This study evaluates the application of financial technology (FinTech) and artificial intelligence (AI), as well as the transparency of their disclosure in Sustainability Reports across four types of financial service institutions (FSIs) in Indonesia: banks, insurance companies, finance companies, and securities firms. By analysing the content of 20 Sustainability Reports, this study finds that banks have the highest level of technology implementation and disclosure, followed by finance, insurance, and securities firms. Although FinTech and AI contribute to operational efficiency, service innovation, and the expansion of financial access, disclosures related to these technologies are still limited in Sustainability Reports, often using terms like “digitalisation” without explicit explanation. These findings underscore the importance of enhanced transparency in technology disclosure to foster public trust and accountability, as well as to ensure compliance with regulations such as the OJK Regulation on Sustainable Finance. This study recommends strengthening reporting standards and regulatory guidelines to improve technology disclosures in the financial sector, thus enabling a sustainable and inclusive digital financial ecosystem.
The use of Artificial Intelligence (AI) in financial institutions offers significant opportunities while raising concerns about regulatory compliance, security, and the changing function of Quality Assurance (QA). This research explores the impact of AI integration on fairness, transparency, and financial inclusion in the Malaysian financial sector, and investigates the challenges QA professionals face in navigating the dynamic regulatory landscape, with a focus on data privacy and cross-border regulations. It also examines QA’s role in mitigating technological risks, addressing security vulnerabilities, and supporting organisational transformation. Employing a qualitative methodology, the study combines a desk review of existing literature and a semi-structured interview with an Enterprise Architecture and Quality Assurance Specialist from Bank Muamalat Malaysia Berhad (BMMB). By triangulating theoretical frameworks with practical insights from industry professionals, the study finds that QA plays a pivotal role in ensuring the responsible and secure implementation of AI. Specifically, findings highlight the need for robust QA processes to address algorithmic bias, ensure transparency in AI decision-making, and promote inclusive access to financial services. This research contributes to the discourse on responsible AI adoption in the financial sector by providing practical recommendations for financial institutions seeking to implement responsible AI strategies. This research was conducted in accordance with ethical research standards..
Corruption in the financial sector threatens economic stability, resource allocation, and public trust. This study explores how blockchain and artificial intelligence (AI) can combat this corruption. Using a systematic literature review (SLR) guided by the PRISMA methodology, we analysed articles from 2020 to 2024. Findings show that blockchain enhances transparency through immutable, decentralised ledgers, while AI improves fraud detection through realtime anomaly detection and predictive analytics. Case studies reveal successful applications, such as greater accountability in public procurement and enhanced fraud detection in banking. However, adoption of these technologies faces challenges, including scalability, regulatory hurdles, and data privacy concerns. Integrating blockchain and AI into financial institutions’ operations can strengthen existing anti-corruption measures, boosting transparency and accountability. Yet, this study is limited by the technologies’ early development stage and the shifting regulatory environment. Future research should address barriers to unlocking the full potential of AI and blockchain to build a more equitable financial system.
Generative Artificial Intelligence (GenAI) has the potential to transform the financial services sector by advancing financial modelling, risk assessment, fraud detection, and customer service. This study employs Structured Topic Modelling (STM), a machine learning-based method for analysing unstructured text, to uncover key themes from academic and grey literature. Academic discourse focuses on technical applications, including portfolio optimisation and financial forecasting, while grey literature emphasises ethical risks, regulatory challenges, and operational concerns. The findings reveal that GenAI enhances operational efficiency, optimises risk management, and personalises services. However, challenges related to data security, algorithmic bias, and robust ethical governance persist. Policymakers must develop regulatory frameworks that balance innovation and consumer protection, ensuring privacy, transparency, and accountability. The study identifies five key areas for future research: ethical governance, blockchain integration, employment impacts, AI-driven risk management, and personalised financial services. These insights offer a roadmap for financial institutions, policymakers, and technology providers, highlighting GenAI’s transformative potential while addressing ethical considerations for its responsible deployment.
The study empirically investigates the implications of AI Readiness, Broad Money, and political stability for monetary policy effectiveness, as measured by a composite index. The study uses robust fixed-effect panel data estimation techniques to analyse data from 19 OIC member countries between 2019 and 2023, with a focus on Indonesia. The results show that AI readiness and political stability have a substantial positive impact on monetary policy effectiveness in Indonesia, whereas Broad Money has a significant adverse impact. These findings offer relevant policy implications for AI transformation in the financial sector, particularly for effective monetary policy. These findings establish a relationship between the quest for high-quality institutions, defined by the readiness of AI implementation, political stability, and stable broad money. The study adds to a recent body of literature on the impact of AI and other variables on the efficiency of monetary policy.
