A new era of financial services: How AI enhances investment efficiency

Financial investment is an important part of the modern economy, promoting economic growth and wealth accumulation through the efficient allocation of capital. However, with the rapid development of global financial markets, the investment environment has become increasingly complex. Investors not only need to cope with a large amount of data and information, but also to capture market opportunities and avoid risks in a timely manner. Traditional investment analysis methods and tools are often overwhelmed when dealing with these complexities. Over the past period of time, the rapid advancement of artificial intelligence (AI) technology has brought new hope to financial investment (Holzinger et al., 2023). Through its powerful data processing capabilities, pattern recognition, and predictive analytics, AI is able to cope with the complexity and dynamics of the financial market, effectively enhancing the efficiency of traditional financial institutions and demonstrating great potential and broad application prospects. Financial complex systems are networks of multiple interconnected financial entities and activities that exhibit complex interactions and dependencies. These systems typically exhibit nonlinear behavior, dynamic evolution, and have self-organizing features. Traders, financial firms, and investors, as the core elements of financial complex systems, together constitute the operating mechanism of financial investment markets through complex interactions and information exchange. In this study, we will discuss how AI technology can empower financial investments (Ahmed et al., 2022) to enhance their efficiency from the perspective of financial complex systems and analyze their limitations and potential drawbacks from a new perspective. The rapid development and application of AI technology, especially in the sector of financial investment, not only foretells a fundamental change in the way the financial market operates, but also strengthens the technological foundation and clarifies the potential direction for the future development of the financial industry. Digital intelligence (Vijayakumar et al., 2022) finance will accelerate into a new era. The financial market is an intricate and ever-evolving landscape where trading operations entail the processing of vast amounts of data, instantaneous analysis, and swift decision-making (Sharma & Gupta, 2024). Conventional trading practices, heavily dependent on extensive human resources and intricate computational models, are not only labor-intensive and time-consuming but also susceptible to delayed judgments and suboptimal decisions. As financial markets become increasingly globalized and technology races ahead at an unprecedented speed (Ahmed et al., 2022), the intricacy of trading operations grows, compelling traders to make swifter and more accurate decisions. In this context, AI technology shows strong potential to simplify complex financial transaction activities and improve efficiency and accuracy (Tanaka et al., 2024). AI technology helps financial institutions and investors cope with the complexity and uncertainty of the market by judging market trends, optimizing portfolios and providing real-time decision support, thus effectively improving financial investment efficiency at all levels. For traders, review is a crucial part of the investment process. By analyzing technical indicators of the candlestick charts, the review process seeks out the characteristics of a stock's trend and forecasts the stock price movements. However, this process is not only time-consuming and labor-intensive but also demands that investors possess profound technical analysis expertise and sharp judgment. AI technology showcases its capability to forecast price movements across a diverse array of assets, including stocks, bonds, commodities, and currencies, by analyzing various factors such as historical price data, trading volumes, market news, and socioeconomic indicators. Currently, the application of AI in image recognition has become highly advanced, especially with the development of convolutional neural network (CNN) technology (Liu et al., 2022). Based on this idea, a study that uses CNNs for financial image analysis to forecast stock price trends was proposed. The method uses a CNN to directly extract features from historical market data implicit in candlestick charts, such as daily opening price, daily high, daily low, daily closing price, and trading volume, to model the relationship between the standardized charts of price and volume data and the future stock price movements, thus enabling the prediction of stock prices. The authors selected US stock price data from 1993 to 2019 for prediction. The results demonstrate that the CNN-based approach significantly outperforms traditional trading strategies (e.g., price momentum and reversal) and one-dimensional time series forecasting methods (e.g., ARIMA and Long Short-Term Memory Networks [LSTM]). In predicting US stock returns, the CNN-based approach achieved an accuracy exceeding 53% and demonstrated exceptional performance in portfolio strategies, with an annualized Sharpe ratio of up to 2.4 for equal-weight portfolios. Additionally, through transfer learning, the CNN-based approach also performs remarkably well across different markets and time scales, providing a powerful tool for global investment strategies. This method not only substantially improves the efficiency of reviewing the market but also reduces errors in human analysis and judgment, enabling investors to focus more on strategy development and risk management, and enhancing the accuracy of trading decisions. In today's complex financial environment, portfolio optimization (Montazeri et al., 2023) is undoubtedly a highly challenging task. It requires investors to conduct detailed risk assessments of different asset classes under uncertain market conditions to identify potential opportunities and risks hidden within the market. This allows for dynamic optimization and adjustment of large-scale investment portfolios (Buonaiuto et al., 2023). The application of AI technology in the realm of investment portfolio optimization assists investors in constructing more robust portfolios through the sophisticated analysis and fine-tuning of the risk-to-return ratio. AI not only extracts features such as mean, variance, and correlation from vast data sets like stock prices and economic indicators to better understand market dynamics and asset performance, but it also employs machine learning algorithms such as decision trees, random forests, support vector machines, and neural networks to build portfolio optimization models by assessing the potential risks and returns of various types of assets (Kazemi et al., 2022). These models undergo continuous learning and adaptation, thereby improving their predictive accuracy and responsiveness. Recently, deep reinforcement learning has been interaged into the domain of asset portfolio optimization, utilizing the deep Q learning algorithm to inform asset trading decisions. In their study, each asset is treated as an environment, and the strategy is trained by considering the income from holding the asset as its return and the average income from other portfolio assets as the cash return. The reward is calculated based on the average return of the asset group, establishing a connection between the trading return in a single asset environment and the overall portfolio return. With a single hyperparameter setting, the trained strategy demonstrates adaptability to various trading scenarios, achieving optimal performance in 36 out of 48 experimental groups. Compared to those traditional mathematical analysis methods for estimating risk premiums or return predictions, AI technology can more effectively capturing profit opportunities under temporary market inefficiencies, thus enabling more accurate portfolio optimization strategies. With the rapid development of financial markets, real-time monitoring of unusual trading behavior has become critical. The complexity and high degree of interconnectedness of financial markets mean that any unusual trading activity can trigger a chain reaction, leading to market volatility and potential financial crisis. Unusual trading behaviors may include insider trading, market manipulation, money laundering, and other forms of financial fraud. These behaviors not only undermine the fairness and stability of markets, but can also cause significant losses to investors and financial institutions. Traditional monitoring methods, which rely on manual review and post-event analysis, are often unable to detect and respond to abnormal trading behaviors promptly, leading to regulatory lag and risk accumulation. This poses a significant challenge for regulatory authorities. In this context, the application of AI technology provides a new solution for real-time detection and adjustment of abnormal trading behaviors. Utilizing machine learning and big data analysis (Based Multidimensional Big Data Analytics over Clouds via Multi-Columnar Big OLAP Data Cube Compression), AI can rapidly process and analyze vast quantities of trading data to pinpoint abnormal trading patterns and potential risk factors. The real-time monitoring system is able to analyze transactions as they occur and making immediate decisions, thereby detecting and alerting to abnormal behavior in a timely fashion. Some researchers have proposed a real-time monitoring framework based on big data technology by integrating real-time trading data, financial news, social media content, and macroeconomic indicators, and this study used efficient data stream processing technology (e.g., Apache Kafka and Apache Flink) to construct an efficient real-time data stream processing framework. Integrating machine learning algorithms such as Isolation Forest for anomaly detection, LSTM for time-series analysis, and Graph Neural Networks for network behavior analysis, the proposed algorithm combination is capable of effectively identifying complex anomalous trading patterns (Bebin & Misbha, 2022; Prakash et al., 2023). The real-time monitoring and response system is designed to dynamically adjust thresholds, trigger alerts instantly, and generate detailed reports on abnormal behavior. This provides regulatory authorities with enhanced capabilities for swiftly responding to market anomalies, significantly improving both the timeliness and accuracy of anomaly detection. In today's highly competitive and rapidly evolving financial industry, managing operational costs and expenses is crucial for maintaining profitability and competitive edge. Financial institutions encounter various operational challenges, including elevated labor costs, cumbersome routine tasks, and complex risk management and data analytics requirements. The extensive application of AI technology in the financial sector not only demonstrates its robust potential for automating processes, enhancing risk management, optimizing customer service, and refining data analytics but also offers significant cost advantages, enhancing quality and efficiency in the financial investment sector. Intelligent customer service system is one of the major applications of AI technology in the financial investment sector. Traditional customer service relies on a large number of human resources, which not only consumes a large amount of human resources costs, but also may have the risk of data loss due to the confusion of manual records. An AI-driven intelligent customer service system integrates natural language processing (NLP), machine learning, and other AI technologies to automate the handling of customer service requests. These systems can understand and interpret user queries, provide instant and accurate responses, or direct users to appropriate solutions. Intelligent customer service reduces dependency on manual customer service, significantly lowers labor costs, and greatly improves response speed and customer satisfaction. This is due to its ability to operate 24/7 and manage a large volume of inquiries autonomously. The “Gong Xiaozhi” intelligent customer service system, launched by the Industrial and Commercial Bank of China in 2016, leverages advanced NLP technologies to achieve a 98% recognition rate of customer needs, setting a high standard in the industry. This enables rapid comprehension and efficient resolution of user inquiries, offering instant responses and significantly boosting service efficiency. The system operates seamlessly across over 20 mainstream channels, accommodating the diverse consultation needs of users and enhancing service convenience and user satisfaction. By integrating big data, customer profiling, and self-learning technologies, “Gong Xiaozhi” delivers comprehensive one-stop services, including proactive suggestions, query resolution, marketing recommendations, business transactions, and after-sales tracking. Additionally, it offers engaging conversational features, making interactions more enjoyable for customers. This system exemplifies the transformative impact of AI in financial customer services, enhancing operational efficiency and customer experience through innovative applications. The financial sector requires a high level of precision and data protection, as even minor human errors can lead to significant risks. Recently, Robotic Process Automation (RPA) (Perdana et al., 2023) has seen rapid advancements both domestically and internationally. The integration of RPA with technologies such as NLP and computer vision has substantially enhanced its problem-solving capabilities. AI-powered RPA effectively automates repetitive tasks that follow well-defined rules, adhering to predefined regulations and procedures. These tasks, including data entry and report generation, are often tedious and monotonous when performed manually, resulting in potential errors and considerable time consumption. Automating these routine tasks not only increases efficiency but also optimizes human resources by relieving employees from repetitive duties. This transition enables employees to focus on higher-value activities, such as strategic planning and innovative business development, thereby enhancing job satisfaction, fostering creativity, and improving overall operational efficiency and competitiveness. In 2023, Hangzhou Bank introduced an intelligent robot equipped with RPA technology to automatically analyze and process loan applications, as well as handle other operational tasks. These robots are capable of mimicking the actions of human loan officers, automatically performing tasks such as processing loan applications, verifying applicant details, assessing credit risks, and making approval decisions. This automation streamlines the loan approval process, making it more efficient, accurate, and reliable, which in turn reduces approval times and boosts customer satisfaction. Moreover, the smart robots can autonomously collect applicants’ personal data, financial standing, and credit histories from diverse data sources. Utilizing optical character recognition and NLP technologies, they can verify the authenticity and accuracy of the documents and information submitted by applicants. These “digital employees” can operate 24/7, significantly reducing the processing time for individual business tasks, and achieve a 100% accuracy rate (Mihalcea et al., 2006) (Figure 1). In conclusion, through automation driven by AI and machine learning, financial service providers can significantly reduce costs and deliver more personalized and efficient services to meet the growing demands of modern consumers. With the support of Artificial Narrow Intelligence (ANI), the efficiency of financial institutions in handling individual tasks has markedly improved (Holzinger et al., 2023). As digital systems evolve and large model technologies are applied, it is expected that the accuracy and efficiency of these services will further expand, ushering the financial industry into a new era of heightened efficiency and security. Since the beginning of 2023, the emergence of large model technologies, exemplified by ChatGPT with its powerful content generation and text comprehension capabilities, has sparked a new wave of AI innovation worldwide. The financial industry, with its rich resources in information, data, knowledge, and talent, offers an ideal environment for the exploration and implementation of large model applications. Leveraging advanced digital infrastructure, large model technology is its deep integration with the financial sector. 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