2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 2 , Issue 2 , PP: 98-105, 2021 | Cite this article as | XML | Html | PDF | Full Length Article
Mahmoud Ismail 1 *
Doi: https://doi.org/10.54216/AJBOR.020204
The surge of Fintech data and its implications on informed decision-making within the transportation sector have spurred the need for advanced analytical frameworks. This study addresses the challenge of leveraging Fintech data's temporal dynamics to enhance predictive capabilities and decision-making. The methodologies encompass an AutoEncoder (AE) for spatial feature extraction and an Improved Gated Recurrent Unit (IGRU) to capture temporal dependencies. Additionally, the Huber loss function optimizes model parameters, particularly in handling outliers. Integrating these techniques, our study explores Fintech data's spatial and temporal patterns, contributing insights for transportation planners and Fintech industries. Results demonstrate the efficacy of AE in learning spatial features, while IGRU effectively captures temporal dependencies, enabling the prediction of Fintech data with enhanced accuracy. The application of Huber loss ensures robustness by mitigating outlier influence. By the study's end, the model's predictive capabilities foster informed decision-making, offering opportunities to enhance Fintech data quality, reduce congestion, and bolster road safety. Overall, this research underscores the significance of advanced machine learning methodologies in decoding Fintech data's intricacies, laying a foundation for data-driven decision-making in the transportation and Fintech sectors.
Financial Technology , Market Analysis , Decision Support Systems , Data Analytics , Pricing Strategies , Information Management , Predictive Modeling , Competitive Intelligence , Technology Integration , Strategic Decision-Making
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