Technical gazette, Vol. 33 No. 3, 2026.
Original scientific paper
https://doi.org/10.17559/TV-20250224002412
Optimizing Banking Operations with AI Using BiGRU-FOA for Financial Data Analysis
Anbarasu Aladiyan
orcid.org/0009-0008-8812-9365
; Lead Software Engineer, Compunnel, Inc, USA
*
* Corresponding author.
Abstract
The financial sector is undergoing a profound transformation with the integration of artificial intelligence (AI) and cloud computing technologies. A notable advancement is the deployment of a deep learning classification system that integrates Bidirectional Gated Recurrent Units (BiGRU) with the Fruit Fly Optimization Algorithm (FOA) to enhance complex banking operations. The BiGRU model efficiently analyzes financial transactions, customer profiles, and risk patterns by processing sequential data with long-term dependencies. FOA, inspired by the foraging behavior of fruit flies, optimizes the network's performance and computational efficiency. A cloud-based implementation of the BiGRU-FOA framework ensures scalability, real-time processing, and seamless integration with existing banking infrastructure. Experimental results demonstrate that BiGRU-FOA outperforms traditional machine learning techniques and standalone deep learning models in financial dataset classification, achieving superior accuracy, precision, and recall. This model enhances fraud detection, customer segmentation, and credit risk assessment, paving the way for more efficient and intelligent banking operations. By leveraging this advanced AI-driven framework, banks can improve decision-making processes, enhance operational efficiency, and offer personalized financial services. This research highlights the potential of deep learning and optimization technologies in revolutionizing the banking sector, enabling a more secure, efficient, and customer-centric approach.
Keywords
AI-driven banking solutions; BiGRU-FOA Optimization; deep learning in banking; financial data classification; fraud detection
Hrčak ID:
346700
URI
Publication date:
30.4.2026.
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