1. Introduction

The financial industry has undergone a major technological transformation in recent years thanks to artificial intelligence (AI). As financial markets become more complex and the data keeps growing at an exponential rate, the need for innovative solutions that can support better decision making, operational efficiency, and customer experience has become more critical [1]. AI technologies like machine learning, natural language processing and big data analytics have come into play as main enablers of these changes, helping financial institutions adapt to the fast-changing environment and stay relevant [2].

AI systems have an advantage that more recent work emphasized that set them apart from a regular computer software system. While software is deterministic, AI systems (and machine learning (ML) systems in particular) are probabilistic and can introduce significant uncertainty and verification/testing problems [3]. This instinctive confusion is also more common in finance, where AI is already deployed for risk management, fraud detection and algorithmic trading. These uncertainty are important to be aware of to successfully apply AI technologies to the financial market [4].

AI is the word used for machines that think, learn, reason and solve problems in human-like ways. Fintech organizations employ AI to analyze huge quantities of data, identify patterns and forecast the market to inform investment, risk mitigation and customer interactions [3]. AI-based apps from algorithmic trading to fraud detection and risk analysis transformed operations and models of financial business. Not only do the tools enable better accuracy and reduce processing time, but institutions can make data-based decisions to mitigate risk and achieve higher returns.

2. Application

AI has changed a lot in the financial sector, and offered some new possibilities for how the process can be more effective, less risky and more strategic. The fintech applications of AI are as endless as they come, from portfolio and risk management to algorithmic trading. These principal uses are discussed here.

2.1. Portfolio Management

Portfolio management — this means managing assets in order to achieve the best possible returns at the lowest possible risk. In the old days, this was based on historical evidence, simple calculation, and human judgment. But the AI has reshaped portfolio management with new instruments that enable optimization, risk assessment and decision making [5, 6].

Financial technology has some of the most extreme cases for AI and ML use-cases in portfolio management with deep learning algorithms and high AI driven architecture [6]. By employing neural algorithms such as CNNs, recurrent neural networks (RNNs) and generative adversarial networks (GANs), we were able to make financial market, opportunity and portfolio risk predictions with much more freedom and precision than prior models [6].

The main areas that AI is being utilized in portfolios are execution of investment strategies and optimal risk-adjusted returns. ML models can sift through hundreds of thousands of years of historical market information to extract patterns and trends not observed by human experts. These patterns enable AI applications to make trades on demand and optimize portfolios in real time [7]. This allows portfolio managers to react rapidly to the market’s changes and capture opportunities as they arise.

AI can also uniquely handle large datasets, unknown patterns and changing market dynamics that other models like Modern Portfolio Theory (MPT) and Capital Asset Pricing Model (CAPM) have not addressed [7]. AI presents you with an adaptive, adaptive asset allocation strategy geared to today’s volatile financial markets [8].

Portfolio management with RL is a great development as AI models can be taught to optimize portfolio allocation according to the current market conditions. RLs support learning through iteration and thus the AI agent is constantly updating how to best reward its long-term investments, making portfolio management robust [8]. AI has also been mated with classical finance ideas into hybrid systems that take the best of both approaches and provide improved prediction and decision support [6].

Multivariate data and big data are two main aspects of AI-driven portfolio management. More data, more real estate, and portfolio optimization can teach AI models a lot [8]. Such data sets might also encompass non-financial information such as sentiment in social media, news, economic signals which will give a better understanding of the market.

The short version is that AI is revolutionizing portfolio management through enabling sophisticated analytics, risk management and personalized investment options. AI can process large datasets in real-time, which gives portfolio managers an advantage over performance and a fast-track to market movement. As markets expand, AI in portfolio management is only going to get bigger — more innovation and more efficiency.

2.2. Risk management

Embracing AI into financial risk management has changed the way banks assess, measure and manage risk. Artificial Intelligence improves performance, precision and speed for regulating market risk, credit risk and fraud prevention. In this part, we will see the application of AI in credit scoring, fraud detection and market risk characterization.

AI has greatly advanced risk management with better data pre-elaboration, modeling, stress testing and validation [9]. It is used in data quality management, fraud detection and business model migration making it an essential part of today’s risk management programs [9]. To mitigate risks and standardize the adoption of AI, adhering to guidelines such as the European Commission’s AI Act and other emerging frameworks like NIST AI Risk Management Framework and ISO/IEC 23894 are also vital in order to standardize AI use [10]. Besides, AI TRiSM is designed to improve trust, security, and reliability of AI assets, as well as improve financial risk management through threat mitigation and collaboration [11].

2.2.1. Credit Scoring and Approval

Credit scoring is one of the core elements of risk management because it has a direct impact on lending. Pre-modern credit scoring systems are based on past credit history and thin demographic information, which creates biases and systemic inequities. The incorporation of AI, mainly through the creation of synthetic data, is a promising way to remove these biases and increase the accuracy of the credit scoring model [12]. Synthetic data generation algorithms like Generative Adversarial Network (GAN), Variational Autoencoder (VAE) and Differential Privacy (DP) are employed to generate the neutral datasets that mitigate biases and optimize the performance of the model. These approaches help eliminate gender, race, and class biases that lead to unfair lending [12].

But artificial data for credit scoring doesn’t come without difficulties. We need to deal with models’ interpretability, transparency, and regulatory compliance so that the synthetic data doesn’t create unintended side effects, like new biases or rewriting of risk profiles [12]. Explainable AI (XAI) techniques are proposed to overcome these interpretability issues and to let stakeholders know the accuracy of AI-based credit scoring models [12].

Moreover, the suitability of different AI models for scoring credit is still being studied. Multilayer perceptron networks and deep belief networks are other techniques applied to credit scoring; however, these don’t always perform better than shallower models and they require more computing resources to build [13]. Ensemble techniques such as XGBoost have, on the other hand, been proven to be better for credit scoring and are therefore one of the most popular approaches for getting good predictive accuracy. Bayesian statistical testing methodologies have also been proposed to replace frequentist techniques as a more trustworthy method to assess model efficacy in credit scoring [13].

2.2.2. Fraud Detection and Prevention

Fraud prevention is an integral part of risk management because financial institutions are vulnerable to sophisticated fraud. Traditional rule-based systems can’t keep up with the changes in fraud trends, making it inefficient and false positive prone. Artificial intelligence-based fraud detectors use machine learning algorithms that continuously scan transaction data looking for suspicious patterns and behaviors indicating a scam.

Interestingly, Random Forest algorithm has proven to be particularly useful for the detection of financial fraud as it can identify the fraud transaction with high accuracy, precision and recall [14]. As an ensemble technique, Random Forest has the advantage that it can process a lot of data and produces high-quality models that scale easily to invisible situations. In addition, data visualizations like graph visualizations and line charts have been used to identify trends and patterns in transaction data and to identify and prevent fraudulent activity early on [14].

AI models such as AI TRiSM are also used for fraud detection by improving the security and reliability of artificial intelligence models in financial services [11]. AI TRiSM is responsible for making fraud detection systems resilient to adversarial attacks and in compliance with the regulations. It’s an approach that enables banks to better identify and block fraudulent activity, protecting both the institution and the customer.

In conclusion, AI has significantly improved risk management in the financial industry by ensuring that credit scoring, fraud detection, and market risk analysis are more accurate, efficient and flexible. By integrating AI into these workflows, banks are better able to control risk, remain compliant with the changing regulatory landscape and be ethical.

2.3. Algorithmic trading

Robotic trading relied more and more on AI to predict the market, make trades optimal, and place trades faster. AI is a very good alternative to human traders in high-frequency trading (HFT), data mining, and forecasting. Here, we will see how AI can be applied to algorithmic trading using synthetic data, deep reinforcement learning and hybrid AI algorithms.

ML and deep learning (DL) AI systems can quickly digest big data in real-time, which results in better trade accuracy [15]. Combine data from technical, fundamental, and investor sentiment and AI systems to pick out sophisticated market movements. HFT can be leveraged by traders to profit from fluctuations in prices within fractions of a second. We use deep learning algorithms, such as LSTMs and CNNs, to make market predictions and for producing accurate trading signals.

Another trend is the addition of artificial information in trading algorithms [16]. Traditional back testing draws on historical records and leaves out some of the rare or brisk events. Artificial neural nets such as GANs or VAEs are algorithms to simulate various market scenarios, such as extreme volatility and shocks. It allows traders to fine-tune strategies, parameterize, and stress-test models to make sure they are robust.

Deep reinforcement learning (DRL) is an algorithmic trading strategy with a revolutionary approach to learning continuously to improve the trading decision. The Trading Deep Q-Network (TDQN) Trading Deep tries to pinpoint the right trading point to get the highest Sharpe ratio (a benchmark of risk-adjusted returns). The artificial trajectory training has also been successful with its adaptive decision-making and optimization on the market feedback [17].

In summary, AI has changed algorithmic trading through machine learning, synthetic data generation, and deep reinforcement learning. These have given traders flexibility, sensitivity, and efficiency to successfully navigate complicated financial markets.

3. Current limitations and future prospects

While AI in finance has been revolutionary, some of the barriers are still waiting to be conquered. Its first issue is comprehensibility and accessibility. AI models, especially those based on synthetic data, can also introduce unintended biases or misrepresent risk profiles and compromise trust and regulation. We need the transparency in AI decision-making, especially in high-risk financial applications such as credit score and fraud detection [12].

In addition, deep learning models are cumbersome in terms of computation cost, overfitting, and generalization if you are dealing with non-representative data. Such problems hamper the effectiveness of these models for some applications, such as credit scoring, where reliability and precision are important [13]. Also, probabilistic AI models come with lots of uncertainty, making testing and verification difficult. This uncertainty is essential to manage, especially in complex financial applications where the consequences of making a mistake can be massive [14].

For all these hurdles, AI in finance has a bright future. The potential for synthetic data generation is massive for stress-testing and risk mitigation. Artificial intelligence-powered synthetic data is able to model multiple market scenarios such as extremes, uncommon occasions and volatility which are typically impossible with manual back testing. This feature is especially useful for high-frequency trading (HFT) systems because it allows traders to build strong models that can handle various market environments [16].

A second exciting next frontier is the use of DRL in financial decision-making. DRL, especially through channels such as TDQN, offers a real-time method of maximizing trade. Adapting to the changing markets with continuous learning from real-time feedback, DRL is able to be flexible and perform better in extreme market situations [17]. In addition, the combination of hybrid AI models with deep learning combined with genetic algorithms or other optimization methods should further advance prediction performance and flexibility in financial markets [11].

Regulation must change in the face of AI as it advances. Regulations like NIST AI Risk Management Framework and ISO/IEC 23894 will be key in guaranteeing the consistency, security and equity of AI use cases in the financial industry. AI TRiSM (AI Trust, Risk, and Security Management) structures will also be growing since it ensures the security, trust, and visibility of AI systems, making it more acceptable and efficient in finance [11].

Overall, there are still a lot of nuances of interpretability, compliance and confusion, but the AI future in finance is quite bright. New applications in synthetic data generation, deep reinforcement learning, hybrid AI models and better regulatory systems will overcome the current bottlenecks and propel AI finance forward. These innovations will make AI-based financial solutions more resilient, reliable and perform better for the financial ecosystem and become more adaptive and effective.

4. Conclusion

The paper has introduced AI use cases in finance such as portfolio management, risk management, and algorithmic trading in this paper. In a systematic survey of the literature, the revolutionary potential of AI was presented, as well as the limitations of AI (problems with interpretability, transparency, regulatory compliance, and the very fact that AI models are uncertain). All of these hurdles need to be averted to make AI in finance successful.

But despite all this, AI in finance has a bright future ahead of it. Synthetic data generation, deep reinforcement learning and hybrid AI models will be the future of financial decision making, risk-management and trading. And regulation will facilitate the legal and ethical adoption of AI.

This analysis aims to provide a clear understanding of the capabilities and limitations of AI in the context of finance. The paper also seeks to draw conclusions and offer recommendations based on the current understanding of AI in finance, as this is what the future should look like for researchers, practitioners and policymakers who are interested in how AI can help create a more efficient and flexible financial system.