Providing quality customer service is one of the crucial matters of concern for any organization as it ensures the best customer satisfaction levels. Organizations all around the globe are widely using newly evolving technologies like artificial intelligence (AI) and machine learning applications to provide the best customer experience. The banking and financial services industry is on the front line to adopt AI-driven technologies in various departments worldwide. However, the adoption rate of these technologies is low in India in comparison with developed economies. In this article, we discussed the significance of various factors (customer satisfaction, AI knowledge among employees, and third-party tie-ups) on implementing the AI-driven technologies from employees’ perspectives at various levels of the organization. We analysed the usefulness of these newly evolving technologies to increase customer satisfaction and studied the role of crucial factors like data security issues and third-party tie-ups on AI technology implementation. The study is based on the primary data collected from 97 employees working in the Indian banking and financial sector across various metro cities of India (Hyderabad, Mumbai, Delhi) in various departments. We identified and analysed various factors that could impact the implementation of the AI-driven technologies at various departments, and we used logistic regression to investigate their significance. Chi-square test was used to check the associations between different factors. The study identifies and examines three key factors—customer satisfaction, AI knowledge among employees, and third-party tie-ups—that could impact AI-driven technologies implementation. According to this study, AI technology considerably decreases the cost and improves the customer experience of services. However, despite of many advantages the adoption rate of AI technology in the Indian banking and financial sector found extremely low. The implications of this study can be extended to the organizations for 360 degrees comprehensive understanding about the AI implementation and repercussions as there is a limited study about this topic from the bottom of the organizational verticals. This analysis is purely based on survey responses received from banking and financial sector employees across various metro cities of India (Hyderabad, Mumbai and Delhi).

Visual impairment poses significant challenges to individuals in their daily lives, limiting their access to information encoded in the visual domain. This paper presents a novel approach to empower the visually impaired by developing a system capable of translating handwritten digits into spoken language. The proposed system leverages a combination of advanced deep learning (DL) architecture, Hopfield Recurrent Neural Network-Grasshopper Optimization Algorithm (HRNN-GOA), and traditional image-processing techniques such as Haralick features. The system employs HRNN-GOA as the core model for handwritten digit recognition. HRNN-GOA exhibits superior sequential learning capabilities, capturing intricate patterns in the handwritten digits. Additionally, Haralick features are extracted from the input images, providing complementary texture-based information. The fusion of DL and traditional features aims to enhance the robustness and accuracy of the recognition process. The experimental results demonstrate the effectiveness of the proposed approach in accurately recognising handwritten digits. The HRNN-GOA model achieves state-of-the-art performance in digit classification tasks, while the incorporation of Haralick features further refines the recognition process, especially in cases with complex textures or variations in writing styles. The simulation results are compared against state-of-the-art strategies in terms of many metrics, including accuracy, precision, recall, specificity, area under the curve, F1-score, and false-positive rate. The proposed system has the potential to significantly improve the independence and quality of life for individuals with visual impairments by providing seamless access to numerical information in a spoken format. Future endeavours could explore the extension of this framework to recognise and translate more complex handwritten symbols or characters. Additionally, user experience studies and real-world deployment assessments will be crucial for refining the system and ensuring its practical utility in diverse scenarios.

His study investigates the role of FinTech (Financial Technology) in promoting sustainability and advancing the Sustainable Development Goals (SDGs) of the United Nations. It examines how FinTech innovations in financial inclusion, digital payments, and green finance contribute to economic and environmental sustainability. The research employs a comprehensive literature review and thematic analysis, using NVivo and the Orange Data Mining Tool, to identify key themes and analyze data from 77 scholarly articles, industry reports, and case studies on FinTech applications related to sustainability. The study finds that FinTech significantly supports SDGs through improved financial inclusion, empowering under-banked populations, and facilitating sustainable investments and green finance initiatives that address climate challenges. Blockchain and InsurTech also contribute to transparency and resilience, promoting responsible production and inclusive economic growth. This research provides valuable information for policymakers, financial institutions, and technology innovators on aligning FinTech with sustainability goals. It highlights the critical role of regulatory support and cross-sector collaboration in maximizing FinTech’s potential to drive sustainable development. This study addresses a gap in current research by systematically connecting FinTech innovations with specific SDGs, providing a structured framework to clarify FinTech's role in advancing sustainable development goals.