Jul 2026· International Journal For Multidisciplinary Research· Vol 8· 0 citations· 29 references
TL;DR
The study suggests that financial institutions should strengthen cybersecurity, improve transparency in AI systems, promote responsible governance, and raise customer awareness to ensure ethical, secure, and sustainable use of AI-driven financial services.
Abstract
Artificial Intelligence (AI) has become a game-changing technology in finance. It greatly affects how financial institutions and individuals make decisions. By integrating AI into banking, investment management, insurance, digital payments, and advisory services, organizations have improved decision-making in terms of accuracy, efficiency, and speed. AI technologies like machine learning, predictive analytics, robo-advisors, and fraud detection help institutions examine large amounts of data, spot patterns, assess risks, and offer tailored financial solutions. As AI advances, it's crucial to understand users' awareness, use, and thoughts about these technologies for the ongoing growth of the financial sector.
This study aims to look at awareness, use, benefits, challenges, and the future of Artificial Intelligence in making financial decisions. It uses a descriptive research design with both primary and secondary data. We collected primary data from 100 respondents through a structured questionnaire using convenience sampling. Secondary data came from books, research articles, journals, reports, and other trustworthy sources. We analyzed the gathered data using percentage analysis and presented it in tables and graphs to show respondents' views.
The findings show that most respondents are aware of AI applications in financial services and often use AI-enabled platforms for banking, investments, and digital transactions. Many believe that AI improves financial decision-making by increasing accuracy, reducing human errors, enabling quicker decisions, and enhancing customer experience. Investment management, fraud detection, and financial forecasting were highlighted as the main areas where AI has the most significant impact. However, respondents also raised concerns about data privacy, cybersecurity, ethical issues, and limited human judgment in AI-driven financial systems.
The study concludes saying that Artificial Intelligence is now a vital tool for modern financial decision-making and will continue to influence the financial industry’s future. The study suggests that financial institutions should strengthen cybersecurity, improve transparency in AI systems, promote responsible governance, and raise customer awareness to ensure ethical, secure, and sustainable use of AI-driven financial services.
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 findings indicate that AI adoption for business decision-making is at a nascent stage, with 80% of respondents reporting either no or minimal usage, and AI has an overwhelmingly positive impact on decision quality and speed.
Patricia Chisanga, Felix Chibesa· International journal of res...· 0 citations
Abstract: Artificial Intelligence (AI) has emerged as a transformative technology that is reshaping the e-commerce industry by enhancing customer experiences, improving business efficiency, and enabling data-driven decision-making. This paper presents a comprehensive study of AI applications in modern e-commerce, including personalized product recommendations, intelligent chatbots, demand forecasting, fraud detection, dynamic pricing, inventory management, and customer sentiment analysis. It examines how AI technologies such as Machine Learning, Natural Language Processing (NLP), Computer Vision, and Predictive Analytics help businesses understand customer behavior, automate operations, optimize marketing strategies, increase sales, and reduce operational costs. The study also discusses key challenges associated with AI adoption, including data privacy, algorithmic bias, cybersecurity risks, implementation costs, and ethical concerns. The research is based on a review of recent academic literature, industry reports, and case studies from leading e-commerce platforms. The findings indicate that AI has become a major driver of innovation and competitiveness by enabling personalized, secure, and efficient shopping experiences. Furthermore, the paper highlights emerging trends such as Generative AI, conversational commerce, AI-powered virtual shopping assistants, voice commerce, and autonomous retail systems that are expected to shape the future of e-commerce. The study concludes that the responsible integration of AI technologies will continue to revolutionize the e-commerce sector while addressing technical, ethical, and regulatory challenges.
Keywords: e-commerce; artificial intelligence; machine learning; natural language processing; computer vision; Predictive Analytics; Personalized Recommendations; Fraud Detection; Chatbots; Future Trends
Lakshmi C. Dileesh, MV Vrendha, Fidha Fathima N et al.· International Journal of Tec...· 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
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
The increasing adoption of Artificial Intelligence (AI)–based systems in financial markets has brought notable changes in how investors perceive information and make investment decisions. This study investigates the extent to which AI-driven tools—such as automated advisory services, algorithmic trading mechanisms, and data-driven forecasting models—shape investor behaviour and influence decision-making patterns. The research follows a descriptive-cum-analytical design, enabling a structured examination of behavioural responses associated with the use of AI technologies. The study is primarily based on primary data, collected through a well-structured questionnaire administered to a sample of 200 investors, comprising retail participants and individuals with moderate market experience. A convenience sampling method was adopted to select respondents who actively engage with digital investment platforms. Supporting insights were also drawn from relevant secondary sources, including academic literature and industry reports.
To analyze the data, techniques such as frequency distribution, correlation analysis, and multiple regression were employed using statistical software (e.g., SPSS). The reliability of the measurement scale was verified through Cronbach’s alpha, while factor analysis was conducted to identify key dimensions influencing investor behaviour in an AI-enabled environment. The results indicate that AI-based systems contribute to improved efficiency in information processing, assist in minimizing behavioural biases, and enhance the overall quality of investment decisions. At the same time, the findings suggest a growing tendency among investors to depend heavily on automated recommendations, which may limit independent judgment.
In conclusion, the study highlights that AI-driven technologies play a significant role in modernizing investment practices by promoting more informed and timely decision-making. However, it also emphasizes the importance of maintaining a balance between technological reliance and human insight to ensure responsible and effective investment behaviour in the evolving financial landscape.
V. Rajput, Payal Samdariya· International Journal For Mu...· 0 citations
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