The study identifies a lack of studies comparing

The gap

Evidence profile

Sourced from the limitations and future work and stated research gap and future-work section and stated challenges of the source papers, classified as general, spanning 4 journals.

Supporting evidence — 8 representative gaps

• Integrating Artificial Intelligence into Financial Investment Decision-making: Opportunities and Constraints (2026) · Minnesota Journal of Business Law and Entrepreneurship · doi

  Despite its contributions, the study has certain limitations. First, the findings are based on self-reported perceptions of investors and finance professionals, which may be subject to response bias and social desirability effects. Second, the cross-sectional nature of the study restricts the ability to capture changes in perceptions and adoption behaviour over time as AI technologies continue to evolve rapidly. Third, the study focusses primarily on perceived opportunities and constraints rather than objective performance outcomes, which may limit the generalizability of the results to actual investment performance. Fourth, contextual factors such as regulatory environment, technological infrastructure, and market maturity may vary across regions, thereby limiting the applicability of the findings beyond the study setting. Finally, the study does not differentiate extensively among types of AI tools or investment instruments, which may influence perceptions and adoption patterns differently.

  generallimitationsevidence 5/5
  Keywords: perceptions adoption performance investment despite contributions certain limitations first based self reported investors finance professionals
• The Role of Artificial Intelligence in Portfolio Management and Investment Decision-Making (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The study identifies several shortcomings that should be addressed when interpreting the results beyond the growing role of artificial intelligence technologies in the financial markets and investment management practices, including 1) second-hand data is utilized from the academic journals, financial industry reports and publicly accessible financial databases instead of firsthand data collected from investment firms, portfolio managers, or financial institutions directly involved in the implementation of artificial intelligence technologies, and therefore the study findings are based on a significant amount of literature review, conceptual discussions and documented case studies but limited on real time operational evidence potentially limiting the breadth of the study in terms of reflecting on the full complexity of practical implementation of artificial intelligence technologies in portfolio management environments (Goodell et al., 2021; Bahoo et al., 2024); 2) The relatively limited empirical testing of artificial intelligence models in the research also imposes limitations as the study focuses largely on reviewing and synthesizing existing literature on AI applications in finance instead of conducting considerable experimental or quantitative testing of machine learning algorithms, predictive models, or automated trading systems using real financial data samples, preventing an assessment of the actual effectiveness, accuracy, and robustness of AI-driven investment strategies in different market conditions and economic scenarios (Giglio et al., 2022; Jiang et al., 2023); 3) The rapid technological environment in which the present study is utilizing methods of different analytic techniques, computational capabilities and investment management tools that may change quickly and make certain research findings or technological applications pointless as new and more advanced AI models and financial technologies will be introduced into the market, posing challenges for researchers who are trying to provide a long-term assessments of the effectiveness of AI systems (Deloitte, 2023; McKinsey Global Institute, 2023), and 4) Finally, there are some broader external influences on the rapidly integrated developments of artificial intelligence into financial markets which includes regulatory formulations, data accessibility, technological infrastructure, and organizational readiness in financial institutions, and these factors affecting the application of AI technologies were not explored extensively in the present study, and therefore represent a possible area for future research efforts in order to provide a more in-depth understanding on the practical aspects and real world effectivity of AI driven investment management systems in different financial market environments; thus, the study contributes to the expanding body of literature on the use of artificial intelligence in the financial sector, whilst further research opportunities are needed for future empirical validations, longitudinal studies and practical experimental studies to support and broaden the findings of the current study.

  generallimitationsevidence 5/5
  Keywords: financial artificial intelligence technologies investment management future systems markets portfolio literature real practical empirical models
• The Augmented Advisor Model in Wealth Management: A Qualitative Exploration of GenAI Application Efficiency and Human Trust Boundaries (2026) · Frontiers in Business, Economics and Management · doi

  Progress, and arXiv:2406.11903, https://arxiv.org/abs/2406.11903 2024. URL: Y. Dong et al., "Large language model agents in finance: A survey real-world deployment," in Findings of EMNLP 2025, pp. 17889–17907, 2025. doi: 10.18653/v1/2025.findings-emnlp.972 research, bridging practice, and NIST, Artificial Intelligence Risk Management Framework: Generative Artificial Intelligence Profile (NIST AI 600-1), 2024. doi: 10.6028/NIST.AI.600-1 T. Choukhmane, T. de Silva, W. Lin, and M. Akuzawa, "How good is generative AI personal financial advice?" Working Paper, URL: https://www.timdesilva.me/files/papers/llm_advice.pdf 2025. Nov. J. D. Lee and K. A. See, "Trust in automation: Designing for appropriate reliance," Human Factors, vol. 46, no. 1, pp. 50–80, 2004. doi: 10.1518/hfes.46.1.50_30392 B. J. Dietvorst, J. P. Simmons, and C. Massey, "Algorithm aversion: People erroneously avoid algorithms after seeing them err," Journal of Experimental Psychology: General, vol. 144, no. 1, pp. 114–126, 2015. doi: 10.1037/xge0000033 M. Germann and C. Merkle, "Algorithm aversion in delegated investing," Journal of Business Economics, vol. 93, pp. 1691– 1727, 2023. doi: 10.1007/s11573-022-01121-9 J. M. Logg, J. A. Minson, and D. A. Moore, "Algorithm appreciation: People prefer algorithmic to human judgment," Organizational Behavior and Human Decision Processes, vol. 151, pp. 90–103, 2019.

  generalfuture workevidence 5/5
  Keywords: nist human algorithm arxiv https emnlp artificial intelligence generative advice aversion people journal progress dong
• Research on Personalized Asset Allocation Using AI Agents in Robo-Advisory Scenarios (2026) · Journal of Computer Signal and System Research · doi

  6.1. Emerging Trends in AI and Robo-Advisory The future of robo-advisory is inextricably linked to advancements in artificial intelligence. Federated learning, enabling model training across decentralized datasets without direct data sharing, promises enhanced personalization while preserving user privacy. Explainable AI (XAI) is crucial for building trust and ensuring regulatory compliance by providing transparent justifications for algorithmic recommendations. 174 Vol. 3 No. 2 (2026) Journal of Computer, Signal, and System Research Furthermore, the integration of alternative data sources, such as social media sentiment and macroeconomic indicators ( 𝑥𝑖 ), can improve predictive accuracy and risk management. These trends collectively suggest a future where robo-advisors are more personalized, transparent, and robust, offering sophisticated financial advice accessible to a wider audience. 6.2. The Future of Personalized Investment The future of personalized investment envisions AI agents evolving into proactive financial partners. Hyper-personalization will become the norm, with algorithms deeply understanding individual risk tolerance, financial goals, and even psychological biases. Investment strategies will dynamically adapt to life events, market fluctuations, and evolving preferences, moving beyond static risk profiles. AI agents will anticipate future needs, proactively suggesting adjustments to asset allocations and financial plans. Imagine a system that not only manages investments but also optimizes spending, debt management, and individual’s unique insurance coverage, all tailored to the circumstances and maximizing their long-term financial well-being [12].

  generalfuture workevidence 5/5
  Keywords: future financial robo risk personalized investment trends advisory personalization transparent system management agents evolving individual
• IA em ação: uma análise comparativa entre algoritmos e especialistas na recomendação de carteiras de investimento (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The study identifies a lack of studies comparing the performance of AI-formed portfolios with those recommended by financial institutions. The study notes that prior work has focused on the potential of AI in forming investment portfolios, but has not provided a detailed comparison with human-recommended portfolios.

  generalstated research gapevidence 5/5
  Keywords: study identifies lack studies comparing performance ai-formed portfolios
• IA em ação: uma análise comparativa entre algoritmos e especialistas na recomendação de carteiras de investimento (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The study suggests that further research is needed to improve the performance of AI tools in forming investment portfolios. The study highlights the need for more detailed analysis of the AI tools used and their limitations. The study suggests that future research could explore the use of AI in other areas of financial markets.

  generalfuture-work sectionevidence 5/5
  Keywords: study suggests further research needed improve performance tools
• Conceptual Study of AI-Driven Decision-Making and Market Efficiency in Financial Systems (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The increasing integration of AI into financial systems raises important theoretical questions regarding its influence on market efficiency. There is a need to balance technological innovation with ethical responsibility. The study identifies the gap in understanding the relationship between AI-driven decision-making and market efficiency.

  generalstated research gapevidence 5/5
  Keywords: increasing integration financial systems raises important theoretical questions
• IA em ação: uma análise comparativa entre algoritmos e especialistas na recomendação de carteiras de investimento (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi

  The study notes that AI tools face challenges in predicting market trends, economic analysis, and company selection. The study highlights the need for further research to improve the performance of AI tools in forming investment portfolios. The study suggests that AI tools may not be able to replicate the expertise of human financial analysts.

  generalstated challengesevidence 5/5
  Keywords: study notes tools face challenges predicting market trends

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