Daily Archives: November 14, 2026

Authors: Dr. S. Mohana Murali

Abstract: Artificial Intelligence (AI) has emerged as one of the most important technologies transforming the banking and financial services industry. The rapid expansion of internet banking, mobile banking, digital wallets, real-time payments and account-based payment systems has generated enormous volumes of financial and transactional data. AI and Machine Learning (ML) provide financial institutions with the ability to analyse this data, identify patterns, automate decisions and deliver personalised services. This research paper examines the role of Artificial Intelligence in digital banking and payment systems, with particular emphasis on fraud detection, cybersecurity, customer service, credit assessment, Know Your Customer (KYC), Anti-Money Laundering (AML), risk management, personalised banking, payment processing and operational efficiency. The paper also examines the Indian context, including the development of UPI and the increasing use of AI in India's banking and payment ecosystem. The study adopts a descriptive and analytical research methodology based on secondary sources, including reports and publications of the Reserve Bank of India (RBI), Bank for International Settlements (BIS), International Monetary Fund (IMF), World Bank and National Payments Corporation of India (NPCI). The study finds that AI can improve the speed, efficiency, security and personalisation of digital banking and payment services. However, AI also introduces challenges relating to data privacy, algorithmic bias, explainability, cybersecurity, model risk, third-party technology dependence and accountability. Therefore, responsible AI governance, human oversight, cybersecurity controls and appropriate regulatory frameworks are essential for sustainable adoption of AI in banking and payment systems.

DOI: https://doi.org/10.5281/zenodo.24218110

Published by: vikaspatanker

Authors: Ketki Satish Pimparkar, Samruddhi Ramesh Rindhe, Assistant Professor Dr. S.D. Shinde

Abstract: Steel is important for buildings, bridges, roads, vehicles and machines. Its production supports development, but it also uses energy and raw materials and may create dust, air pollution, greenhouse gas emissions and solid waste. This paper reviews published research on environmental challenges in the steel industry and explains the main manufacturing stages. It describes the Blast Furnace–Basic Oxygen Furnace route and the Electric Arc Furnace (EAF), including its working principle and advantages. A manufacturing flow chart, a labelled EAF diagram and the supplied photograph of an industrial furnace are included. Ten major steel and metals-sector companies operating in India are also listed. The review suggests that good pollution control, efficient equipment, energy management and recycling can help reduce the environmental impact of steel production. This is a literature-based student paper and does not report new experiments. Steel is a fundamental material for construction, transportation, machinery and infrastructure. This review paper presents an overview of the steel industry, including raw materials, major production routes, steel manufacturing stages, Electric Arc Furnace (EAF) technology, environmental impacts and sustainable practices. It explains the blast furnace–basic oxygen furnace route and the EAF route, followed by secondary refining, continuous casting and rolling. Particular attention is given to EAF components, operation, advantages andlimitations. The paper also discusses greenhouse-gas emissions, air pollution, water use, solid by-products and occupational safety, together with commonly applied control measures. Applications of steel in civil engineering and other sectors are outlined, and examples of major Indian steel producers are listed for context. The review indicates that energy efficiency, responsible scrap recycling, cleaner electricity, pollution control and by-product utilization are important measures for improving the sustainability of steel production. The paper is a descriptive review and does not present plant-specific experimental results.

DOI: https://doi.org/10.5281/zenodo.24217845

Published by: vikaspatanker

Authors: Suyash Dahitule, Ketan Sonawane, Om Ranmode, Prasad Ingole, Dr.Chetana Nemade

Abstract: Color blindness, also known as color vision deficiency (CVD), is a visual condition in which an individual experiences difficulty distinguishing certain colors, particularly red, green, and blue. Early detection of color blindness is important for improving visual health awareness and supporting individuals in their daily activities. This study, titled “Color Blindness Detection Using Ishihara Plates,” focuses on identifying color vision deficiencies through the Ishihara Color Test, a widely used screening method based on specially designed pseudoisochromatic plates containing numbers, patterns, and symbols formed by colored dots. The proposed system aims to provide a simple, cost-effective, and user-friendly approach to preliminary color blindness detection. During the testing process, individuals are asked to identify the numbers or patterns displayed on a series of Ishihara plates. Their responses are analyzed to determine possible difficulties in distinguishing specific color combinations, particularly red-green variations. The system can record responses, evaluate test results, and generate an assessment indicating whether the individual may have normal color vision or a possible color vision deficiency. The approach helps reduce the time and effort required for preliminary screening while increasing accessibility to basic color vision testing. The study highlights the importance of digital screening solutions in promoting early identification and awareness of color vision deficiencies. However, the results are intended for preliminary screening and should be confirmed through a comprehensive examination by a qualified eye-care professional.

DOI: https://doi.org/10.5281/zenodo.24216479

Published by: vikaspatanker

Authors: Irakshetti Pranav Amol, Prof. C. A. Kolse, Prof. M. C. Kolse, Prof. S. K. Said

Abstract: Credit risk assessment is an important process in the banking and financial industry, as it helps financial institutions evaluate the possibility of borrowers failing to repay their loans. With the advancement of Artificial Intelligence (AI) and Machine Learning (ML), traditional credit assessment methods are increasingly being supported by intelligent prediction models. However, many advanced AI models operate as black-box systems, making it difficult to understand how they arrive at particular credit decisions. Explainable Artificial Intelligence (XAI) addresses this limitation by providing transparency and interpretability in AI based credit risk assessment. This paper presents a review of existing XAI techniques and their applications in predicting credit risk and supporting financial decision-making. Various machine learning approaches, including Decision Trees, Random Forest, Logistic Regression, and Gradient Boosting, are examined along with explainability techniques such as SHAP (SHapley Additive exPlanations), LIME (Local Interpretable Model-Agnostic Explanations), and feature importance analysis. The review discusses how these methods help identify important factors influencing credit scores, loan approvals, and default predictions. It also highlights the benefits of explainable models, including improved transparency, customer trust, accountability, and regulatory compliance. Furthermore, major challenges such as data privacy, algorithmic bias, model complexity, and the balance between prediction accuracy and interpretability are explored. The paper also discusses future research opportunities in developing fair, reliable, and transparent AI-based financial systems. Overall, this review emphasizes the importance of Explainable AI in making credit risk assessment more understandable, trustworthy, and responsible while supporting better financial decisions.

DOI: https://doi.org/10.5281/zenodo.24214153

Published by: vikaspatanker