1. Introduction

This paper explores the dynamic intersection between artificial intelligence and the financial sector, uncovering the transformative potential that emerges at their confluence. The synergy between these domains has yielded pioneering breakthroughs, most notably in the form of robo-advisors and AI-fueled risk assessment methodologies. Through these innovations, established norms in investment strategies and risk management are being reshaped, laying the foundation for a new era of financial operations. This introduction offers a succinct glimpse into the key terminologies, the central research inquiry, and the underlying structure that guides our discourse.

1.1. Definition and Overview of Key Terms

At the heart of this exploration lies the concept of artificial intelligence (AI), a manifestation of human-like intelligence in machines, endowing them with the capacity to learn, reason, and make judicious decisions. Within finance, the embodiment of AI involves the strategic deployment of algorithms and models to amplify investment management, risk assessment, and fraud detection. Among these advancements driven by AI, robo-advisors take centre stage as transformative platforms, harnessing the prowess of data analytics to personalize investment recommendations and navigate the intricate landscapes of risk.

1.2. Key Research Question

In this transformation landscape, our inquiry finds its anchor in a central question: How does the fusion of artificial intelligence recalibrate the contours of investment management, risk assessment, and fraud prevention within the financial domain? This overarching question propels our exploration, compelling us to dissect the manifold benefits, complexities, and uncharted dimensions that AI introduces to the financial landscape.

1.3. Article Structure

The roadmap of our exploration takes shape as follows: Beyond this introductory foray, we embark on an exhaustive examination of AI's imprint on investment management. This expedition uncovers the pivotal role played by robo-advisors and AI-driven portfolio recommendations. Our discourse subsequently navigates to the realm of risk assessment and management, with a spotlight on AI's acumen in deciphering latent risk indicators and fortifying preemptive response strategies. Concomitantly, we delve into the crucial domain of fraud detection through AI-orchestrated transaction surveillance. As we culminate this voyage of discovery, we skillfully weave these thematic threads together, offering illumination into the profound ramifications that AI bestows upon the financial landscape and anchoring its significance within the broader canvas of our interconnected world.

1.4. Implications and Beyond

Extending our perspective beyond the immediate scope of our investigation, it becomes evident that the transformative effects of AI within the financial sector extend beyond its technical facets. This evolution ushers in a paradigm shift in how financial institutions approach to risk, investment, and operational methodologies. As AI progressively takes on roles traditionally reserved for human expertise, questions surrounding accountability, ethics, and regulatory frameworks emerge. Moreover, the fusion of AI and finance embodies the accelerating pace of technological advancement shaping our global economic landscape. As we delve deeper into the following sections, each dedicated to dissecting AI's impact on investment management, risk assessment, and fraud detection, we unravel not only the ways AI redefines financial paradigms but also its potential to reshape our fundamental understanding of financial systems in a digitally driven era.

In essence, this paper encapsulates the ongoing metamorphosis fueled by AI in the financial sector, where conventions are upended, possibilities are magnified, and innovation flourishes, underscoring the intricate relationship between cutting-edge technology and the intricate world of finance.

2. Literature Review

When it comes to the financial sector, the application of artificial intelligence has shown great potential. Robo-advisors have attracted significant attention in recent years due to efforts by regulators and investors to understand the changing investment management landscape. However, in 2013, AI took a significant leap forward as the top disruptive innovation in the financial market. By integrating financial technology (Fintech) into the portfolio management process, AI primarily utilized algorithms designed to optimize various components of the investment portfolio, such as asset allocation, tax management, product selection, and trade execution. This technology has brought about changes to the investment management industry by rethinking business models and expanding the wealth management capabilities of their customer base. In 2014, venture capitalists invested nearly 300 million dollars in artificial intelligence, and by 2020, it is forecasted that the assets under management of AI in the US will reach approximately 2.0 trillion dollars, representing a compound annual growth rate of 68% over the next five years (Ahmed et al., 2022). Many corporations utilize AI to change their operation. In the same way, artificial intelligence has fit into financial services, especially in risk assessment and management. The complexity and high risk of the financial industry make accurate risk assessment and management essential (Yi et al., 2023). Through its powerful data analysis and model-building capabilities, AI technology offers financial institutions a new way to identify, quantify, and manage risk. Nowadays, delayed payment is a common phenomenon. The delay of the transaction will bring huge opportunity costs to the enterprise, but it will also affect the business decision of the company (Niu et al., 2022). Many small and medium-sized corporations have been negatively affected by delayed payments. Nevertheless, the use of AI models such as SCF risk analysis will help the company assess the risk and help manage funds (Shanmuganathan, 2020).

Through the mining and analysis of large-scale data, AI can identify hidden risk signals in the market to formulate response strategies in advance when the market is volatile. In the lending and credit space, AI can analyze vast amounts of customer data, such as credit reports, income records, and payment history, to predict a borrower's credit risk. Artificial intelligence may use algorithms to calculate the data and give suggestions to financial portfolios (Ahmed et al., 2022). Although AI has great potential in financial risk assessment and management, financial institutions still need to be careful to ensure that various risks and challenges are taken into account in the application process. Continuous improvement and refinement of AI models, combined with human expertise and experience, will enable more effective financial risk management strategies that will enhance the stability and sustainability of the entire financial industry. At the same time, the government and regulators should also strengthen the supervision of artificial intelligence in the financial field. The adoption of artificial intelligence and machine learning is fundamentally changing trading and investment decisions. At the same time, financial research is responding to the need better to understand the economic impact of AI and machine learning (Goodell et al., 2021). This article will discuss the different uses of AI in the financial field.

2.1. The crucial role of AI in financial transaction detection

The advantage of artificial intelligence technology in the financial markets is reflected in its ability to detect potentially fraudulent activity by monitoring transaction behavior and patterns to ensure the security and stability of transactions. This complex process encompasses several key steps aimed at identifying unusual transactions and detecting possible fraud in advance. First, the data collection and preprocessing phase is critical. Financial institutions accumulate a large amount of transaction data, including transaction amount, time, location and other information, which comes from a variety of sources, including ATMs, mobile applications, websites and other channels(Hasan and Rizvi, 2022).On this basis, the data is processed through data cleansing, conversion and standardization to ensure data consistency and analyzability, followed by feature engineering, which is a key part of building an effective monitoring model. In the feature engineering stage, raw transaction data is transformed into interpretable features suitable for machine learning algorithms. This includes extracting key information from dimensions such as transaction time, location, and type. By calculating statistical metrics, formulating rules, and applying domain knowledge, feature engineering helps capture key features in transactions to better identify anomalous patterns (Maree, Modal and Omlin, 2021). Model training is at the heart of AI applications, where financial institutions train machine learning models, such as anomaly detection models or classification models, based on preprocessed data. These models determine anomalies in new transactions by learning normal patterns in historical transaction data. In this process, appropriate algorithm selection and model structure design directly affect the accuracy of the model. Anomaly detection and pattern recognition are the core tasks of artificial intelligence technology in transaction monitoring. The trained model can be applied to new transaction data, scoring each transaction to determine whether it conforms to normal patterns. Those transactions with high scores or not conforming to normal patterns will be flagged as potentially abnormal transactions, providing financial institutions with targeted monitoring direction. Real-time monitoring and response are an important guarantee for transaction security. In real-world applications, transactions need to be monitored in real time for early detection of potentially abnormal activity. The monitoring system will continuously track the transaction flow, and once potentially fraudulent activities are detected, financial institutions can take prompt action, such as freezing accounts or notifying customers in a timely manner to minimize potential losses (Bao, Hilary and Ke, 2020). However, it is worth noting that despite the many advantages of AI technology in monitoring transaction behaviors, the problem of false positives and underreporting still exists. Therefore, a combination of continuous model updates and human expertise is indispensable. As AI technology continues to evolve, trade monitoring will be able to perform more granular data analysis and model improvements to capture anomalous behaviors more accurately. The application of AI technology in the financial market provides a powerful tool for transaction monitoring, which helps financial institutions effectively identify abnormal transactions and safeguard market security and stability through accurate data processing, model training and real-time monitoring. In addition, AI can provide valuable support in the following areas:

2.2. AI and Portfolio Recommendation

Intelligent Portfolio Advice: Artificial Intelligence can analyze large amounts of investment data and market intelligence to provide investors with more accurate portfolio advice. Through in-depth analysis of the historical performance of different asset classes and market trends, AI can develop more optimized investment strategies for investors, thereby reducing risk and increasing investment returns. Moreover, the introduction of AI in the field of portfolio advice clearly differentiates it from traditional investment strategies. Prior to the introduction of AI, portfolio recommendations typically relied on limited historical data and market indicators for analysis. However, the introduction of AI allows investors to draw from a wider range of data sources, including historical prices, trading volumes, market sentiment and macroeconomic indicators. This comprehensive data analysis allows AI to assess the potential performance and risk of an asset more accurately (Gunjan and Bhattaharyya, 2022). Prior to the application of AI, investors may have been able to make investment decisions based on only limited information, making them vulnerable to market volatility and information asymmetry. However, through machine learning algorithms and deep learning techniques, AI can identify patterns and trends hidden in large-scale data and provide more accurate market forecasts. This enables investors to better understand the relationships between different asset classes and develop more optimized investment strategies for them (Beccalli, Ellito and Virili, 2020). Future trends in the use of AI in portfolio advice will continue to evolve. As deep learning technologies continue to evolve, AI will be able to capture complex market patterns and provide investors with more insightful advice more accurately. The application of reinforcement learning techniques will enable AI to continually optimize investment strategies from its interactions with the environment to better cope with market uncertainty. In addition, future trends will emphasize the interpretability of AI models to increase investor trust in model recommendations. In summary, the development of AI in the field of portfolio advice will provide investors with more accurate, personalized and reliable investment advice, further reducing risk and increasing investment returns.

2.3. AI and Risk Management

Risk management: The application of artificial intelligence in the financial field also includes risk management. By mining and analyzing a large amount of market and investment data, AI can identify potential risk signals and help investors actively formulate response strategies to protect their assets during market volatility. Risk management often relies on traditional statistical methods and empirical judgment. However, with the introduction of AI, risk management has become more refined and intelligent. Through machine learning and data mining techniques, AI can analyze large amounts of market data to discover correlations between different assets and potential risk factors. This enables investors to identify market risks more accurately and take timely preventive measures (Bussmann et al., 2020). In addition, the application of AI in risk management is reflected in the real-time monitoring of market events and sentiment. By analyzing social media, news reports and market sentiment indicators, AI can capture fluctuations in market sentiment and predict potential risk events. This allows investors to react earlier to mitigate adverse effects. As big data and computing power increase, AI can process large-scale data and recognize smaller risk signals. At the same time, the development of reinforcement learning and automated trading techniques will enable AI to make risk assessment and response decisions in a shorter period of time. In the future, AI will play a more critical role in risk management, helping investors to better protect their assets and realize long-term stable investment returns.

2.4. AI and Personalized investment plans

Personalized Investment Plans: Based on an investor's risk appetite, goals and financial situation, AI can customize personalized investment plans. This helps ensure that investment decisions are more accurately aligned with the investor's goals, thereby increasing the likelihood of investment success. Then, in the past, creating a personalized investment plan was usually based on the experience and basic information of the investment advisor. However, with the use of artificial intelligence, investors can enjoy more refined and personalized investment advice. Artificial intelligence can customize the best portfolio and strategy for each investor by analyzing factors such as their risk tolerance, investment time horizon, and financial goals (Fatima, Desouza and Dawon, 2020). This allows investors to better align themselves with the market while reducing the risks associated with mismatched investments. As AI technology continues to advance, investors will be able to integrate personal financial data, social information and market forecasts more deeply for more comprehensive investment planning. With the rise of quantitative investing and algorithmic trading, personalized investment plans will be more integrated into the automated investment process, enabling investors to manage their portfolios more efficiently.

2.5. AI and Automated trading

Automated trading: AI technology can also be used to automate trading by executing trading operations based on predefined rules and parameters. This can improve the efficiency and effectiveness of trading by eliminating the influence of human emotions and enabling faster and more accurate trading (Tran, Pham-Hi and Bui, 2023). In traditional trading, human investors can be affected by mood swings, fatigue and cognitive biases, leading to erratic and inconsistent decision-making. However, automated trading systems that introduce artificial intelligence can base trading decisions on rigorous algorithms and data analysis, eliminating the interference of emotional factors in trading. This means that trading decisions are more accurate and consistent, thus reducing the risk of trading errors due to emotions. Automated trading systems also excel at monitoring market volatility in real-time. Artificial intelligence can quickly analyze large amounts of market data and make trades in real-time based on pre-set rules and strategies. This enables the system to react instantly to rapidly changing market conditions and capture more trading opportunities, thus improving trading efficiency and effectiveness (Borch, 2022). In the future, automated trading will further incorporate advanced artificial intelligence technologies such as deep reinforcement learning and adaptive learning algorithms. This will make the automated trading system more intelligent and adaptive, capable of adjusting trading strategies according to market changes to achieve more stable investment returns. Meanwhile, with the rise of blockchain technology and the cryptocurrency market, AI will help to better understand and predict the behaviors of these emerging markets, bringing more possible investment opportunities to investors.

In short, the application of AI in the financial sector is gradually reshaping the investment and risk management landscape. It provides investors and financial institutions with a range of innovative and intelligent tools to help make informed decisions and achieve superior results.

3. Conclusion

The application of artificial intelligence in the financial sector shows great potential to revolutionize portfolio recommendations, risk management, personalized investment plans and automated trading. By analyzing large amounts of financial data and employing advanced algorithms, AI can provide personalized investment recommendations, identify hidden risk signals in the market, and improve the decision-making efficiency and risk management capabilities of financial institutions.

However, we must also recognize the limitations of AI applications in finance. Data quality and reliability, lack of interpretability, model risk and overfitting, as well as legal and ethical issues, are all areas that require attention. These limitations require us to exercise caution when applying AI techniques and combine them with human expertise and experience to ensure accuracy, transparency and reliability in decision-making.

Future research and practice should continue to explore ways to overcome these limitations and better apply the potential of AI in finance. In addition, the role of regulators and governments was emphasized, who should increase their oversight of AI in finance to ensure compliance and stability.

Overall, AI has a revolutionary impact on the financial sector. By providing personalized investment advice, improving risk management and enhancing decision-making efficiency, AI presents tremendous opportunities for financial institutions and individual investors. However, understanding and overcoming the limitations of AI applications is key to achieving sustainable and responsible applications. It is only through technological advancement and regulatory cooperation that we will be able to better utilize the potential of AI and drive the financial industry to new heights.

3.1. Limitation

This paper does not make specific reference to the sources and timeframe of the data used. As the financial industry is a dynamic and changing field, relying on outdated or inaccurate data may affect the overall understanding of the application of AI in finance. Although this paper mentions the application of AI in different fields, such as portfolio recommendation and risk management, the discussion of each field is relatively general and does not delve into specific algorithms, cases or practical experiences. A more detailed study could further demonstrate the specific implementation and effects of AI in finance.

3.2. Future Agenda

In the future, the application of AI in finance will continue to evolve and innovate. As deep learning technology continues to advance, AI will be able to capture complex market patterns and provide more insightful recommendations more accurately. The application of reinforcement learning techniques will enable AI to continually optimize investment strategies from its interactions with the environment and better cope with market uncertainty. Future trends will also emphasize the interpretability of AI models to increase investor trust in model recommendations. Future trends in the use of AI in the area of portfolio advice will continue to evolve to provide investors with more accurate, personalized and reliable investment advice. In addition, AI is expected to continue to play an important role in areas such as risk management, personalized investment planning, and automated trading to help investors better protect their assets and achieve long-term stable investment returns.