This review covers AI in finance, challenges, techniques, and opportunities.
problem Challenges and opportunities in AI applications in finance.
method Comprehensive categorization and overview of AI research in finance over decades.
result A dense roadmap of AI challenges, techniques, and opportunities in finance.
The paper explores AI in finance, focusing on XAI's role in enhancing interpretability and trust.
problem The need for AI in finance and the importance of XAI for better decision-making.
method Tracing AI's evolution in finance, highlighting XAI's role, and demonstrating through simulations.
result XAI enhances trust in AI systems, leading to more responsible decision-making.
AI helps simplify complex ship finance processes.
problem Complexity in ship finance due to data and regulatory requirements.
method Integrates large language models for document comprehension, information extraction, and workflow automation.
result AI-assisted systems can support maritime finance professionals in managing complex information and reporting requirements.
This paper reviews methods to improve AI explainability in finance.
problem Lack of explainability in AI models, especially in finance.
method Categorizes methods to improve explainability of deep learning models.
result Provides a comparative survey of methods to enhance AI explainability.
AI helps assess nature-related financial risks for financial institutions.
problem Challenges in evaluating nature-related risks due to large data volume and complexity.
method Uses AI to address data gaps, uncertainty, and complex systems.
result Potential AI solutions for two use cases: beef supply and water utility.
AI in finance uses quantum logic for better decision-making.
problem Improving financial decision-making models using AI.
method Application of quantum logic in machine learning techniques.
result Advantages of quantum-inspired neural networks in finance.
Survey examines agentic AI in finance, highlighting its autonomy and challenges.
problem Autonomous AI systems in finance and their implications.
method Systematic review of research, technical architectures, market applications, and governance frameworks.
result Agentic AI offers enhanced market efficiency but introduces new risks.
AI framework predicts invoice dilution in supply chain finance.
problem Invoice dilution risk in supply chain finance.
method AI, machine learning, dynamic credit limits, real-time projections.
result Supplemental AI model improves prediction accuracy.
Agent-to-agent finance aims to manage payments and trust for AI agents.
problem Managing financial interactions between autonomous AI agents.
method Develops agent-to-agent finance concept and explores blockchain solutions.
result Agent-to-agent finance can address coordination frictions in financial markets.
Survey of AI in quant finance, from deep learning to LLMs.
problem Improving predictive modeling and automation in asset management.
method Exploring AI contributions to quant investment pipeline, from human-crafted features to LLMs.
result AI has enabled scalable modeling and autonomous agents in quant finance.
AI agents in experimental markets exhibit behavioral patterns that aggregate into market dynamics.
problem Understanding AI trading behavior and its impact on market dynamics.
method Experimental asset markets populated by AI agents trained on Large Language Models (LLMs).
result AI agents' behavior leads to market dynamics similar to human traders, including bubbles.
Paper proposes government indemnification for AI risks to solve judgment-proof problem.
problem Uninsurable risks from AI, especially existential risks, create a judgment-proof problem.
method A government-provided, mandatory indemnification program using risk-priced fees and Bayesian Truth Serum.
result The approach better leverages private information and signals risk mitigation efforts.
This paper ranks Latin American countries based on AI potential.
problem Identifying emerging AI powers in Latin America.
method Ranking based on infrastructure, education, and finance.
result Argentina, Colombia, Uruguay, Costa Rica, and Ecuador are new emerging powers in AI.
Examines AI regulation in finance, highlighting risks and gaps in current laws.
problem Rapid AI adoption in finance introduces risks and compliance challenges.
method Reviews current legislation, industry guidelines, and real-world use cases.
result Need for adaptive, technology-neutral policies to balance innovation and consumer protection.
FinRobot opens-source AI for financial tasks, breaking down complex problems.
problem Barriers to AI adoption in finance due to proprietary data and specialized knowledge.
method Develops open-source AI agent platform with four layers: Financial AI Agents, LLM Algorithms, LLMOps/DataOps, and Foundation Models.
result FinRobot democratizes AI access for financial analysis.
This paper explores good practices for AI explainability in finance.
problem Complex financial models lack transparency and interpretability.
method Exploring good practices for deploying explainability in AI-based financial systems.
result Developing effective XAI tools for the financial industry.
FinTech uses data science and AI to transform finance.
problem Transforming finance with data science and AI.
method DSAI techniques including complex system methods, quantitative methods, etc.
result DSAI enables smart FinTech for various financial sectors.
This paper tests LLMs in finance to assess ethical behavior.
problem Aligning AI with ethical and legal standards in finance.
method Prompted LLMs to simulate CEO behavior, analyzed with logistic regression.
result Significant heterogeneity in LLMs' unethical behavior propensity.
Survey of LLMs in finance tasks, including adoption and performance.
problem Utilizing large language models in financial tasks.
method Review of current approaches, decision framework for adoption.
result Synthesizes state-of-the-art for LLMs in finance.
The paper evaluates the importance of monotonicity in AI fairness across various fields.
problem Ensuring fairness in AI applications across criminology, education, health care, and finance.
method Theoretical reasoning, simulation, and extensive empirical analysis of monotonic neural additive models (MNAMs).
result Monotonicity is essential for fairness in AI ethics and society, especially in criminology, education, health care, and finance.
DeFi TrustBoost uses blockchain and AI to assess small business loans.
problem Assessing small business loans from low-wealth households.
method Combines blockchain and Explainable AI to ensure confidentiality, compliance, and security.
result Tamper-proof auditing and on-chain/off-chain data storage for financial organizations.
Survey of financial LLMs in finance.
problem Limited research on Financial LLMs (FinLLMs).
method Chronological overview of PLMs, comparison of techniques, performance evaluations, and advanced tasks.
result Compilation of accessible datasets and benchmarks for AI research in finance.
Paper develops framework for AI agents in financial markets.
problem Systemic implications of AI in finance depend on agent architectures.
method Four-layer architecture and AFMM model for analysis.
result AI agents can improve market efficiency and resilience.
FinSurvival provides a large-scale financial survival modeling benchmark.
problem Lack of large-scale, realistic, and freely available datasets for benchmarking AI survival models.
method Derived 16 survival modeling tasks from cryptocurrency lending data using an automated pipeline.
result Demonstrated that existing AI survival models are not well-suited for these challenging tasks.
AI investors signal higher debt in ESG firms, boosting portfolio management.
problem Determining the value of ESG investing amid AI investment trends.
method Cross-sectional regressions of ESG scores and debt ratios of S&P 500 firms.
result ESG scores signal higher debt in firms, supporting ESG investing.
Paper reviews the evolution of alpha from human insight to AI-powered systems.
problem Exceeding market benchmarks in finance.
method Five-stage taxonomy integrating representation learning, multimodal data fusion, and LLM agents.
result Unified framework for evaluating and developing next-gen alpha systems.
This review explores XAI in finance, highlighting common techniques and areas needing improvement.
problem Balancing accuracy and transparency in financial AI models.
method Bibliometric and content analysis of XAI applications in finance.
result Post-hoc interpretability techniques are most used in financial XAI.
New AI governance framework tackles risks in finance.
problem Risks from evolving AI models in finance.
method Agent-based framework with modular governance architecture.
result Controls quarantine harmful behavior in real time.
This paper enhances credit risk management using explainable AI techniques.
problem Lack of transparency and explainability in AI models for credit risk management.
method Implement LIME and SHAP for explaining ML-based credit scoring models.
result Demonstrates practical challenges and solutions for XAI methods in finance.
Pipeline for comparing trading algorithms in finance and crypto.
problem Disconnected research and applications in algorithmic trading.
method General pipeline for designing, programming, and evaluating trading strategies.
result Systematic comparison of trading algorithms in finance and crypto.
New PU ratio predicts long-term Bitcoin returns better than other methods.
problem Lack of convincing proxies for cryptocurrency fundamentals.
method Developed a new market-to-fundamental ratio (PU ratio) using blockchain accounting methods.
result PU ratio effectively predicts long-term Bitcoin returns compared to alternative methods.
Survey of AI in finance covering models, strategies, and knowledge systems.
problem Challenges in applying AI to financial markets, especially in high-frequency trading.
method Systematic analysis of financial AI across predictive models, decision frameworks, and knowledge augmentation systems.
result Critical trade-offs and gaps between theoretical advances and practical implementation in financial AI.
AI enhances ESG practices in finance, but requires careful consideration.
problem Regulatory pressures and stakeholder awareness drive ESG adoption.
method Industrial survey categorizing AI applications in ESG.
result AI improves analytical capabilities, risk assessment, and customer engagement.
Study uses FinBERT for financial sentiment analysis to predict stock movement.
problem Predicting stock movement with greater accuracy.
method Integrates sentiment analysis with FinBERT and LSTM networks.
result FinBERT enhances model's ability to predict market fluctuations.
AI model automates financial investment research tasks.
problem Manual labor-intensive tasks in financial analysis.
method Fine-tuning language models on diverse financial data.
result Improved model performance in financial tasks.
DeltaHedge uses AI to optimize portfolio options trading.
problem Balancing risk and return in volatile markets.
method Multi-agent framework integrating reinforcement learning and options hedging.
result Outperforms traditional and standalone models.
AI models predict stock trends using historical data and public sentiment.
problem Improving stock market prediction accuracy using AI.
method Employed regression and classification ML algorithms for technical and fundamental analysis respectively.
result Median performance suggests AI is not yet superior to stock markets.
Unified AI system for data quality control and governance in regulated environments.
problem Isolated data quality control steps in existing systems.
method AI-driven framework integrating rule-based, statistical, and AI methods.
result Empirical gains in anomaly detection, reduced manual remediation, improved auditability.
This paper reviews ML applications in finance, enhancing asset pricing models.
problem Limitations of traditional asset pricing models in complex market dynamics.
method Exploring ML models including supervised, unsupervised, semi-supervised, and reinforcement learning.
result Enhanced return prediction and portfolio optimization through ML integration.
Peer-induced fairness framework audits algorithmic fairness in AI applications.
problem Current auditing methods lack robustness and fail to distinguish between algorithmic discrimination and subject limitations.
method Combines counterfactual fairness and peer comparison strategy for a reliable auditing tool.
result Demonstrates significant unfairness in micro-firms compared to non-micro firms, highlighting the framework's potential.
ValueBlindBench tests LLM-generated investment rationales for validity before returns are known.
problem Delayed-ground-truth evaluation of LLM-generated investment rationales.
method Agreement-gated stress testing protocol to validate LLM-judged rationales.
result ValueBlindBench prevents overclaims and identifies flawed financial constructs.
This paper tackles interpretability of LLMs in finance.
problem Complexity and lack of transparency of LLMs in finance.
method Mechanistic interpretability to understand LLM behavior.
result Demonstrates practical relevance of mechanistic interpretability in financial use cases.
ECLIPSE detects AI hallucinations in finance with high accuracy.
problem Hallucinations in AI-generated answers limit safe deployment in finance.
method Combines entropy estimation and perplexity decomposition to measure model evidence use.
result ECLIPSE achieves ROC AUC of 0.89 and average precision of 0.90 on financial QA dataset.
AutoML enhances credit decisions with XAI for better transparency.
problem Transparency in AI-driven financial decisions.
method Combining AutoML and XAI (SHAP) for credit scoring.
result Improved efficiency and accuracy in credit decisions with enhanced transparency.
Paper proposes FinAR-Bench to evaluate LLMs in financial analysis tasks.
problem Inaccurate financial analysis by LLMs leading to investment and regulatory issues.
method Proposes FinAR-Bench, a benchmark dataset with three steps: key info extraction, financial indicator calculation, and logical reasoning.
result LLMs perform better in key info extraction and indicator calculation but struggle with logical reasoning.
New AI technique explains neural net decisions over time.
problem Difficulty of explaining AI decisions in time series data.
method Proposes a novel XAI technique for deep learning methods.
result Preserves and exploits the natural time ordering of data.
Enhances trading metrics with financially grounded loss functions.
problem Challenges in financial deep learning, especially interpretability.
method Introduces loss functions derived from finance metrics and turnover regularization.
result Proposed loss functions outperform traditional methods in trading metrics.
Study uses AI to predict changes in international public finances based on US markets.
problem Understanding correlations between US and international public finances.
method Artificial intelligence and neural networks to model and predict changes.
result Neural network model achieved MSE of 2.79, indicating significant correlation and impact of US market volatility on international markets.