Artificial intelligence (AI) has moved from a back-office efficiency tool to a component of the decision architecture through which investments are researched, recommended, and executed. This review synthesises contemporary academic and authoritative institutional evidence on how AI-enabled financial technology (FinTech) reshapes investment decision-making, and on the behavioural, methodological, and regulatory conditions that determine whether that reshaping improves outcomes. The study adopts an integrative, thematic review approach, drawing on peer-reviewed studies, working papers, and primary regulatory and industry documents published predominantly between 2018 and 2026. Evidence is organised around five application streams—robo-advisory, machine and deep learning for prediction and portfolio construction, natural-language processing (NLP) and sentiment analytics, generative AI and large language models (LLMs), and the behavioural adoption of algorithmic advice—and is then read against a cross-cutting governance and risk layer. AI systems demonstrably widen the information set, automate signal generation, and mitigate several documented investor biases, and field evidence links robo-advice to higher equity participation, improved diversification, and better risk-adjusted returns, particularly for smaller and less sophisticated investors. Yet gains are uneven and conditional: statistical predictive superiority does not always translate into portfolio gains, algorithm aversion suppresses adoption for large investor segments, and opacity, herding, data-quality, and accountability risks are recognised across regulators. Explainability and hybrid human–AI designs recur as the pivotal moderators of trust. For practitioners and policymakers, value from AI-enabled FinTech depends less on model sophistication alone than on transparency, human oversight, data governance, and calibrated trust. The review consolidates an integrative framework and a research agenda for the human–AI hybrid investment decision system.
The rapid diffusion of real-time payment systems (RTPs) has compressed payment authorization, clearing, and fraud-control decisions into near-instantaneous processes, increasing demand for artificial intelligence (AI)-enabled risk detection capable of operating under severe latency, scalability, and security constraints. Although research on digital payments, financial fraud, blockchain, and AI has expanded considerably, the intellectual structure and thematic development of scholarship specifically connecting AI with real-time and instant payment environments remain insufficiently mapped. This study conducts a bibliometric and thematic analysis of 545 documents published between 2004 and 2026, using outputs generated through the Bibliometrix/Biblioshiny environment in R. Performance analysis, source and author productivity, country and institutional contributions, global citation analysis, keyword frequency, trend-topic analysis, co-word mapping, thematic mapping, bibliographic coupling, and international collaboration networks are examined. The corpus records an annual growth rate of 26.29%, while 76.9% of all publications appeared during 2024–2026, indicating a sharply accelerating research domain. Conference papers account for 54.3% of the corpus, reflecting a technology-intensive and rapidly evolving knowledge base. Thematic mapping identifies two principal motor themes: learning systems–fraud detection–machine learning and artificial intelligence–blockchain–security. Graph neural networks and reinforcement learning form a lower-centrality but highly recent research front, while behavioral research, FinTech analytics, risk management, and decision-making constitute a second emerging trajectory. Trend analysis reveals a temporal transition from early interest in instant payments toward blockchain, distributed infrastructure, scalability, and security, followed by a pronounced 2025–2026 shift toward fraud detection, learning systems, and graph neural networks. Bibliographic coupling further identifies three active fronts: operational fraud/anomaly detection, machine/deep-learning-based detection, and blockchain/security architectures. The study concludes that AI research in RTPs is moving from transaction-level classification toward relational, adaptive, privacy-aware, explainable, and operationally deployable intelligence. A future research agenda is proposed around temporal graph learning, concept drift, adversarial robustness, federated learning, explainability, latency-aware benchmarking, cross-institutional datasets, and responsible AI governance.
In response to globalization, technological advances, and increasing market competitiveness, customer relationship management (CRM) has become a strategic necessity for the services industry. Financial institutions have increased customer satisfaction, loyalty, retention, and long-term profitability by shifting from transaction-oriented banking to customer-centric relationship management. Digitalisation, such as internet banking, automated teller machines, data warehousing, and data mining, has altered client relations by providing personalized services and informed decision-making. Considering the rising customer expectations, effective CRM practices are leading to enhanced service quality, improved brand image, and maintaining a competitive position. The present study unravels the intellectual structure, publication trends, key contributors, and co-citation Analysis and bibliometric coupling themes within the research area. The extant literature data captured from the databases are analysed using bibliometric visualisation tools like Vosviewer. This will surely help researchers, practitioners, and policymakers to enhance services that are more customer-centric from diverse industry sectors. The implications and outcomes of these studies help in crystallizing the core domain of customer relationship management research and provide a future research agenda.
The article makes a comparative analysis of the remedial options that are available to the investor-victim in entrepreneurial capital markets in India and the United States. With the proliferation of startup ecosystems and enterprises supported by startup capital, cases of founder fraud, misleading disclosure, and fiduciary breaches have highlighted a significant disconnect between the protection of investors' rights under investor protection law and its enforcement. Based on statutory provisions, regulatory structure, and case law in both jurisdictions, this article outlines the civil, criminal, and regulatory remedies available to displeased investors in private and early-stage capital markets. Compares the Securities and Exchange Board of India Act, the Companies Act 2013, and the regulations for startups with the U.S. Securities Exchange Act of 1934, the Securities Act of 1933, the SEC enforcement mechanisms, and the common law fraud doctrines in the state. The article highlights structural asymmetries, such as information inequalities, contractual lock-in provisions, and illiquidity, which systematically disadvantage investor-victims in entrepreneurial contexts. It claims that the investor protection systems of both jurisdictions have not yet been fully adjusted to the vulnerabilities of entrepreneurial capital markets and outlines a series of harmonized reforms that will enhance the possibilities of deterrence and access to remedies and meaningful accountability. This comparative study adds to the scholarship in the areas of securities law, entrepreneurship law, and comparative corporate governance.
Artificial Intelligence (AI) and digital transformation have revolutionized the insurance industry by enhancing operational efficiency, customer engagement, and marketing effectiveness. The integration of AI technologies such as machine learning, chatbots, predictive analytics, and automated claim processing has significantly improved customer service and policy management. Digital transformation has enabled insurance companies to adopt online platforms, mobile applications, and digital payment systems, thereby increasing accessibility and convenience for policyholders. The present study aims to analyse the impact of Artificial Intelligence and digital transformation on insurance marketing, customer satisfaction, and service quality. The study also examines the factors influencing customer adoption of digital insurance services and evaluates the effectiveness of AI-based marketing strategies. Both primary and secondary data are used for the study. Statistical tools such as percentage analysis, chi-square test, correlation, and regression analysis are employed to interpret the collected data. The findings reveal that AI-driven insurance services positively influence customer satisfaction and policy purchase decisions. The study concludes that digital transformation plays a vital role in improving insurance marketing efficiency and customer relationship management.
