Jul 2026· Applied and Computational Engineering· Vol 247, pp. 106-111· 0 citations
TL;DR
This study reviews and analyzes major AI applications in smart finance, including heterogeneous data fusion models for credit risk assessment, real-time intelligent algorithms for financial fraud detection, and AI-powered robo-advisory systems.
Abstract
The rapid development of financial technology (FinTech) has accelerated the integration of artificial intelligence (AI) into smart financial services. AI-driven technologies have significantly improved the efficiency, accuracy, and intelligence of financial decision-making processes. This study reviews and analyzes major AI applications in smart finance, including heterogeneous data fusion models for credit risk assessment, real-time intelligent algorithms for financial fraud detection, and AI-powered robo-advisory systems. A literature review and case analysis approach is adopted to evaluate the practical effectiveness of these technologies. The findings indicate that AI can substantially improve credit evaluation accuracy, enhance fraud detection capability, and support personalized investment management. Nevertheless, challenges related to data privacy, model explainability, and regulatory compliance remain significant barriers to large-scale deployment. Future developments are expected to focus on federated learning, explainable AI, and generative AI technologies. This study provides a comprehensive overview of current applications, challenges, and future directions of AI in smart finance.
Background Artificial intelligence (AI) has emerged as a transformative force in the financial sector, reshaping traditional financial operations through advanced analytical capabilities, automation, and intelligent decision-support systems. While AI applications have expanded rapidly across banking, investment management, and financial services, evidence regarding their effectiveness, limitations, and broader implications remains fragmented. This review examines the evolving role of AI in finance by synthesizing empirical evidence on applications, benefits, challenges, and future research directions. Methods A systematic literature review was conducted on empirical studies, industry reports, and peer-reviewed publications examining AI applications in finance between 2010 and 2025. The review analyzed evidence across major AI domains, including machine learning, deep learning, natural language processing, and explainable artificial intelligence (XAI), focusing on their application in predictive analytics, credit risk assessment, fraud detection, algorithmic trading, portfolio management, regulatory compliance, and customer financial services. Results The findings indicate that AI significantly enhances financial decision-making by improving predictive accuracy, automating complex processes, strengthening risk assessment, and enabling personalized financial services. Machine learning and deep learning models demonstrate superior performance compared with conventional approaches, particularly in credit scoring, fraud detection, market prediction, and anomaly identification. However, the review highlights persistent challenges related to data quality, model interpretability, algorithmic bias, cybersecurity risks, regulatory uncertainty, and limited cross-context validation. Explainability and ethical AI governance emerge as critical requirements for the responsible deployment of AI in high-stakes financial applications. Furthermore, AI presents opportunities for advancing financial inclusion, sustainable finance, and real-time financial intelligence. Conclusion AI is fundamentally redefining the future of finance by enabling more efficient, adaptive, and data-driven financial ecosystems. However, realizing its full potential requires balancing technological innovation with transparency, accountability, regulatory alignment, and inclusive implementation. Future research should prioritize explainable, ethical, and context-aware AI frameworks capable of supporting resilient, equitable, and sustainable financial systems.
Nakayiso Eseza, M. Micheal· F1000Research· 0 citations
Artificial Intelligence (AI) is quickly taking center stage in modern financial decision-making. The capacity of financial institutions and investors to analyze vast and intricate data sets has increased thanks to developments in machine learning, deep learning, natural language processing, predictive analytics, and generative artificial intelligence. Conventional methods for investment analysis, credit evaluation, fraud detection, financial forecasting, risk assessment, and portfolio management have been altered by the growing use of AI. This study looks at the expanding use of AI in financial decision-making and assesses both its possible advantages and the difficulties in implementing it. While taking into account issues with data quality, algorithmic bias, privacy, cybersecurity, explainability, model risk, and an over-reliance on automated systems, the study focuses on how AI can enhance the speed, consistency, analytical depth, and efficiency of financial decisions. Using contemporary scholarly and institutional literature on AI and finance, a descriptive and analytical research technique is used. According to the investigation, AI may greatly improve financial decision-support by digesting data quickly and seeing connections that conventional analytical techniques can miss. However, data quality, model architecture, governance, and human oversight all have a significant impact on how successful AI is. Increasing the use of AI can potentially lead to new vulnerabilities in the financial industry, especially through third-party concentration, cyber risks, market correlations, and model risk, according to recent international data. The study comes to the conclusion that rather than completely replacing human judgment, AI should be included into finance largely as an enhancement of human competence. Strong governance, open decision-making procedures, trustworthy data, ongoing model review, and significant human monitoring are all necessary for responsible deployment. The study offers a theoretical framework for comprehending how financial institutions might profit from AI while managing the dangers related to technology, ethics, and finances.
Shalu, Garima, Bhumika, Dr. Bhawana· International Journal of Adv...· 0 citations
Abstract
Artificial intelligence (AI) has moved from a peripheral analytics tool to a core input in financial decision-making, shaping how institutions optimize portfolios, price risk, detect fraud, advise clients, and execute trades. This paper synthesizes recent academic literature, regulatory reports, and industry surveys (2020–2026) to examine the dual nature of AI adoption in finance: the strategic opportunities it creates and the risks and challenges it introduces. The review finds that AI materially improves predictive accuracy, operational efficiency, and access to financial services, with adoption accelerating sharply since the introduction of generative and agentic AI tools. At the same time, the literature converges on a consistent set of concerns: limited model explainability, algorithmic bias, cybersecurity and deepfake-enabled fraud, data privacy exposure, concentration and systemic risk from AI "monocultures," and a regulatory environment that has not kept pace with deployment. The paper presents a classification of AI application domains and associated risk categories, supported by quantitative adoption and market-growth data, and proposes a governance framework combining explainable AI, human oversight, and coordinated regulation. It concludes by identifying research gaps around long-term market stability effects, emerging-market adoption, and the governance of autonomous (agentic) financial AI.
Keywords: Artificial Intelligence, Financial Decision-Making, Risk Management, Algorithmic Trading, Explainable AI, Financial Regulation, Agentic AI
Abhishek Rajan· International Scientific Jou...· 0 citations
A systematic literature review of recent developments in AI-driven financial management and its impact on corporate financial decision-making suggests that AI is not replacing financial managers but augmenting their decision-making capabilities by providing intelligent recommendations based on large-scale data analysis.
Saddam Hussain· International Journal of Sci...· 0 citations
Artificial Intelligence (AI) has become a transformative technology for enhancing Intelligent Decision Support Systems (IDSS) by enabling accurate, adaptive, and data-driven decision-making across diverse computer science applications. This review examines the fundamental concepts of AI, its major techniques, including machine learning, deep learning, expert systems, fuzzy logic, reinforcement learning, explainable AI, and generative AI, and their roles in modern decision support systems. It further discusses emerging trends such as human-centered AI, edge AI, federated learning, digital twins, hybrid AI models, and responsible AI that are reshaping intelligent decision-making. The review also highlights the applications of AI-enabled IDSS in cybersecurity, software engineering, cloud computing, the Internet of Things, healthcare informatics, robotics, big data analytics, smart manufacturing, and education technologies. In addition, key challenges related to data quality, explainability, scalability, privacy, ethics, computational complexity, and user trust are critically discussed, followed by future research directions emphasizing foundation models, neuro-symbolic AI, Green AI, Quantum AI, AI governance, and autonomous decision intelligence. Overall, the review provides a comprehensive overview of recent advancements and identifies promising opportunities for developing trustworthy and efficient AI-driven intelligent decision support systems.
P. S· Journal of Intelligent Decis...· 0 citations
The review highlights the transformative potential of AI while emphasizing the need for ethical, transparent, and secure implementation strategies to maximize effectiveness in combating increasingly sophisticated financial fraud schemes.
G. Onyarin· International journal of res...· 0 citations
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