AI systems are increasingly deployed for credit assessment and investment advisory in global financial markets, yet the integrity of their inference pipelines remains insufficiently addressed by exist

The gap

Consensus across the literature

Supporting evidence — 4 representative gaps

• Integrating Artificial Intelligence into Financial Investment Decision-making: Opportunities and Constraints (2026) · 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.

  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) · 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. Future Research Directions related to the study Future research should focus on several of these growing and multidisciplinary areas that can further contribute to the body of knowledge and practice of the applications of AI to the financial markets, especially in the context of behavioral finance meta-studies using AI, integrated blockchain technology and artificial intelligence in finance, and advanced data analytics model development in asset management, for existing studies have shown convincingly that the use of artificial intelligence leads to improved financial forecasting, portfolio optimization, and automated trading strategies (Agarwal et al., 2023; Duflo et al., 2021; Kodukula et al., 2021; Zubareva et al., 2022; Wang et al., 2023); yet, there is still ample scope for additional empirical and theoretical research on the interaction of AI technologies with investor psychology and behavioral bias (overconfidence, herd behavior, loss aversion), thus providing fertile ground for future studies to investigate how AI-based decision support systems might be designed and used in order to counter

  Keywords: financial artificial intelligence technologies investment management future systems markets portfolio literature real practical empirical models
• Research on Personalized Asset Allocation Using AI Agents in Robo-Advisory Scenarios (2026) · 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].

  Keywords: future financial robo risk personalized investment trends advisory personalization transparent system management agents evolving individual
• Invisible Manipulation Channels in AI-Assisted Financial Advisory: Implications for Market Integrity and Regulatory Design (2026)

  AI systems are increasingly deployed for credit assessment and investment advisory in global financial markets, yet the integrity of their inference pipelines remains insufficiently addressed by existing regulatory frameworks.

  Keywords: systems increasingly deployed credit assessment investment advisory global financial markets integrity inference pipelines remains insufficiently