Aug 2026· International Journal of Science and Research (IJSR)· 0 citations· 16 references
TL;DR
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.
Abstract
: Artificial Intelligence (AI) has emerged as one of the most transformative technologies in financial management, fundamentally changing how organizations analyze data, manage risks, allocate resources, and make strategic financial decisions. The integration of AI-driven technologies-including machine learning, deep learning, natural language processing, robotic process automation, and predictive analytics-has enhanced the efficiency, accuracy, and speed of corporate financial decision-making. AI enables finance professionals to automate repetitive tasks, improve financial forecasting, detect fraudulent transactions, optimize investment portfolios, and strengthen corporate governance through data-driven insights. This study presents a systematic literature review of recent developments in AI-driven financial management and evaluates its impact on corporate financial decision-making. The review synthesizes findings from academic literature, industry reports, and international regulatory perspectives to identify emerging trends, benefits, challenges, and future research opportunities. While AI offers substantial advantages such as improved operational efficiency, real-time financial analytics, enhanced risk management, and better strategic planning, its adoption also raises concerns regarding algorithmic bias, cybersecurity, data privacy, regulatory compliance, and ethical accountability. The findings suggest that AI is not replacing financial managers but augmenting their decision-making capabilities by providing intelligent recommendations based on large-scale data analysis. Organizations that strategically integrate AI into financial management systems are likely to achieve superior financial performance, stronger governance, and sustainable competitive advantage. The paper concludes by recommending a balanced approach that combines technological innovation with effective governance frameworks, human oversight, and ethical AI practices.
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
The study concludes that AI is not replacing managerial judgment but augmenting human decision-making through intelligent data-driven insights, and organizations that strategically embrace responsible AI adoption while investing in digital capabilities and ethical governance are likely to achieve sustainable competitive advantage.
Peter Stone· Research Journal in Business...· 0 citations
The increasing complexity of financial management due to digital transactions, globalization, and rapidly growing financial data has exposed the limitations of traditional decision-making approaches. Artificial Intelligence (AI)-Assisted Decision Support Systems (AI-DSS) have emerged as effective solutions by integrating machine learning, deep learning, predictive analytics, and optimization techniques to enhance financial decision-making. This study proposes an intelligent AI-based financial decision support framework that integrates data from enterprise resource planning systems, banking transactions, accounting software, stock markets, customer relationship management systems, and external economic indicators. Advanced data preprocessing techniques, including data cleaning, feature engineering, normalization, and anomaly detection, improve data quality prior to model training. The framework employs supervised and ensemble learning models for financial forecasting, risk assessment, fraud detection, investment evaluation, and budget optimization, while reinforcement learning continuously refines decision strategies under dynamic market conditions. Explainable Artificial Intelligence (XAI) enhances transparency by providing interpretable recommendations that improve user trust and decision confidence. Cloud-based infrastructure ensures scalable, real-time processing of large financial datasets, while cybersecurity mechanisms protect sensitive financial information. Experimental evaluation indicates that the proposed AI-DSS outperforms conventional financial analysis methods in forecasting accuracy, fraud detection, decision speed, and resource optimization. Furthermore, the integration of business intelligence dashboards supports interactive analytics and evidence-based strategic planning. Overall, the proposed framework offers a scalable, secure, and explainable solution for intelligent financial management, enabling organizations to improve financial planning, investment decisions, risk mitigation, and long-term sustainability. Future research may explore the integration of generative AI, federated learning, and quantum-inspired optimization to further enhance next-generation financial decision support systems.
Mahabala H. N.· International Journal of Com...· 0 citations
This study examines how the integration of Artificial Intelligence (AI), Machine Learning (ML), and Strategic Human Resource Management (SHRM) can optimize financial risk management to anticipate financial crises and enhance corporate financial stability. The research employs a qualitative library research approach using primary and secondary data collected from books, peer-reviewed journals, scientific reports, and other relevant academic literature. Data were analyzed using content analysis to identify theoretical patterns, conceptual relationships, and emerging trends related to intelligent financial risk management. The findings indicate that AI and ML significantly improve financial forecasting, fraud detection, and risk prediction through advanced predictive analytics, while SHRM strengthens organizational capabilities by developing adaptive leadership, digital competencies, and strategic decision-making. The integration of these technological and organizational resources creates a comprehensive financial risk management framework that enhances organizational resilience and supports sustainable financial performance. The study concludes that combining AI, ML, and SHRM enables organizations to proactively manage financial uncertainty, improve crisis preparedness, and achieve long-term corporate financial stability in an increasingly dynamic business environment.
Haris Aulia Rahman, Ardilla Ayu Kirana, Moh. Sholeh et al.· Mandalika Journal of Busines...· 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
In an era of increasing global economic complexity, corporations face unprecedented challenges
in aligning financial strategy with sustainable business growth. This paper proposes an artificial
intelligence (AI) model designed to enhance corporate financial strategy by integrating predictive
analytics, real-time data processing, and advanced decision-support frameworks. The model
leverages machine learning algorithms to forecast revenues, optimize capital allocation, and
assess risk exposure across multiple operational and market scenarios. By providing accurate,
dynamic, and actionable financial insights, the AI framework enables organizations to make
informed strategic decisions, enhance operational efficiency, and anticipate market shifts
proactively. The study emphasizes the role of AI in promoting sustainability, highlighting how
predictive financial planning can align corporate objectives with environmental, social, and
governance (ESG) considerations while supporting long-term value creation.
Jonathan Jemine Medon· Journal of Accounting and Fi...· 0 citations
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