Integrating ESG, AI, and Financial Strategies in Banking: Advancing Sustainable Innovation and Risk Management

Sustainable corporate finance is an attractive field of study in sustainability literature; however, the literature lacks systematic bibliometric analysis that provides a comprehensive review to clarify state-of-the-art sustainable corporate finance and that discusses new opportunities and potential instructions for further studies. To address this gap, this study adopts a literature review, bibliometric analysis, network analysis and co-wording technique to systematically investigate the Scopus database. In total, 30 keywords listed at least three times are used and are divided into six clusters considering six fields of research, namely, corporate finance in corporate sustainability, sustainable competitive advantages, sustainable stakeholder engagement, circular economy, sustainable corporate finance innovation and risk management and sustainable supply chain ethics. This study contributes to examining the sustainable corporate finance bibliometric status to provide directions for future studies and practical accomplishment. The sustainable corporate finance knowledge gaps are (1) corporate finance in sustainability; (2) sustainable competitive advantages; (3) sustainable stakeholder engagement; (4) circular economy; (5) sustainable corporate finance innovation and risk management; and (6) sustainable supply chain ethics. The knowledge gaps and future directions are also discussed.

Sustainable risk and disaster management in the built environment has become a critical research focus amid escalating environmental challenges. Building Information Modeling (BIM) is recognized as a key digital tool for enhancing disaster resilience through simulation, data integration, and collaborative management. This study systematically reviews BIM applications in sustainable risk and disaster management from 2014 to 2024, employing the PRISMA framework, literature coding, and network analysis. Five primary research clusters are identified: (a) sustainable construction and life cycle assessment, (b) performance evaluation and implementation, (c) technology integration and digital innovation, (d) Historic Building Modeling (HBIM) and post-disaster reconstruction, and (e) project management and technology adoption. Despite increasing scholarly attention, the field remains dominated by conceptual studies, with limited empirical exploration of emerging technologies such as artificial intelligence (AI). Four key challenges are highlighted: weak foundational integration with structural risk research, technological bottlenecks in AI and digital applications, limited practical implementation, and insufficient linkage between sustainability and risk management. Future trends are expected to focus on achieving Industry 4.0 interoperability, advancing AI-driven intelligent disaster response, and adopting multi-objective optimization strategies balancing resilience, sustainability, and cost-effectiveness. This study provides a comprehensive overview of the field’s evolution and offers insights into strategic directions for future research and practical innovation.

PurposeFashion supply chain (FSC) research has identified two important issues of sustainability management and risk management. However, investigation of these issues is relatively sparse and has primarily been independent with little combinatory research, despite their important interrelationships. The purpose of this paper is to address that gap by critically reviewing extant literature to synthesise important sustainability risk issues in FSCs and proposing an empirical research agenda.Design/methodology/approachThis paper uses a structured literature review approach and Denyer and Tranfield’s (2009) context, intervention, mechanisms and outcome (CIMO) criteria for critical analysis to enable the development of future empirical research areas.FindingsWhile sustainability and risk are discussed independently in the supply chain literature, combinatory discussions are very limited, despite the interdependence of these concepts. There is little substantial research on sustainability risk in global FSCs and therefore, an empirical research agenda is proposed with the four research directions to address the gap and take forward the notion of supply chain sustainability risk management in FSCs: definition; organisation and management; influence on performance; and development of a conceptual framework.Research limitations/implicationsThis paper provides a critical literature review and thus lacks empirical study.Practical implicationsThis paper highlights important issues in sustainability risk management for FSCs and presents an agenda for future empirical research.Originality/valueThis paper contributes by providing a combinatory synthesis of sustainability and risk management in FSC literature and an agenda for future empirical research.

Innovation and risk management in the service industry pose unique challenges as services cannot be inventoried as the customer is involved in the service delivery process. This research work focuses on the study of the service industry and understanding how they are coping with the forces of continuity and change in innovation and risk management. The data was generated from secondary research and primary research through personal interviews with over 100 experts using a structured questionnaire. Factor analysis of the data collected reveals six underlying factors from perspective of continuity and seven factors for change affecting innovation and risk management in service operations. The continuity and change framework suggested would lead to more efficient and sustainable innovation and risk management in service enterprises. This research paper represents one of the few efforts to study innovation and risk management in services from this perspective. The framework suggested can be adapted for applications in the global context.

Purpose This research seeks to systematically review studies on risk management (RM) in the construction industry, tracing its evolution from traditional approaches focused on risk identification, assessment, and mitigation to modern approaches that prioritize sustainability, resilience, and adaptability. It aims to develop a clear definition of sustainable risk management (SRM) and propose a framework for its implementation to ensure cost-efficient, resilient, and flexible operations. Design/methodology/approach A systematic literature review, guided by the PRISMA framework, analyzed 79 peer-reviewed articles (2014–2024) from Scopus, IEEE Xplore, Wiley, and other databases. Thematic grouping was used to categorize key SRM components, identifying emerging trends, gaps, and challenges in its adoption. Findings The study identifies key SRM pillars and attributes, demonstrating how SRM enhances the sustainability and resilience of RM practices. The proposed framework provides a structured approach to integrating SRM principles into construction operations, addressing implementation barriers such as regulatory misalignment, industry resistance, and technological integration. The findings also highlight the broader impact of SRM on shaping proactive and future-proof RM strategies. Practical implications Construction firms and policymakers can benefit from the findings of this study by understanding the key pillars, attributes, and challenges of SRM. The proposed framework provides practical guidance for firms to evaluate and improve their current RM practices, particularly in addressing complex industry challenges and enhancing resilience and sustainability. Policymakers can use these insights to align regulations with SRM principles, supporting RM processes that are both effective and future-proof. Additionally, the study equips industry professionals with tools to enhance adaptability and long-term RM effectiveness. Originality/value As one of the first comprehensive reviews of SRM in the construction industry, this study consolidates key insights and provides a structured framework for both researchers and practitioners. It advances discussions on integrating sustainability within RM and highlights the need for empirical validation, particularly in assessing the role of digital transformation in SRM. By bridging theoretical gaps and practical applications, this research establishes a foundation for future studies on sustainable and technology-driven RM strategies.

This study investigates the integration of sustainability practices and risk management in Indonesian social enterprises, emphasizing the role of innovation as a mediator and operational type as a moderator. Social enterprises face unique challenges in balancing economic sustainability with social impact, especially in emerging markets like Indonesia. A structured survey was conducted with 118 social enterprises to assess their sustainable practices, risk management procedures, innovation scores, and operational models (permanent vs. project-based). Using Structural Equation Modeling (SEM) and Partial Least Squares (PLS) analysis, the results show that sustainability practices positively influence innovation, while both innovation and risk management significantly improve sustainable performance. Additionally, innovation mediates the relationship between sustainability practices, risk management, and performance. The operational type moderates the link between risk management and sustainable performance but does not influence the connection between sustainability practices and performance. These findings suggest that innovation is crucial for improving the sustainability of social enterprises and that risk management strategies should be tailored to the operational model. Social enterprises in Indonesia should prioritize innovative approaches and effective risk management to enhance their long-term sustainability and social impact.

The main objective of this research is to investigate the role of controlling in the innovation management process respecting sustainability. The question of interest is whether controlling is involved in the innovation management process and how to measure the effectiveness of innovation process using controlling as analytical and informative function and support to the management of the company. Today's approach to strategic management emphasized concept of sustainability and innovation. For that reason there is a need for a broader role of controlling in decision making process, especially for the purposes of efficient measurement system. In order to develop conceptual model of the relationship between innovation management and controlling research is done on Croatian enterprises that has controlling department. The involvement of controlling function in innovation process is analyzed using interview method and results confirmed insufficiently developed linkage between controlling and sustainable innovation management. The conceptual model which is proposed is developed with regard to sustainable innovation process and management performance within which controlling place coordinative and integrative role. A model suggests five stages of the innovation process in which controlling is included as analytical and informative function. Also, a model provides a framework for further elaboration of controlling effectiveness, when it is included in innovation management process.

The effective development of the digital economy largely depends on the integration of artificial intelligence (AI) technologies into company business processes. The use of AI significantly increases labor productivity, optimizes internal operations, and creates sustainable competitive advantages in the market. However, the implementation of AI involves high financial costs, lack of guarantees of investment returns, and is accompanied by uncertainty in evaluating its actual effectiveness. A particularly pressing issue is the lack of understanding within the business community regarding the essence and functional capabilities of AI. The academic and business environments still lack a unified and established definition of AI, and the term is often inaccurately applied to algorithmic tools. This reflects the complexity, multilayered nature, and interdisciplinary character of the field. This article aims to systematize key concepts, functions, methods, and application areas of AI in business. Successful cases of AI implementation in management are analyzed, and critical success factors are identified. The empirical base includes secondary data and expert interviews with practitioners implementing AI projects in companies across various industries and levels of maturity. Special attention is given to analyzing the barriers and risks associated with integrating AI into existing business models and organizational structures. Internal and external types of AI influence on business are substantiated - from analytical transformation to improvements in operational, financial, market, and environmental performance. Promising directions for AI development are also discussed in the context of sustainable growth, digitalization, innovation management, and strategic planning amid digital transformation.

The integration of artificial intelligence (AI) in financial analytics is transforming the landscape of decision-making, risk management, and business performance optimization. This study explores the development of AI-driven financial models that leverage machine learning, deep learning, and natural language processing to generate predictive insights for improved decision-making. By integrating structured and unstructured financial data, AI-driven analytics enhance accuracy, efficiency, and adaptability in financial forecasting, fraud detection, and investment strategies. Traditional financial analytics rely on historical data and rule-based models, which often fail to adapt to dynamic market conditions. AI-driven models, on the other hand, utilize real-time data processing, automated feature selection, and adaptive learning mechanisms to provide more precise and timely financial insights. These models enable businesses to proactively identify risks, optimize resource allocation, and improve profitability through data-driven decision-making. This study examines various AI techniques, including supervised and unsupervised learning, reinforcement learning, and sentiment analysis, in predicting market trends, customer behavior, and credit risk. A key contribution of this study is the development of a framework for AI-driven financial analytics that integrates big data processing, cloud computing, and AI algorithms to streamline financial operations. The framework is evaluated using empirical financial datasets, demonstrating its ability to enhance predictive accuracy, reduce operational inefficiencies, and optimize financial strategies. Additionally, the study highlights the ethical considerations, biases, and regulatory challenges associated with AI-driven financial decision-making. The findings underscore the significance of AI in financial analytics for increasing transparency, mitigating risks, and fostering strategic decision-making. By leveraging AI, financial institutions can improve fraud detection systems, optimize algorithmic trading, and enhance customer relationship management. Furthermore, this study discusses the future implications of AI in financial analytics, including the potential for AI-powered financial assistants, enhanced personalization in financial services, and the role of explainable AI in regulatory compliance. This research contributes to the growing body of knowledge on AI applications in finance and provides insights into the practical deployment of AI-driven financial models for enhanced business performance and decision-making.

This paper explores the intersection of artificial intelligence (AI) and the financial sector, showcasing their transformative synergy. The integration of AI into finance has led to pioneering advancements like robo-advisors and AI-driven risk assessment methods. These innovations reshape investment strategies and risk management, ushering in a new era of financial operations. The study's focal question examines how AI recalibrates investment management, risk assessment, and fraud prevention in finance. The paper comprises sections on AI's impact on investment management, risk assessment, and fraud detection, detailing how robo-advisors provide personalized portfolio recommendations, AI aids risk identification and management, and transaction surveillance benefits from AI-powered fraud detection. Ethical, regulatory, and accountability considerations are discussed, reflecting AI's transformative influence on traditional financial paradigms. The application of AI in transaction detection and its role in enhancing portfolio recommendations, risk management, and automated trading are examined. While AI holds potential, its limitations such as data quality, model risks, and ethical concerns must be addressed. Regulatory oversight is crucial to ensure responsible AI implementation, fostering a balance between technological progress and financial stability. This paper underscores the intricate relationship between AI and finance, portraying AI's capacity to reshape the financial landscape and drive innovation

Abstracts The rapid evolution of Artificial Intelligence (AI) has revolutionized the landscape of risk management, introducing powerful predictive models that can identify, assess, and mitigate risks with unprecedented accuracy and speed. From finance and healthcare to supply chains and cybersecurity, AI-driven risk management tools are reshaping organizational strategies and decision-making frameworks. At the heart of this transformation are machine learning algorithms and data analytics techniques capable of processing vast amounts of structured and unstructured data to forecast potential threats and opportunities. These predictive models enhance early warning systems, optimize resource allocation, and improve operational resilience. However, the integration of AI into risk management is not without its challenges. As AI systems become more autonomous and complex, new risks emerge—such as model opacity, algorithmic bias, and systemic vulnerabilities. These risks are compounded by the lack of standardization in AI governance and the difficulty of interpreting machine-driven decisions. Regulatory frameworks around the world are struggling to keep pace with technological advancements, raising concerns over accountability, transparency, and ethical usage. Current regulations are often reactive and fragmented, creating inconsistencies across jurisdictions and sectors. This paper explores the dual-edged nature of AI in risk management by critically examining its predictive capabilities alongside the regulatory challenges it presents. We delve into the architecture of AI-based risk models, their applications across industries, and the methodological issues related to data integrity, explainability, and model validation. Case studies highlight how leading organizations have harnessed AI to enhance risk detection while navigating the limitations and uncertainties of these technologies. In parallel, the paper evaluates the evolving regulatory landscape, including notable efforts by the European Union (such as the AI Act), the United States (through NIST and executive orders), and other international bodies. It discusses how regulators are attempting to balance innovation with safeguards, emphasizing the need for frameworks that are adaptive, inclusive, and technologically informed. The analysis includes a review of principles such as “human-in-the-loop,” fairness, accountability, and transparency (FAT), and how these are being operationalized in policy and corporate governance. Ultimately, this study argues for a multidisciplinary approach to AI risk management—one that combines technical rigor with legal, ethical, and organizational insights. It calls for the development of robust regulatory ecosystems that can foster responsible AI deployment without stifling innovation. Future directions include the standardization of risk assessment protocols for AI systems, cross-sectoral collaboration for best practices, and the promotion of explainable AI to bridge the gap between machine predictions and human judgment. By understanding both the power and the pitfalls of AI in risk management, stakeholders can better navigate the complexities of this transformative era. Key Words: Artificial Intelligence, Risk Management, Predictive Models, Regulatory Challenges, Algorithmic Bias, Explainable AI, Governance Frameworks.

Contents: Foreword Introduction to corporate sustainability: enterprise-wide risk management approach to contemporary business management and organization Corporate sustainability and enterprise risk management The enterprise sustainability risk management conceptual model: a holistic and proactive perspective The economics of global warming Global warming and sustainable aviation Modeling human factor-based risks in aviation Risk management, change management, and effectiveness in aviation operations The integration of sustainability risk into airport business and management Concluding remarks References Index.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.

Artificial Intelligence (AI) has emerged as a disruptive and transformative force in education as it offers potential benefits such as personalized learning, effective assessment methodologies, and automated administrative processes. This study examines the teachers' perspectives on AI integration in education, reflecting on their perceptions, prevalent challenges, and professional development practices required to empower the teachers with technical skills to ensure effective implementation of AI. A questionnaire was prepared, validated, and used to collect data from the teachers about their awareness and readiness to adopt emerging technologies such as AI, AR, and VR. Some open-ended questions were added to collect information regarding the challenges faced and supportive measures required for AI integration in Education.The research reveals that the majority of teachers reflected a positive attitude toward AI integration. Many educators realize that AI can fill quality gaps in education by making learning experiences more enriching, and student-centered, and enhancing assessment practice. Teachers also appreciate AI in terms of alleviating their burden and making the teaching-learning process student-centric. However, the report highlights major challenges faced by teachers in integrating AI in Education, including limited accessibility to AI-based resources, lack of training, ethical concerns, and data privacy. Concerns regarding resistance to change and infrastructure constraints complicate AI integration further. The study underscores the need for effective and professional training programs to equip and apprise teachers with the skills and confidence to integrate AI into teaching practices. Workshops, online courses, and hands-on training are preferred modes of professional development identified through the study. Moreover, Institutional policies must also align with the vision of NEP 2020 regarding AI in education. Policies also try to create friendly environments for using AI, reducing infrastructural bottlenecks or gaps, establishing ethical use guidelines, and involving teachers in processes of decision-making.This research has also emphasized the role of teachers in realizing AI’s potential and advocating for effective strategies needed to overcome challenges associated with AI Integration. By empowering teachers through adequate training and resources, the education sector can harness the power of AI to create an inclusive, effective, and future-ready learning environment.

This study empirically investigated the role played by government policy in the financial industry in promoting sustainable innovation, business performance, and risk management. An original dataset, comprising data from the Taiwan Economic Journal (TEJ), Taiwan Patent Search System, and company annual reports from the period 2015–2019 was used to analyze the effects of government policy on the financial industry in Taiwan. The research results showed that a firm’s sustainable commitment is conducive to its business growth and does not increase its risk in the financial industry. The financial industry can report on FinTech news that highlights business growth, while companies with high capital adequacy rates are better equipped to manage the risks associated with innovation commitment. Financial companies are suggested to engage in sustainable innovation and thus improve their profitability. In addition, policymakers should mandate that financial companies increase their capital adequacy ratios, improve their risk-bearing capacity, and maintain financial market stability.