The integration of Environmental, Social, and Governance (ESG) principles with Financial Technology (Fintech) has emerged as a pivotal mechanism for advancing sustainable finance. This study investigates the interplay between ESG and Fintech through bibliometric and thematic content analysis to uncover key research trends, thematic clusters, and existing knowledge gaps in this dynamic field. The research problem focuses on how FinTech innovations can support ESG-driven initiatives such as corporate social responsibility (CSR), financial inclusion, and sustainable development while addressing challenges like performance metrics and governance issues. By mapping the research landscape, the study identifies significant contributions from scholars, notably in China and the USA, and explores prominent themes, including the role of Fintech in ESG disclosures, corporate governance, and sustainability. Emerging technologies like AI and blockchain are also highlighted for their impact on ESG reporting. The findings reveal exponential academic interest in this domain but underscore critical industrial challenges, such as the absence of standardized ESG metrics and the limited application of Fintech in addressing sustainability issues. The study concludes by offering future research directions aimed at bridging these gaps and emphasizing the transformative potential of Fintech in driving sustainability across the financial sector.

The Banking sector of Bangladesh is trapped in a gridlock of non-performing loans (NPLs) so much so that NPL accounts for 11.60 percent of the total volume of classified loans. This problem has started to be widening with an evil trend of loan embezzlement among the industrial borrowers in our country. Frequent scam series in banking industry is surely a red light and unfortunately the commercial banks are highly surrounded by it. The goal of the study is to analyze the impact of non-performing loan (NPL) on profitability where in this study considered net interest margin (NIM). This paper attempts to find out the time series scenario of non-performing loans (NPLs), its growth, provisions and relation with banks profitability by using some ratios and a linear regression model of econometric technique. The empirical results represent that non-performing loan (NPL) as percentage of total loans on listed banks in Dhaka Stock Exchange (DSE) is very high and they holds more than 50 % of total non-performing loans (NPLs) of the listed 30 banks in Dhaka Stock Exchange (DSE) for year 2008 to 2013. Moreover it is one of the major factors of influencing banks profitability and it has statistically significant negative impact on net profit margin (NPM) of listed banks for the study periods.

Research background: Digitalization and the associated digital divide are crucial issues impacting socio-economic development globally. Extensive research has examined digitalization and the digital divide in EU countries, but there is a lack of understanding regarding comparisons with studies conducted in Western Balkan countries. This study investigates digitalization trends in research from the past five years in both regions, focusing on efforts and factors contributing to the digital gap. Purpose of the article: The study analyzes research on digitalization from 2018 to 2023 in the EU and Western Balkans. It explores factors causing the digital divide and efforts in digitalization, aiming to guide future research and policy for digital inclusion and sustainable development. Methods: The study employs a meticulous data selection process, choosing Scopus as the database for its extensive coverage of diverse journals. A total of 1119 articles from EU countries and 277 from Western Balkan countries are selected for bibliometric analysis, adhering to PRISMA guidelines. Findings & value added: The research reveals a growing interest in digitalization-related issues, demonstrating the multidisciplinary nature of ongoing research. It points out the distribution of publications on digitalization in the EU and Western Balkans countries. The EU focuses on digital technologies, economic growth, and sustainability, while Western Balkan countries focus on COVID-19 impact and digitalization in education and business. The research compares digitalization efforts in the EU and Western Balkan countries presented in the literature, pointing to new dimensions of the digital divide studies. It discusses how socio-economic contexts affect digital transformation and stresses the need for tailored policy approaches for digital inclusivity. These insights are of great importance for policymakers, researchers, and practitioners working towards global digital development and bridging the digital divide. The study lays the groundwork for future research and policy considerations, considering limitations like potential bias in databases and search criteria.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.

The ever-changing landscape of financial technology has undergone significant changes owing to advancements in machine learning, artificial intelligence, blockchains, and digitalization. These changes have had a profound impact on the provision of financial services, specifically, credit scoring and lending. This study examines the intersection of financial technology, artificial intelligence, machine learning, blockchain, and digitalization in the context of credit services with a focus on credit scoring and lending. This study addressed three main research questions: The research followed a comprehensive methodology, considering factors such as population, intervention, comparison, outcomes, and setting to ensure that collected data aligns with research objectives. The research questions were structured using the PICOS framework, and the PRISMA model was used for the systematic review and study selection. The publications analysed covered a wide range of datasets and methodologies.