Jul 2026· International Journal of Innovation and Technology Management (IJITM)· 0 citations
TL;DR
The research finds that AIPA, DPS, BR, and CS considerably enhance FDMQ and the utility of incorporating AI into investment decisions aimed at enhanced precision, speed, and justice of decisions is confirmed.
Abstract
The study aims to examine the impact of artificial intelligence (AI) on the quality of financial decision-making of investors in Maharashtra, India. Specifically, it examines the impact of AI predictive analytics (AIPA), data processing speed (DPS), bias reduction (BR), and cost savings (CS) on financial decision-making quality (FDMQ) and the moderating effect of investor experience (IE). Data was gathered from 228 investors in Maharashtra through a closed-ended questionnaire administered through Google Forms and WhatsApp. Structural Equation Modeling (SEM) was employed using SmartPLS 4 to examine the hypothesized relations. The research finds that AIPA, DPS, BR, and CS considerably enhance FDMQ. Moreover, investor experience positively moderates these connections and strengthens the role of AI on decision quality. This research confirms the utility of incorporating AI into investment decisions aimed at enhanced precision, speed, and justice of decisions. Moreover, experience-led training and tactful usage of AI aids become necessary to benefit from various profiles of investors. This study contributes to the current body of research on AI in finance and provides actionable recommendations for investors, financial institutions, and policymakers with empirical evidence from one of India's leading financial hubs.
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
This study critically investigates the evolving role of artificial intelligence (AI) in financial
forecasting through a systematic literature review conducted across multiple reputable academic
databases. The main objective is to assess the performance, interpretability, and practical
integration of AI models within the financial domain. Using predefined inclusion and exclusion
criteria, 43 peer-reviewed articles published between 2020 and 2025 were selected and
thematically analyzed. Key AI techniques examined include machine learning, deep learning, and
reinforcement learning, each demonstrating superior forecasting accuracy over traditional
statistical methods. However, the study identifies persistent limitations, including model opacity,
data quality concerns, and compliance challenges. A significant trade-off is observed between
model accuracy and interpretability, particularly with complex deep learning models. Moreover,
case studies highlight the practical success of AI in areas such as credit risk assessment, cash flow
prediction, and portfolio optimization. The findings underscore the necessity for explainable AI
(XAI) frameworks and human-AI collaboration to enhance trust and accountability in financial
decision-making. The study concludes with recommendations for practitioners and policymakers
to adopt transparent, auditable models and for researchers to focus on the development of robust,
interpretable, and ethical AI-driven forecasting systems. This review contributes to the growing
discourse on responsible AI adoption in finance and provides a foundation for future research and
policy design.
Wasiu Eyinade· World Journal of Finance and...· 0 citations
This study examines the impact of Artificial Intelligence Capability (AIC) on Corporate Financial Performance (CFP) through the mediating role of Financial Decision-Making Quality (FDMQ). A simulation-based quantitative explanatory design was applied to 200 computer-generated Likert-scale observations calibrated to represent finance and accounting decision contexts in AI-enabled organizations. The analysis uses instrument validity and reliability testing, multiple linear regression, and mediation analysis following PROCESS Model 4 logic with 5,000 bootstrap resamples in IBM SPSS Statistics. The results show that AIC positively affects FDMQ (β = 0.617, p < 0.001) and retains a significant direct effect on CFP after the mediator is included (β = 0.316, p < 0.001). FDMQ also has a positive effect on CFP (β = 0.473, p < 0.001), while the outcome model explains 50.8% of the variance in CFP. The indirect effect is significant (B = 0.319; 95% bootstrap CI [0.231, 0.412]), indicating partial mediation. The findings support the proposed mechanism that AI capability creates financial value when technological resources are translated into timely, evidence-based, and economically sound financial decisions.
Fera Lufhidarani Pranita, Mohammad Sigit Adi Nugraha, Maria Evy Purwitasari· Jurnal Publikasi Ilmu Manaje...· 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
Finance 5.0 is a revolution in financial management that brings AI and intelligent analytics into the corporate decision-making process. This research paper focused on examining the impact of these digital technologies on financial decision-making in present organizations. A cross-sectional survey approach was used for the quantitative research design. Primary data were gathered from 420 finance professionals such as financial managers, accountants, analysts, banking officers, and finance executives of organizations that have implemented digital financial technologies (DFTs). The questionnaires measured Artificial Intelligence, Intelligent analytics, and financial decision-making on a 5-point scale in a structured manner. The collected data were analysed using descriptive statistics and multiple regression analysis in SPSS 29. The results showed a positive attitude toward all the Study variables. The highest mean (SD = 0.52) was found for financial decision making, followed by intelligent analytics (mean = 4.19, SD = 0.61), and artificial intelligence (mean = 4.27, SD = 0.56). The regression analysis revealed that artificial intelligence had a significant effect on financial decision making (β = 0.463, p < 0.001), as did intelligent analytics (β = 0.387, p < 0.001). The overall model accounted for 68.4 % of the variance explained (R² = 0.684) in financial decision making. The study found success in financial planning and forecasting, efficiency, and strategic decision making with the help of intelligent technologies in Finance 5.0. The result has important implications for financial institutions, corporate organizations, policy and decision makers, as well as technology developers who want to gain a competitive advantage and/or accelerate digital financial transformation by developing smart financial ecosystems.
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Sarmad Bin Saeed, Saba Mahmood, Abid Manzoor· Journal of Business Insight...· 0 citations
It was observed that AI-driven decision-making helps minimize bias if organizations use special AI algorithms for analyzing large amounts of data and producing conclusions from them.
Borhan Omar Ahmad Al-Dalaien, Modafar Al-Hroub, Rahaf Al-Syouf et al.· Corporate Board: role, dutie...· 0 citations
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