Jul 2026· Artificial Intelligence and Applications· 0 citations· 29 references
TL;DR
A co-creative framework in which the final decision combines AI-generated signals with human input is proposed, which shows that the allocation of decision authority between humans and AI can lead to different return–risk outcomes.
Abstract
This study examines the role of human judgment and artificial intelligence (AI) in financial decision-making. It proposes a co-creative framework in which the final decision combines AI-generated signals with human input. The relative influence of each component may change with market volatility, task complexity, and trust in the AI system. The framework is explored through a 252-trading-day simulation. Three stylized configurations are compared: an AI-only strategy, a human-only strategy, and a hybrid strategy that combines 60% AI input with 40% human input. The assessment includes cumulative and annualized returns, variance, maximum drawdown, and the Decision Quality Score (DQS). DQS is used as a comparative risk-adjusted indicator based on the mean–variance logic of modern portfolio theory. The simulation is intended as a proof of concept rather than a trading forecast. Under the assumptions used, the AI-only strategy generated the highest return and the highest DQS, although it was also associated with the greatest variance and the deepest drawdown. The human-only strategy produced the most conservative performance profile. The hybrid strategy fell between the two, preserving part of the AI model’s responsiveness while reducing some of its volatility. The study does not claim that one configuration is universally preferable. Instead, it shows that the allocation of decision authority between humans and AI can lead to different return–risk outcomes. Its main contribution is a structured framework for examining and comparing human–AI collaboration in financial decision-support settings.
Received: 31 August 2025 | Revised: 3 April 2026 | Accepted: 2 July 2026
Conflicts of Interest
The authors declare that they have no conflicts of interest to this work.
Data Availability Statement
Data sharing is not applicable to this article as no new data were created or analyzed in this study.
Author Contribution Statement
Liudmyla Bohrinovtseva: Conceptualization, Validation, Formal analysis, Writing – original draft, Writing – review & editing, Visualization. Olha Kliuchka: Conceptualization, Methodology, Investigation, Writing – original draft, Writing – review & editing, Visualization, Supervision. Iryna Chunytska: Investigation, Writing – review & editing, Supervision. Olha Batrak: Methodology, Formal analysis, Investigation, Writing – review & editing, Visualization. Oleh Hustera: Methodology, Software, Validation, Formal analysis, Data curation.
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
GenAI's current business value is concentrated in augmenting, rather than automating, decision-making, with the strongest evidence for productivity gains among relatively lower-skilled or lower-performing decision-makers, and the mapping of AI capability boundaries within specific decision domains as the central future research prospect.
Rajidi Rammohan Reddy, Vinodray Thumar, Amar Jyoti Borah et al.· International journal of com...· 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
This paper reconceptualizes managerial rationality in artificial intelligence (AI)-augmented decision-making through the notion of algorithmic-bounded rationality (ABR). It argues that AI does not remove boundedness but relocates it into algorithmic constraints related to data volatility, model opacity and governance maturity.
Building on bounded rationality and socio-technical systems theory, this conceptual study develops an ABR framework linking three decision modes (AI-led, human-first and collaborative) to mechanisms of algorithmic boundedness. The framework is further extended through propositions on mode–task fit and governance conditions for sustaining hybrid decision architectures.
The analysis shows that human–AI collaboration represents a distinct rationality configuration rather than a midpoint between automation and human judgment. Under ABR, each decision mode becomes effective under different combinations of data intensity, contextual ambiguity and accountability demands.
Managers should treat AI integration as a redesign of decision governance rather than a technological upgrade, emphasizing appropriate authority allocation and oversight mechanisms.
The study reframes rationality in the AI era by showing how boundedness shifts from human cognition to socio-technical decision infrastructures. It contributes a mechanism-based framework linking decision modes, task conditions and governance arrangements in AI-augmented decision systems.
In recent years, the real-world implementation of artificial intelligence (AI) technology in the financial sector has continued to deepen. With more accurate prediction models, higher operational efficiency, and personalized services, this technology has reshaped the generation model of investment decision-making. However, the large-scale deployment of AI has also given rise to two core categories of risks. First, AI systems inherently face the problems of algorithmic opacity and implicit systemic bias. Second, preexisting cognitive biases among retail investors—such as overconfidence and the anchoring effect—are further amplified by AI-powered digital financial platforms. This study draws on two core theoretical frameworks, behavioral finance and AI ethics, to focus on the coexistence logic of human behavioral biases and algorithmic biases, and develops an analytical tool that integrates both types of bias. At the same time, the study identifies conceptual gaps in the existing traditional definition of financial literacy, puts forward the concept of algorithmic literacy as a component of digital literacy, and clarifies the core competencies investors must have in AI-driven investment scenarios: the ability to critically evaluate AI-generated investment advice, and to offset their own behavioral biases through active human engagement. This study argues that AI development must implement robust ethical principles encompassing transparency, accountability, and digital trust, to optimize governance systems that promote the responsible use of AI. By moving beyond the narrow framework of traditional financial literacy, this research provides support for aligning AI innovation with consumers’ financial well-being. The study’s findings are usable for financial education practitioners, policymakers, and financial institutions, to advance investor protection, and ensure that technological research and development aligns with ethically sound financial decision-making.
K. Jalaja, G. Ashoka· Journal of Commerce, Economi...· 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.
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