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Search Minerva’s current Findings using transparent hybrid retrieval. Results remain strictly inside the configured corpus.

Hybrid retrieval

Results for “Artificial Intelligence in Finance”

30 findings

73match

Finding 60631 source

Artificial intelligence transforms finance by streamlining merger and acquisition processes, enhancing due diligence, while natural language processing harnesses data from Google Trends and YouTube to reshape stock analysis and forecasting.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

72match

Finding 60621 source

The chapter unveils a transformative integration of network science and artificial intelligence, revealing venture capital dynamics from Silicon Valley hubs to the PayPal Mafia and exposing investment flows in finance.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

72match

Finding 23921 source

The study demonstrates how artificial intelligence transforms corporate finance by enhancing prediction accuracy, uncovering hidden patterns, and analyzing unstructured data with models like random forests, neural networks, and causal forests.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

69match

Finding 75141 source

The results indicate that LSTM models are highly effective for stock price prediction. Potential applications include investment strategies and financial planning. Future research could explore other companies and longer time frames to validate these findings.

LexicalMeaningQualityGraph

Matched: artificial, intelligence

69match

Finding 38031 source

The study introduces advanced quantile-on-quantile and cross-multiquantilogram methods, revealing asymmetric, nonlinear spillovers and predictability in crypto markets, especially during booms and crashesa novel approach for understanding market shocks.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

69match

Finding 38041 source

AI acts as an early warning signal for crypto markets, but combining AI and crypto assets may reduce diversification benefits; findings are limited by short data periods and few tokens per sector.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

68match

Finding 38021 source

AI developments, tracked by the NASDAQ AI and Robotics Index, consistently drive price movements in 15 major crypto sectors, with the strongest impact on DeFi, NFT, and Smart Contracts during market extremes.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

68match

Finding 38051 source

This paper uniquely explores dynamic connectedness between the NASDAQ AI index and 15 sectoral cryptocurrency indices using advanced quantile-on-quantile and cross-multiquantilogram methods. Its sectoral granularity, focus on AI-crypto spillovers, and actionable diversification insights offer novel, timely contributions, making it compelling for readers interested in AI-driven finance and DeFi.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

66match

Finding 21311 source

Researchers reveal that integrating artificial intelligence with digital twin technology enables real-time, customer-centric financial models, dynamically predicting behaviors and reducing operational risks, marking a breakthrough transformation in personalized financial services.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

66match

Finding 60641 source

Merging foundational theory with empirical rigor, the study employs network analysis, statistical models, temporal simulations, and NLP techniques to yield interdisciplinary insights, though dependence on specific data demands additional validation.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

66match

Finding 77301 source

Artificial intelligence is revolutionizing real estate by addressing challenges like spatial dependence, asset heterogeneity, and unstructured data, with AI models outperforming traditional methods in property valuation, risk assessment, and market analysis.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

64match

Finding 38651 source

The study underscores the importance of integrating ESG and AI in finance for sustainable practices. Future research should explore emerging AI techniques and regulatory impacts. Potential applications include enhancing ESG data analysis and developing responsible AI solutions.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

64match

Finding 23931 source

The chapter integrates deep learning with explainable AI to demystify models, combining text, audio, video, and numerical data for insights into investor sentiment, financial distress, portfolio construction, and corporate governance.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

63match

Finding 23941 source

Employing ensemble learning, SHAP values, and double machine learning, the research boosts performance while noting challenges like data manipulation, fake citations, and quality compromises, urging human-machine collaboration and market impact.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

63match

Finding 73141 source

Researchers categorize AI frameworks from GANs to multi-agent reinforcement learning, clarifying model strengths and limitations. They propose hybrid architectures and adaptive, interpretable models for real-time risk assessment in dynamic markets.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

63match

Finding 21321 source

Study finds Financial Digital Twins boost customer experience by 30--40% while mitigating risks, demonstrating a multi-layer architecture integrating data fabrics, behavioral models, simulations, decision intelligence, bi-directional synchronization, and predictive agency.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

63match

Finding 68201 source

The study concludes that AI-powered financial advisers democratize wealth management and optimize investment strategies. Future research should focus on improving transparency and user experience. Potential applications include personalized investment services and enhanced financial decision-making tools.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

63match

Finding 60651 source

Bridging network science and AI, this paper offers a multidisciplinary analysis of financial markets. It investigates venture capital networks, $M&A$ efficiency, and applies innovative NLP techniques to unconventional stock data. Integrating historical network evolution, including the $\text{PayPal Mafia}$, its fresh approach and surprising insights make it uniquely original and compelling indeed.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

63match

Finding 76191 source

Researchers introduce S.A.F.E. metricsSustainability, Accuracy, Fairness, and Explainabilityto evaluate AI trustworthiness, combining Lorenz curve statistics with Shapley value explainability, innovatively balancing transparency and robustness in model performance assessments with excellence.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

63match

Finding 76201 source

Empirical analysis on extensive Italian company dataset under preprocessing and diverse evaluation metrics validates the sustainable AI models; however, reliance on data calls for future research expanding to richer structures.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

63match

Finding 23951 source

The paper presents an original synthesis of established corporate finance methods with innovative applications of $AI$ and $machine learning$. It introduces fresh perspectives on analyzing unstructured data, causal inference, and related issues. Though primarily building on existing ideas, its thoughtful integration offers readers valuable insights and compelling practical relevance overall.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

63match

Finding 73151 source

Using heat maps, radar charts, and van diagrams from cloud platforms, the study quantifies AI performance, revealing challenges in scalability, interpretability, and adaptability while outlining directions for future financial solutions.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

62match

Finding 73131 source

A sixmonth review finds generative AI transforms finance by enhancing risk models, investment decisions, fraud detection, with $25%$ improved accuracy, $20%$ reduction in defaults, and $40%$ fewer fraud alerts overall.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

62match

Finding 76181 source

Study harnesses sustainable AI integrating Environmental, Social, and Governance metrics to enhance credit ratings; ensemble machine learning models, gradient boosting and stacked ensembles, capture non-linear ESG relationships using updated data.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

62match

Finding 21261 source

The chapter concludes that AI's integration in finance offers significant advantages, with potential applications in risk assessment and tax filing. Future research should explore further technological adoption to enhance financial research and practice.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

61match

Finding 77311 source

The survey introduces the 'agentic frontier,' where AI evolves from prediction tools to autonomous agents automating complex tasks like underwriting and portfolio management, and coins new terms such as 'agentic imperative.'.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

61match

Finding 77321 source

Despite AIs gains in accuracy and efficiency, the study highlights gaps: limited causal analysis, underexplored areas like sustainability and urban planning, and the urgent need for standardized benchmarks and ethical, transparent AI governance.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

61match

Finding 73161 source

The paper compiles recent research on generative AI agents in finance, emphasizing risk management improvements via quantitative illustrations. Its structured synthesis provides practical value and fresh insights by aggregating diverse studies. As a literature review lacking groundbreaking methods, its timeliness and presentation make it a compelling resource for financial AI.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

61match

Finding 77331 source

This paper uniquely reviews AI applications in real estate, addressing spatial dependence, asset heterogeneity, and unstructured data. Its originality lies in cross-domain perspectives and advanced AI methods, though novelty and impact are limited for hedge funds. It is compelling for readers interested in transferable AI methodologies beyond core quantitative finance.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence

61match

Finding 23521 source

The study concludes that AI can significantly enhance cryptocurrency price prediction and trading strategies. Future research should focus on integrating cryptocurrencies with traditional markets and improving feature engineering. Potential applications include automated trading systems and risk management tools.

LexicalMeaningQualityGraph

Matched: artificial, finance, intelligence