Apache Kafka, Apache Flink, Directed Acyclic Graphs (DAGs), General Data Protection Regulation (GDPR), Payment Card Industry Data Security Standard (PCI DSS), Platform as a Service (PaaS), JavaScript Object Notation (JSON), Continuous Integration/Continuous Deployment (CI/CD)

[This article belongs to International Journal of Algorithms Design and Analysis Review (ijadar)]

How to cite this article:
Anil Kumar Bayya. Data-Driven Predictive Analytics and Decision-Making in FinTech Using MongoDB and High-Throughput Data Pipelines. International Journal of Algorithms Design and Analysis Review. 2025; 03(01):-.
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Anil Kumar Bayya. Data-Driven Predictive Analytics and Decision-Making in FinTech Using MongoDB and High-Throughput Data Pipelines. International Journal of Algorithms Design and Analysis Review. 2025; 03(01):-. Available from: https://journals.stmjournals.com/ijadar/article=2025/view=0

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Regular Issue Subscription Original Research
Volume 03
Issue 01
Received 09/01/2025
Accepted 23/01/2025
Published 24/01/2025
113