1. Introduction

The financial industry is characterized by its dynamic nature, where prices of assets fluctuate based on a myriad of factors, including market sentiment, economic indicators, and geopolitical events. Traditional financial pricing models, such as the Capital Asset Pricing Model (CAPM) and the Black-Scholes option pricing model, have been widely used to estimate asset values and manage risks [1][2]. However, these models often fall short of capturing the complexities of real-world financial markets due to their reliance on linear assumptions and historical data [3][4].

In recent years, the integration of machine learning (ML) and artificial intelligence (AI) into financial pricing has emerged as a game-changer. These technologies leverage advanced algorithms to analyze large volumes of data, identify patterns, and make predictions with greater accuracy [5][6]. By employing techniques such as supervised learning, unsupervised learning, and reinforcement learning, financial institutions can enhance their pricing strategies, optimize trading decisions, and improve risk management practices [7][8].

This review aims to explore the intersection of machine learning (ML), artificial intelligence (AI), and financial pricing, highlighting the advancements, challenges, and future directions in this rapidly evolving field. By analyzing existing research and practical applications, this review aims to provide insights into how machine learning (ML) and artificial intelligence (AI) can reshape financial pricing and enhance overall market efficiency.

2. Literature survey

The comparison between traditional pricing methods and machine learning-based approaches is crucial for understanding the advancements in financial pricing. Traditional models, such as the CAPM and the Black-Scholes model, often rely on simplifying assumptions and linear relationships among variables. For example, the CAPM assumes a linear relationship between expected return and systematic risk, which may not hold in volatile markets [1]. In contrast, machine learning models can capture non-linear relationships and interactions among multiple variables, leading to more accurate pricing predictions [4][6].

The rise of machine learning in financial pricing has been documented in numerous studies. For instance, empirical studies by Krauss et al. [7] reveal the superior performance of deep learning architectures in stock price prediction, attributed to their ability to capture non-linear market dynamics. Similarly, Bontemps et al. [8] highlighted the effectiveness of ML algorithms in risk assessment, showing that they can identify potential risks more accurately than traditional models. Furthermore, He et al. [5] emphasized the role of AI in enhancing asset pricing models, allowing for a more comprehensive analysis of market dynamics.

3. Advanced applications of ML and AI in financial pricing

3.1. Algorithmic trading

Algorithmic trading has revolutionized the way financial markets operate. By utilizing ML algorithms, traders can automate their trading strategies, executing orders at optimal prices based on real-time market data. Techniques such as reinforcement learning allow algorithms to learn from past trading experiences, continuously improving their performance over time [7].

Table 2 systematically outlines key machine learning (ML) techniques applied in algorithmic trading, highlighting their strengths and limitations. Decision Trees, built through recursive data partitioning, offer interpretability and visualization advantages, making them suitable for stock price prediction, though they are prone to overfitting with noisy data. Neural Networks, as deep learning models, excel at capturing complex nonlinear patterns and are widely used in high-frequency trading, but they demand substantial computational resources and large datasets while lacking interpretability. Support Vector Machines (SVM) perform well in high-dimensional classification tasks, such as risk assessment, yet their efficiency declines with large-scale data and sensitivity to parameter tuning. Reinforcement Learning dynamically optimizes strategies through trial-and-error, enabling adaptability to market shifts, but it requires careful reward mechanism design to avoid suboptimal outcomes. These techniques emphasize trade-offs between efficiency, accuracy, and interpretability, necessitating scenario-specific selection.

3.2. Asset pricing

Machine learning has made significant strides in asset pricing, providing tools to analyze complex datasets and improve valuation accuracy. Traditional models often struggle to account for non-linear relationships and interactions among variables. In contrast, ML algorithms can analyze multiple factors simultaneously, leading to more accurate asset valuations [5][6].

For example, gradient-boosting machines and ensemble methods have been employed to improve the accuracy of stock price predictions by considering various market indicators and economic factors [7]. The following table 3 summarizes some of the prominent ML techniques used in asset pricing:

3.3. Risk management

Effective risk management is crucial for financial institutions to mitigate potential losses. ML and AI technologies enhance risk assessment by analyzing historical data and identifying potential risk factors. Techniques such as anomaly detection and clustering can help in identifying unusual patterns that may indicate financial distress or market volatility [4][8].

For instance, a study by Chen et al. [4] demonstrated that using ML algorithms for credit risk assessment significantly improved the accuracy of default predictions compared to traditional logistic regression models. Additionally, Bontemps et al. [8] highlighted the use of clustering algorithms to segment portfolios based on risk profiles, allowing for more tailored risk management strategies.

The following Table 4 illustrates the application of ML techniques in various aspects of risk management:

Table 4 categorizes ML applications across risk management domains: logistic regression, decision trees, and random forests predict credit defaults; neural networks and SVM forecast market risks; anomaly detection and clustering identify operational risks like fraud; time series models analyze liquidity risks; and Monte Carlo simulations with reinforcement learning optimize portfolio strategies. Beyond these, ML drives intelligent risk mitigation through real-time anomaly detection for fraud monitoring, clustering to segment clients by risk profiles, ensemble learning for robust predictions, and NLP to assess market sentiment from news or social media. However, challenges such as data quality gaps, limited model interpretability, and algorithmic bias persist. Addressing these requires ethical frameworks and transparent practices to ensure compliance and sustainable integration of ML in risk management systems.

4. Future directions

The future of ML and AI in financial pricing holds immense potential. As technology continues to evolve, we can expect further integration of these tools with big data analytics, enabling more sophisticated pricing models that account for a wider range of variables [7][8]. Additionally, the rise of alternative data sources, such as social media sentiment and satellite imagery, presents new opportunities for enhancing pricing accuracy and market predictions [5][6].

However, the adoption of ML and AI in financial pricing also raises ethical considerations, including data privacy, algorithmic bias, and transparency in decision-making. It is essential for financial institutions to establish ethical guidelines and frameworks to ensure the responsible use of these technologies [4][8].

5. Conclusion

The convergence of AI and ML technologies has redefined financial pricing paradigms, fostering unprecedented advancements in predictive accuracy and operational efficiency. By enhancing algorithmic trading, asset pricing, and risk management, these technologies have improved the accuracy and efficiency of financial decision-making. The ability of ML and AI to process vast amounts of data and identify complex patterns allows financial institutions to make more informed decisions, ultimately leading to better investment strategies and risk mitigation.

As the field continues to evolve, the potential for further advancements in pricing models and strategies is significant. The incorporation of alternative data sources, such as social media sentiment and satellite imagery, presents new opportunities for enhancing pricing accuracy and market predictions. Moreover, the ongoing development of more sophisticated algorithms will likely lead to even greater predictive capabilities.

However, addressing ethical considerations will be crucial to ensure the responsible and equitable use of ML and AI in finance. Issues such as data privacy, algorithmic bias, and transparency in decision-making must be prioritized to build trust among stakeholders. Financial institutions should establish robust ethical guidelines and frameworks to navigate these challenges effectively. By doing so, they can harness the full potential of ML and AI while promoting a fair and sustainable financial ecosystem. The future of financial pricing, enriched by these technologies, promises to be more dynamic, insightful, and responsive to the complexities of global markets.