E-ISSN:2250-0758
P-ISSN:2394-6962

Research Article

Artificial Intelligence

International Journal of Engineering and Management Research

2025 Volume 15 Number 5 October
Publisherwww.vandanapublications.com

Impact of Artificial Intelligence on Investment Decision-Making

Saini V1*
DOI:10.5281/zenodo.17667583

1* Vandana Saini, Assistant Professor, Institute of Hospitality, Management and Sciences, Kotdwar, Uttarakhand, India.

This research paper examines the impact of Artificial Intelligence (AI) on investment decision-making using secondary data and existing literature. The study analyzes key AI tools—including machine learning, deep learning, natural language processing, and predictive analytics—and their applications in algorithmic trading, portfolio optimization, and robo-advisory services. Findings indicate that AI significantly enhances forecasting accuracy, improves risk management, reduces human biases, and strengthens overall decision efficiency compared to traditional approaches. The analysis also highlights differing adoption patterns between retail and institutional investors. Despite the advantages, the study identifies critical challenges such as data privacy concerns, algorithmic transparency issues, ethical risks, and potential over-reliance on automated systems. The paper concludes that while AI is reshaping the financial landscape and investment practices, responsible integration supported by strong regulation and continuous monitoring is essential for sustainable use.

Keywords: Artificial Intelligence, Investment Decision-Making, Machine Learning, Algorithmic Trading, Robo-Advisory Systems

Corresponding Author How to Cite this Article To Browse
Vandana Saini, Assistant Professor, Institute of Hospitality, Management and Sciences, Kotdwar, Uttarakhand, India.
Email: 		Saini V, Impact of Artificial Intelligence on Investment Decision-Making. Int J Engg Mgmt Res. 2025;15(5):133-140.
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https://ijemr.vandanapublications.com/index.php/j/article/view/1818

Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted
2025-09-04 2025-09-18 2025-10-04
Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note
None Nil Yes 4.58

© 2025 by Saini V and Published by Vandana Publications. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License https://creativecommons.org/licenses/by/4.0/ unported [CC BY 4.0].

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Download PDFBack To Article1. Introduction2. Research
Questions3. Research
Objectives4. Review of
Literature5. Identified
Research Gaps6. Analysis
and Discussion7. ConclusionReferences
Saini V. Impact of Artificial Intelligence on Investment Decision-Making

1. Introduction

Artificial Intelligence (AI) has become a transformative force in the global financial markets, fundamentally altering how investment decisions are made. AI techniques — such as machine learning, deep learning, natural language processing, and predictive analytics — enable financial actors to process vast amounts of structured and unstructured data at unprecedented speed. These capabilities offer significant improvements in forecasting, risk assessment, and automated trading, thereby changing the traditional dynamics of investment decision-making (Saoudi & Zidane, 2025; Kumar & Renuka, 2025).

Historically, investment decisions were predominantly based on human judgement, experience, and traditional methods such as fundamental and technical analysis. Investors relied on their intuition, market knowledge, and qualitative assessments. But as market complexity and data volume increased, there was a gradual shift toward computer-assisted models and algorithmic trading. Over time, this evolution has progressed further toward fully AI-driven systems that can detect patterns, optimise portfolios, and execute trades with high precision (Mahajan, 2024; Guo, Wang, Ni & Shum, 2022).

Understanding the impact of AI on investment decision-making is crucial for several reasons. For individual investors, AI-powered tools like robo-advisors can offer personalized recommendations, reduce decision biases, and increase efficiency (Panwar, Aggarwal, Jamwal, Saini & Sharma, 2025). For financial institutions, AI can strengthen risk management processes, improve fraud detection, and enhance portfolio performance (Saoudi & Zidane, 2025). At the macro level, widespread AI adoption affects market efficiency, liquidity, and regulatory issues — raising important questions about systemic risks, governance, and transparency (Sujarwo&Utama, 2025).

Despite growing interest, existing literature reveals important gaps. While many studies discuss AI's technical potential, fewer examine its broader implications for investor behavior, decision quality, and institutional transformation (Arifian, Mudawanah, Herlina& Sofana, 2024). Empirical evidence comparing AI-driven strategies with traditional methods remains limited, and ethical concerns — such as algorithmic bias,

lack of transparency, and data privacy — are not yet fully explored (Saoudi & Zidane, 2025; Bai, Jeena&Shanavas, 2024). This research thus seeks to fill these gaps by synthesizing diverse secondary sources and offering a comprehensive view of both the promise and the risks of AI in investment decision-making.

The primary purpose of this study is to examine how AI is reshaping investment decisions at multiple levels: individual investors, institutional players, and financial markets as a whole. By analyzing published research, industry reports, and relevant case studies, this paper aims to assess the benefits, challenges, and broader implications of AI adoption. The significance of this study lies in its potential to inform policy makers, financial institutions, and investors about responsible and effective uses of AI — enabling better frameworks for governance, ethics, and strategy in an AI-driven financial future.

2. Research Questions

The rapid integration of Artificial Intelligence (AI) into financial markets raises several critical questions that guide this study. Existing research highlights the potential of AI to enhance data processing, prediction accuracy, and decision quality, yet gaps remain in understanding its broader implications for investors and financial institutions (Saoudi & Zidane, 2025; Mahajan, 2024). Based on these gaps, the following research questions are formulated:

RQ1: How does AI influence investment decision-making processes in financial markets?

AI technologies contribute to faster data processing, improved pattern recognition, and enhanced decision accuracy, but the extent of their influence on different stages of investment decision-making remains underexplored (Guo et al., 2022; Kumar & Renuka, 2025).

RQ2: What types of AI tools are most widely used in investment decisions?

Studies indicate increasing use of machine learning algorithms, robo-advisors, algorithmic trading systems, sentiment analysis tools, and predictive analytics, yet comparative adoption levels are not clearly established (Panwar et al., 2025; Bai, Jeena&Shanavas, 2024).


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RQ3: What advantages and limitations do investors experience when using AI?

While AI offers benefits such as reduced bias, efficiency, and higher accuracy, challenges related to transparency, algorithmic risk, and technological dependency persist (Arifian et al., 2024; Saoudi & Zidane, 2025).

RQ4: How has AI improved investment performance and risk prediction?

AI systems have demonstrated measurable improvements in trend forecasting, portfolio optimisation, and risk assessment, though empirical evidence remains fragmented across market contexts (Mahajan, 2024; Sujarwo&Utama, 2025).

RQ5: How is investor behaviour changing with the adoption of AI-driven systems?

AI tools influence investor confidence, reliance on automated advisory services, and decision patterns, yet behavioural dynamics require deeper scholarly investigation (Panwar et al., 2025; Khanna, 2021).

3. Research Objectives

1. To examine how AI technologies are transforming investment decision-making.
2. To analyse the role of AI-based tools (e.g., robo-advisors, ML algorithms, sentiment analysis models).
3. To identify the benefits and challenges of using AI in investment decisions.
4. To evaluate the impact of AI adoption on investment efficiency, accuracy, and risk management.
5. To assess how investor behaviour is influenced by AI-driven insights.

4. Review of Literature

AI Tools in Finance: Machine Learning, Deep Learning, NLP, Predictive Analytics

Research across finance identifies machine learning (ML), deep learning (DL), natural language processing (NLP) and predictive analytics as the core AI toolset reshaping investment workflows. ML models (e.g., random forests, XGBoost) are widely applied for feature selection and forecasting, while DL architectures (LSTM, GRU, CNN hybrids) excel at capturing nonlinear temporal patterns in asset prices (Vancsura, 2025; Mienye, 2024).

NLP techniques enable extraction of sentiment and event signals from news, earnings calls and social media, providing alternative inputs that complement price-based models (Guo et al., 2022; Vancsura, 2025). Predictive analytics — an umbrella term for ML/DL models plus ensemble and hybrid methods — is now central to short-term forecasting, volatility estimation and scenario simulation (Bahoo, 2024; ResearchGate chapter on Predictive Analytics, 2025). Collectively, these tools increase the dimensionality and informational richness of models available to investors, but they also raise concerns about overfitting, interpretability, and the stability of predictions across regimes (Mienye, 2024; Bahoo, 2024).

Applications: Algorithmic Trading, Portfolio Optimization, Robo-Advisory

AI’s principal investment applications are algorithmic/high-frequency trading, portfolio optimisation, and robo-advisory. Algorithmic trading systems use ML and DL to detect micro-structure patterns, order-book dynamics and cross-asset signals to execute trades with low latency (Guo et al., 2022; Pattnaik, 2024). For portfolio optimisation, AI enables multi-objective and non-convex optimisation (e.g., risk-return tradeoffs with transaction costs and nonlinear constraints), often outperforming classical mean-variance models in backtests by exploiting complex interactions among assets (Zhao, 2025; ResearchGate SLR on portfolio optimisation, 2022). Robo-advisors leverage automated rule engines plus ML personalization to deliver low-cost, scalable advice — growing rapidly in assets under management and widening retail access to algorithmic strategies (Panwar et al., 2025; SSRN report on robo-advisors, 2025). Empirical studies show AI-based systems can improve ex-ante risk estimates and backtested returns, but results vary significantly across assets, horizons, and market states (Mahajan, 2024; Sujarwo&Utama, 2025).

Global Trends and Adoption Patterns

Large-scale surveys and industry reports document fast, though uneven, adoption of AI across financial institutions. Global AI adoption expanded sharply in the late 2010s and early 2020s, with firms deploying multiple AI capabilities (NLP, forecasting, automation) — though adoption concentrations remain (applied AI clusters in trading, risk and customer service) rather than uniform penetration across all functions (McKinsey, 2022; 2023).


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Consultancy reports estimate substantial economic potential from AI adoption and forecast continued growth in AI-driven finance services; for example, PwC highlights large GDP and productivity gains from AI that translate into investment in financial sectors (PwC, Sizing the Prize). Market projections for robo-advisory AUM and AI-enabled investment platforms indicate rapid expansion (SSRN robo-advisory projection, 2025), while academic reviews note growing research output on AI in finance and fintech applications (Bahoo, 2024; Pattnaik, 2024). Adoption patterns are, however, moderated by regulatory readiness, data governance, model risk management maturity, and the concentration of technical talent (McKinsey, 2022; PwC, 2017).

Comparative Review: Traditional vs. AI-Driven Decision-Making

Comparative studies contrast classical human/analytic approaches (fundamental and technical analysis, CAPM, mean-variance optimisation) with AI-driven methods. Systematic comparisons find that AI models often outperform traditional linear or parametric models on predictive accuracy and some backtested performance metrics, especially in noisy, nonlinear markets (ResearchGate comparative analyses; Vancsura, 2025). However, findings are nuanced: human managers can outperform AI in regime shifts, in capturing qualitative information, and in exploiting market narratives where data is scarce; conversely, AI tends to better handle high-dimensional data and reduce behavioural bias in routine decisions (Anuar et al., 2025; OSF comparative study, 2023). Robust comparative literature therefore emphasizes complementarity — hybrid approaches that combine AI’s pattern recognition with human oversight and domain knowledge often yield the most resilient outcomes (Guo et al., 2022; Anuar et al., 2025).

Key Findings from Previous Empirical and Conceptual Studies

Empirical research reports several recurring findings: (1) ML/DL models (particularly LSTM/GRU and tree-based ensembles) frequently deliver improved short-term forecasts relative to benchmark statistical models when properly regularised (Vancsura, 2025; Mienye, 2024); (2) AI adds value in risk modelling and tail-risk detection by integrating alternative data sources and nonlinear relationships (Mahajan, 2024; Sujarwo&Utama, 2025);

(3) robo-advisors expand financial inclusion and standardize low-cost portfolio services, though they also encourage behavioural reliance on automated advice (Panwar et al., 2025; SSRN, 2025); (4) AI systems can improve operational efficiency (trade execution, compliance automation) but introduce new model-risk vectors such as algorithmic crowding and amplification of market moves during stress (Bahoo, 2024; McKinsey, 2023). Conceptual reviews further stress governance, ethics and explainability as central themes for sustainable AI use in finance (Bahoo, 2024; VirtusInterpress monograph, 2025).

5. Identified Research Gaps

Despite rapid progress, the literature reveals several persistent gaps that this study aims to address:

1. Heterogeneity and reproducibility of empirical results. Performance improvements reported for AI methods are often dataset- or strategy-specific and lack standardized cross-market replication (Vancsura, 2025; Zhao, 2025).
2. Behavioural and organisational impacts. There is limited systematic evidence on how AI changes investor psychology, decision autonomy, and organisational decision processes over time (Khanna, 2021; Panwar et al., 2025).
3. Model explainability and regulatory alignment. Research on operationalising explainability (XAI) for compliance and supervisory review is still evolving, particularly in the context of portfolio managers and robo-advisors (Guo et al., 2022; Bahoo, 2024).
4. Systemic risk and algorithmic interactions. Studies on how algorithmic strategies interact during market stress (herding, liquidity spirals) are nascent and require more cross-institutional data (McKinsey, 2023; Bahoo, 2024).
5. Longitudinal assessments. Most studies focus on backtests or short windows; longitudinal, out-of-sample evaluations that capture multiple market regimes are rarer (Anuar et al., 2025; Zhao, 2025).

Addressing these gaps requires systematic synthesis of secondary sources, cross-study meta-analysis when possible, and research designs that separate genuine predictive skill from overfitting and data snooping. The current paper contributes by consolidating secondary evidence across methodological strands (ML/DL/NLP) and application areas (trading, optimisation, advisory), highlighting both documented benefits and unresolved risks.


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6. Analysis and Discussion

The analysis of existing secondary data reveals a profound transformation in investment decision-making as Artificial Intelligence (AI) becomes increasingly integrated into financial markets. This section synthesizes insights from empirical studies, conceptual frameworks, and case-based evidence to evaluate the market-wide implications of AI adoption.

1. AI’s Impact on Market Prediction and Forecasting

AI-driven models are significantly improving forecasting accuracy in financial markets. Machine Learning (ML) and Deep Learning (DL) algorithms can analyze complex, nonlinear, and high-frequency datasets far beyond human capacity (Guo et al., 2022). These models incorporate technical indicators, historical data, and alternative data sources—such as sentiment analysis and social media trends—to predict price movements with greater precision.

Mahajan (2024) notes that AI systems enhance predictive performance by employing real-time analytics and adaptive learning techniques, enabling continuous updates based on market fluctuations. Similarly, Bai, Jeena and Shanavas (2024) argue that AI’s ability to process sustainability metrics (ESG) further improves long-term predictive reliability. The overall literature supports the conclusion that AI significantly improves predictive accuracy compared to traditional econometric models.

2. AI-Enabled Risk Management and Fraud Detection

Risk management has been one of the most transformative areas influenced by AI. AI systems can identify potential risks by analyzing large volumes of structured and unstructured data, allowing for early detection of market anomalies and potential fraud (Arifian et al., 2024). Deep learning models detect patterns in abnormal transactions, thereby strengthening Anti-Money-Laundering (AML) efforts.

According to Sujarwo and Utama (2025), AI tools enhance stress testing, scenario analysis, and volatility forecasting. This capability allows financial institutions to create more resilient portfolios and better mitigate systemic risks.

Compared to traditional risk assessment—which often relies on backward-looking analyses—AI enhances predictive risk modeling through dynamic, data-driven mechanisms.

3. Performance Comparison: Traditional vs. AI-Based Decisions

The literature consistently shows that AI-based investment decisions outperform traditional approaches in terms of speed, accuracy, and consistency (Saoudi & Zidane, 2025). Traditional methods are heavily dependent on human expertise, behavioural biases, and limited data processing capabilities.

AI-driven systems, on the other hand:

  • Process large datasets within milliseconds
  • Identify hidden correlations
  • Operate without emotional bias
  • Adapt continuously through learning algorithms

Panwar et al. (2025) find that portfolios managed with AI outperform those managed traditionally, especially in volatile market conditions. Likewise, algorithmic trading systems exhibit superior speed and precision, enabling more efficient price discovery.

However, the literature also highlights concerns, such as AI models’ lack of interpretability and vulnerability during extreme market events, raising questions about reliability under stress.

4. Impact on Retail vs. Institutional Investors

AI adoption varies considerably between retail and institutional investors. Institutional investors—hedge funds, banks, and asset management firms—were early adopters of AI tools, particularly algorithmic trading and portfolio optimization systems (Khanna, 2021). These firms leverage advanced ML models, high-frequency trading bots, and proprietary algorithms to gain competitive advantages.

Retail investors, by contrast, rely more on robo-advisors and AI-enabled trading applications, which democratize investment access by providing low-cost automated portfolio management. Panwar et al. (2025) highlight that AI-driven advisory tools enhance financial literacy, reduce decision-making biases, and improve investor confidence among retail users.


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While institutional investors benefit from customized, high-powered AI systems, retail investors enjoy ease of access and affordability. However, retail investors remain more vulnerable to over-reliance and limited algorithmic transparency.

5. Ethical and Technological Challenges

Despite AI’s advantages, several ethical and technological challenges persist in financial decision-making. The opacity of “black-box” algorithms raises concerns regarding accountability and transparency (Saoudi & Zidane, 2025). Algorithmic bias emerges when models are trained on skewed or incomplete datasets, potentially leading to discriminatory outcomes.

Additionally, data privacy concerns arise as many AI systems rely on personal and behavioural data. Guo et al. (2022) emphasize that regulatory frameworks have not kept pace with AI advancement, resulting in governance gaps. Technological risks—including model overfitting, cyber threats, and systemic vulnerabilities during market shocks—further complicate widespread adoption.

6. Insights from Case Studies: Robo-Advisors and Algorithmic Trading Firms

Case studies on robo-advisors reveal significant improvements in portfolio diversification and cost efficiency. Bai et al. (2024) show that robo-advisors integrate ESG preferences and behavioural finance insights to tailor portfolios to investor profiles, outperforming traditional advisory models in certain contexts.

Algorithmic trading firms such as Renaissance Technologies and Two Sigma demonstrate how large-scale AI deployments can consistently outperform market benchmarks through deep learning and reinforcement learning techniques (Mahajan, 2024). These firms employ real-time decision-making algorithms capable of executing thousands of trades per second, something impossible in traditional systems.

Additionally, fintech companies integrating NLP for sentiment analysis (e.g., using news feeds and social media signals) showcase how AI enhances intraday and long-term trading strategies.

7. Comparative Findings from Multiple Studies

Across the reviewed studies, several consistent findings emerge:

ThemeConsensus in Literature
AI improves prediction accuracySupported by Mahajan (2024); Guo et al. (2022)
AI enhances risk managementHighlighted by Arifian et al. (2024); Sujarwo&Utama (2025)
AI outperforms traditional decision modelsFound in Saoudi & Zidane (2025); Panwar et al. (2025)
Retail adoption increasing via robo-advisorsSupported by Panwar et al. (2025)
Ethical and transparency issues remain barriersEmphasized by Saoudi & Zidane (2025)

Overall, secondary data demonstrates a strong consensus that AI enhances investment performance, risk management, and decision-making quality. However, structural challenges related to ethics, governance, and market stability must be addressed for sustainable adoption.

7. Conclusion

The study shows that Artificial Intelligence has significantly transformed investment decision-making by improving forecasting accuracy, risk assessment, and decision efficiency. AI tools such as machine learning, deep learning, NLP, and predictive analytics outperform traditional methods, offering faster processing, better pattern recognition, and reduced human bias. Retail investors benefit from robo-advisors, while institutional investors gain advantages through algorithmic trading and advanced analytics.

However, challenges remain. AI systems lack transparency, may introduce algorithmic bias, and rely heavily on data quality. Ethical concerns, data privacy issues, and vulnerabilities during market disruptions highlight the need for stronger governance and responsible use. Overall, AI enhances investment performance but requires careful regulation and continuous monitoring.

Recommendations

1. Strengthen Regulations: Develop guidelines on transparency, accountability, and ethical AI use in finance.
2. Improve Explainability: Promote Explainable AI (XAI) to increase investor trust in automated decisions.


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3. Enhance Data Governance: Improve data privacy, cybersecurity, and data quality for reliable AI performance.
4. Use Hybrid Models: Combine human judgment with AI tools to ensure balanced and resilient decision-making.
5. Educate Investors: Increase AI literacy among retail investors to promote informed usage of robo-advisors and trading apps.
6. Monitor AI Models: Frequently test and review AI systems to ensure stability and reliability.
7. Encourage Responsible Innovation: Support AI development while safeguarding market stability.

References

[1] Arifian, D., Mudawanah, S., Herlina, & Sofana, A. I. (2024). The impact of artificial intelligence on investment decision-making. Islamic Studies in the World, 1(2). https://doi.org/10.70177/isw.v1i2.1522

[2] Anuar, A. A., et al. (2025). Comparative analysis of AI-driven versus human-managed funds. Future Business Journal.

[3] Bai, T. R., Jeena, R., & Shanavas, A. (2024). Sustainable finance and use of artificial intelligence in investment decision making. International Journal of Advanced Research, (Sep), 1212–1218. https://dx.doi.org/10.21474/IJAR01/19554

[4] Bahoo, S. (2024). Artificial intelligence in Finance: A comprehensive review. Springer Journal.

[5] Guo, J., Wang, S., Ni, L. M., & Shum, H.-Y. (2022). Quant 4.0: Engineering quantitative investment with automated, explainable and knowledge-driven artificial intelligence.

[6] Kumar, K. N., & Renuka, R. (2025). The impact of artificial intelligence on financial decision-making. Journal of Information Systems Engineering and Management, 10(47 s).

[7] Guo, J., Wang, S., Ni, L. M., & Shum, H.-Y. (2022). Quant 4.0: Engineering quantitative investment with automated, explainable and knowledge-driven artificial intelligence.

[8] Khanna, P. (2021). Evaluating the impact of artificial intelligence on investment decision-making in finance. International Journal of Research in Finance and Management, 4(1), 78–84.

[9] Kumar, K. N., & Renuka, R. (2025). The impact of artificial intelligence on financial decision-making. Journal of Information Systems Engineering and Management, 10(47s).

[10] Khanna, P. (2021). Evaluating the impact of artificial intelligence on investment decision: Making in Finance. International Journal of Research in Finance and Management, 4(1), 78–84. https://doi.org/10.33545/26175754.2021.v4.i1a.248

[11] Mahajan, K. (2024). Transforming financial decision-making with artificial intelligence: A comprehensive study on ai-driven algorithms for investment, trading, and portfolio management. Journal of Electrical Systems, 20(10s).

[12] McKinsey & Company. (2022). The state of AI in 2022 — and a half-decade in review.

[13] Mienye, E. (2024). Deep learning in finance: A survey of applications. MDPI.

[14] Panwar, M. S., Aggarwal, K., Jamwal, N., Saini, A., & Sharma, M. (2025). Analyzing the impact of AI-driven financial advisory services on investment decision-making. International Journal of Environmental Sciences, 11(14s), 1158–1170. https://doi.org/10.64252/37hn9w50

[15] Pattnaik, D. (2024). Applications of AI and ML in banking and financial services: A bibliometric review. Science Direct.

[16] PwC. (2017/updated reports). Sizing the prize — AI’s potential economic impact.

[17] ResearchGate(2022). Portfolio optimization using artificial intelligence: A systematic literature review.

[18] Saoudi, S. E., & Zidane, B. (2025). The role of artificial intelligence in financial decision-making: Opportunities and risks. International Journal of Economic Perspectives, 19(5), 2381–2393.

[19] Sujarwo, M. D. A., & Utama, A. G. S. (2025). AI-driven financial planning: The future of investment advice through advanced analytics. Jurnal Akuntansidan Keuangan, 13(2). https://doi.org/10.29103/jak.v13i2.23452

[20] SSRN. (2025). The AI revolution in investment advisory: Global robo-advisory growth projections.

[21] Vancsura, L. (2025). Navigating AI-driven financial forecasting: A systematic review. MDPI.


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[22] Zhao, Y. (2025). How to optimize modern portfolio theory? A systematic review. Science Direct.

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