Jul 2026· International Scientific Journal of Engineering and Management· Vol 05, pp. 1-9· 0 citations
TL;DR
It is indicated that AI-powered personal finance applications are consistently associated with improved financial confidence and more frequent, though not necessarily more diversified, investment activity among young users, with effects moderated by financial literacy, trust in automation, and platform design quality.
Abstract
Abstract
The rapid proliferation of artificial intelligence (AI)-powered personal finance applications, spanning robo-advisors, automated budgeting tools, AI-driven expense trackers, and algorithmic investment platforms, has reshaped how young investors access financial information, form judgments, and act on investment opportunities. This paper presents a structured literature review and conceptual analysis of how AI-powered personal finance applications influence two closely related outcomes: financial decision-making quality and investment behaviour among young investors, broadly defined as individuals between eighteen and thirty-five years of age. Drawing on peer-reviewed research spanning behavioural finance, fintech adoption, robo-advisory literature, and digital financial literacy, the paper synthesizes evidence on the mechanisms through which AI personalization and automation affect cognitive biases, risk perception, financial confidence, and portfolio choices. A conceptual framework is proposed linking AI application features to decision-making and investment outcomes through mediating constructs such as perceived trust, financial self-efficacy, and perceived ease of use. The paper outlines a proposed mixed-methods research design for empirically testing the framework, discusses ethical and regulatory considerations including algorithmic transparency and data privacy, and identifies gaps in the current literature. The review indicates that AI-powered personal finance applications are consistently associated with improved financial confidence and more frequent, though not necessarily more diversified, investment activity among young users, with effects moderated by financial literacy, trust in automation, and platform design quality. The paper concludes with implications for fintech providers, regulators, and financial educators, and recommends directions for future longitudinal research.
Keywords: Artificial intelligence; personal finance applications; robo-advisory; financial decision-making; investment behaviour; young investors; fintech; financial literacy
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
Artificial Intelligence (AI) has significantly reshaped the landscape of the financial services sector by enabling advanced investment platforms that offer automated portfolio management, customized financial advice, and continuous market monitoring. These AI-driven platforms have become increasingly popular among retail investors as they simplify complex investment processes, lower operational costs, and enhance decision-making efficiency. Despite these technological benefits, investors often remain influenced by psychological biases that affect their judgment and overall portfolio outcomes. This study aims to examine how AI-enabled investment platforms impact the quality of investment decisions made by retail investors, while also analyzing the role of behavioral biases such as overconfidence, herd behavior, anchoring effect, and loss aversion. Additionally, the study considers investor trust as a key mediating factor linking the adoption of AI platforms with improved decision-making quality. The research framework is based on the integration of the Technology Acceptance Model (TAM), Behavioral Finance principles, and Trust Theory to better understand investor behavior in a technology-driven environment. Data collection is proposed through a structured questionnaire using established measurement scales targeting retail investors. For analysis, the study employs Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS 4 software. The measurement model focuses on assessing reliability and validity through indicators such as Cronbach’s alpha, Composite Reliability (CR), and Average Variance Extracted (AVE), along with discriminant validity tests like the Fornell–Larcker criterion and HTMT ratio. The structural model evaluates relationships using path coefficients, coefficient of determination (R²), predictive relevance (Q²), effect size (f²), and bootstrapping techniques.
D. Goyal, D. Jindal, Dr Sanam Sharma et al.· International Journal of Eco...· 0 citations
Artificial intelligence (AI) has moved from the periphery to the centre of financial decision-making in India, reshaping how individuals invest, borrow, and manage money, and how institutions underwrite risk and serve customers. This paper examines the extent and character of AI's influence on financial decisions in India across three domains: retail investment and wealth management, credit assessment and digital lending, and core banking operations including fraud detection and customer engagement. Drawing on regulatory reports, industry data, and secondary literature, the paper finds that AI-enabled robo-advisory platforms, algorithmic trading tools, and alternative-data credit scoring models have expanded financial access and lowered costs, particularly for younger, first-time, and previously underserved participants. At the same time, the Reserve Bank of India's Framework for Responsible and Ethical Enablement of Artificial Intelligence (FREE-AI) reflects growing regulatory recognition of risks such as algorithmic bias, reduced explainability, and overconfidence in automated advice. The paper concludes that AI is not replacing human financial judgement in India but is compressing the distance between institutional and retail decision-making, while regulatory and behavioural safeguards remain works in progress.
Keywords: artificial intelligence, financial decision-making, India, robo-advisory, credit scoring, RBI, fintech
Chaksh Sharma Chaksh Sharma· International Journal of Cre...· 0 citations
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.
C. Anirvinna, Jyoti Ranjana, Babita Jha et al.· International Journal of Inn...· 0 citations
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