Research on the Application of Artificial Intelligence in the

Financial Field

Zhiheng Tang

College of Computer and Cyberspace Security, Fujian Normal University, Fuzhou, Fujian,350000, China

Keywords: Artificial Intelligence, Financial Application, Financial Risk.

Abstract: With the development of high and new technology, artificial intelligence has become a powerful productivity

that can not be ignored in modern times, especially in the financial industry. Artificial intelligence technology

brought new impetus to the financial industry, financial processing efficiency significantly increased, showing

its creativity and intelligence. This paper primarily examines the use of artificial intelligence in the financial

sector and delves into the potential risks and challenges associated with its implementation. This paper aims

to summarize the current research progress, classify the application of artificial intelligence in the financial

industry, and look forward to the future development. By analyzing the existing research, this article hopes to

provide a reference for further research, promote the sustainable development of artificial intelligence in the

financial sector. Specifically, this article will discuss the artificial intelligence in the smart interest, risk

management, personalized financial products, such as custom applications, data quality analysis, algorithm

of prejudice and systemic risk simultaneously, putting forward the corresponding solution and the future

research direction. This paper concludes that although artificial intelligence can bring a lot of convenience to

the financial industry, there are still risks that cannot be ignored.

1 INTRODUCTION

With its wide range of applications and powerful

capabilities, artificial intelligence has become one of

the three cutting-edge technologies in the 21st century

(Wang, 2017). Microsoft Copilot ChatGPT,

Midjourney, and other breakthroughs in artificial

intelligence demonstrate exceptional learning

capabilities and natural language processing skills.

These AI technologies are rapidly integrating into

various fields such as healthcare, education, industry,

and finance. Among them, the combination of

artificial intelligence and the financial sector is

particularly conspicuous, artificial intelligence for the

financial sector to achieve the value creation, and

gradually realize intelligent, personalized and

customized financial services.

Because financial markets generate vast amounts

of data, they offer abundant resources for the

advancement of artificial intelligence, which in turn

drives further growth in the financial industry.To err

on the side of the development of financial technology,

https://orcid.org/0009-0005-1420-1318

financial institutions to speed up the digital

transformation in the backdrop of the financial sector

combined with artificial intelligence and artificial

intelligence to the financial industry has brought new

opportunities at the same time it still has potential

risks, both at home and abroad attach great

importance to the development of the theory of

artificial intelligence research and practice, But the

applications in the field of artificial intelligence in the

financial the lack of a systematic review and

summarize. Liao Gao, ting-hui li duo to the artificial

intelligence application in the field of artificial

intelligence research is summarized, from generalizes

the development stage and the development direction

of (Liao, 2023). Shouyang wang and other scholars to

generate type of artificial intelligence, especially

chapgpt generalizes the application in the field of

finance, and put forward solutions to its risk (Wang,

2023).

This research will be detailed from the main

application fields of artificial intelligence, and related

fields to carry out the main problems and puts forward

the main problem of solution and the paper points out

Tang, Z.

Research on the Application of Artiﬁcial Intelligence in the Financial Field.

DOI: 10.5220/0013224100004568

In Proceedings of the 1st International Conference on E-commerce and Artiﬁcial Intelligence (ECAI 2024), pages 307-313

ISBN: 978-989-758-726-9

307

the future development in this field. This article

structure arrangement is as follows: the second part

will introduce artificial intelligence different

scenarios in the financial industry, the third part

introduces the problems existing in the application

scenarios in the second part and the potential impact,

and the fourth part will exist in the artificial

intelligence industry in the financial industry in the

future the impact of risk and proposed solutions are

put forward. The fifth part is the literature summary

and outlook.

2 THE APPLICATION OF

ARTIFICIAL INTELLIGENCE

IN FINANCE

2.1 Application of Artificial

Intelligence in the Trading Market

Trading market is an important game, investors and

investment institutions in order to win the competitive

advantage, investors and employees to continuously

collect more information to assist decision making. In

the information age, advanced information

technology is regarded as an important enabler (Xiao,

2023). Artificial intelligence can through its

intelligence and sophistication, quickly can gain a lot

of information from the trading market for the huge

amounts of data processing and analysis to provide a

more reliable technical support. To help investors

make correct investment, reduce the investment risk,

and optimize the decision to obtain greater profits,

scope for artificial intelligence in the trading market.

Artificial intelligence technology in dealing with

financial problems, especially when dealing with the

nonlinear model, artificial neural network, expert

system, variational mode decomposition method and

hybrid intelligent systems in credit assessment,

portfolio management, financial forecasting and

planning field application accuracy significantly

superior to the traditional statistical methods (Liao,

2023). AI technology can efficiently process and

analyze massive data and provide rich and diverse

trading decisions through various machine learning

and deep learning models. At the same time, the

ability of AI market prediction, especially in time

series prediction and sentiment analysis, significantly

enhances the investor's decision-making ability.

Artificial intelligence, rather than investors

trading by oneself, in the speed of speed and

efficiency will be much more than people, artificial

intelligence algorithm can trade within millisecond

level, through the high speed advantage to capture

market price fluctuation in the tiny, high-frequency

trading. Artificial intelligence is not only on the speed

and efficiency of artificial traders but also has a great

advantage in data processing and learning. By

analyzing large amounts of data, artificial intelligence

can more deeply understand the dynamics and trends

of the trading market, to make decisions and execute

trades in the millisecond level, catch the tiny price

fluctuations in the market, and realize high-frequency

trading. The advantages of AI in the trading market

are also reflected in resource allocation and

investment strategies.

Artificial intelligence has provided strong help in

resource allocation and investment strategies, and

artificial intelligence can make resource allocation

more diverse. By proposing different types of

resource allocation, it can provide investors with

more choices, and artificial intelligence can also

choose the best investment allocation and choice for

investors. Ko and Lee(Ko, 2024) show that

ChatGPT's selection shows a statistically significant

improvement in the diversity index compared with

random selection, indicating that its asset selection

process is based on the asset allocation concept that

emphasizes diversification. In addition, the portfolio

performance analysis shows that the portfolio

constructed using ChatGPT's selection outperforms

the one constructed using random selection (Wang,

2023). Not only that, artificial intelligence has some

power for market prediction

Through in-depth analysis of massive structured

and unstructured data, artificial intelligence can help

banks to carry out comprehensive and accurate

market insights and provide sufficient basis for bank

decision-making (Dwivedi, 2023). Artificial

intelligence for the financial market forecast brings

new methods and tools. Despite some challenges, AI's

efficient data processing capabilities, automation and

real-time, and continuous learning and improvement

characteristics give it important application potential

in financial markets. As technology continues to

advance, AI will increasingly enhance the accuracy of

financial market forecasts, offering investors more

precise and effective decision-making support.

2.2 The Application of Artificial

Intelligence in Personalized

Financial Products

A personalized application refers to a specific use case

of artificial intelligence in finance, designed to meet

the unique needs of individual users. It applies the

technology and algorithm in the basic application to

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the financial situation to meet the personalized needs

of users. By combining cross-domain knowledge

graph, causal reasoning and deep learning

technologies, artificial intelligence empowers

machines with thinking logic and cognitive ability to

solve users 'personalized needs and automatically

adapt to users 'preferences, thereby improving users

'satisfaction. Artificial intelligence uses knowledge

graph to enrich information background, uses causal

reasoning to understand the causal relationship

between user needs, and analyzes user preferences

through deep learning

The research of personalized needs mainly

involves processing user's personalized needs by

artificial intelligence. With the development of

economy and society, financial users are no longer

limited to the general services provided by financial

institutions, and financial institutions need to apply

artificial intelligence more to solve the growing

personalized needs (Liao, 2023). By personalizing

and customizing financial products, artificial

intelligence can better serve customers and effectively

increase customer stickability. Through the deep

application of artificial intelligence technology, it can

realize intelligent and batch service to customers, so

as to realize the popularization of artificial

intelligence personalization, which will bring

significant innovation to the consumer finance

industry (Cheng, 2021). Tencent Cloud has launched

Tencent Financial intelligent marketing platform to

help customers customize financial products. The

Industrial and Commercial Bank of China has piloted

RPA technology to empower intelligent marketing.

RPA technology has become an important tool for

enterprise digital transformation because of its short

implementation cycle, fast implementation speed,

high processing efficiency and low cost. In addition

to customize their own financial products, the

application of artificial intelligence in the financial

industry also exists in the bank of personalized

customer service, in the front end of the financial

services, artificial intelligence technology can

provide the customers with the refinement,

humanization, specialization, intelligent financial

services. In China's financial services, artificial

intelligence technology to bank credit, financial

transactions, financial analysis, and other fields to

provide decision support. A background in financial

services and artificial intelligence technology can

provide technical support for the risk prevention,

control, and supervision of (Ma, 2018) . It effectively

reduces the human burden, improves the efficiency,

and can better serve the public. The personalized

application of artificial intelligence can provide

multiple programs for people who do not understand

financial knowledge through its accurate algorithm

and massive database as support. At the same time,

through the choice of the masses, artificial

intelligence can understand the preferences of the

operator's investment risk through the choice of the

operator's program so as to provide better programs

for investors.

Intelligent advisors can outperform humans in

investment allocation and trade execution, while also

assisting investors in overcoming emotional biases.

2.3 Application of Artificial

Intelligence in Risk Management

With the development of digital transformation in the

financial industry, the automated management of

financial risks by artificial intelligence has become

indispensable. Artificial intelligence has been applied

to financial risk management. Artificial intelligence

can also be combined with other technologies to

continuously learn and optimize the risk management

process and continuously improve the efficiency and

quality of the whole portfolio management platform

(Wang, 2023). Artificial intelligence can help

financial institutions identify and assess risks and

provide more accurate risk prediction and advice,

which is of practical significance for developing the

financial field (Zhao, 2024).

The application of artificial intelligence in risk

identification has significantly improved the ability of

financial institutions to identify and manage risks.

Artificial intelligence has played an important role in

fraud detection. Through machine learning

algorithms and big data analytics, AI can identify

abnormal transaction behavior and thus detect

potential fraudulent activity in a timely manner.

Artificial intelligence algorithm can process mass

transaction data, quickly locate abnormal trading

patterns, and through the real-time monitoring and

timely stop of suspicious transactions, reduce the

financial loss.

Second, the artificial intelligence are widely used

in the credit risk assessment. Traditional credit

scoring method usually depends on the limited

financial data, and artificial intelligence combines

various data sources, such as social media, consumer

behavior, geographical position, etc., to generate

more accurate credit score. Through deep learning

and other advanced machine learning technology,

artificial intelligence can analyze customer behavior

and credit record history, predict the future, and credit

risk for financial institutions to provide more reliable

risk assessment results. AI can help financial

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institutions reduce risks and losses and improve

customer service capabilities.

In market risk assessment, artificial intelligence

provides banks with a comprehensive and detailed

basis for credit risk assessment of corporate

customers by learning and analyzing the financial

reports and other public information of corporate

customers. Different from the traditional artificial

subjective judgment, the data-driven evaluation

model adopted by artificial intelligence is more

objective. On the level of automatic analysis of

financial indicators, artificial intelligence can

evaluate enterprise quality of accounts receivable,

cash flow status, etc. The key elements. Through the

depth of market data analysis and the forecast model,

artificial intelligence can identify market volatility

ahead of risk factors, to help investors make a more

informed decision. Sentiment analysis technology

uses natural language processing (NLP) to analyze

news and social media content, assess market

sentiment, and further improve the accuracy of

market risk identification.

AI can help financial institutions reduce risk and

losses and improve customer service capabilities. The

adoption of artificial intelligence technology in the

financial industry will rapidly increase, leading to

more efficient, secure, and intelligent services. (Wang,

2023).

3 ARTIFICIAL INTELLIGENCE

AND APPLICATIONS IN THE

FIELD OF FINANCIAL

CHALLENGES

3.1 The Potential Risk of Artificial

Intelligence in the Trading Market

and the Impact

The advancement of artificial intelligence enables

people to take advantage of the characteristics of

high-frequency trading. Still, the risk of market

volatility brought by high-frequency trading is a

significant problem for the application of artificial

intelligence. Ai-driven high-frequency trading

systems can execute a large number of trades in

milliseconds or even microseconds, which improves

market liquidity, but may also lead to extreme market

volatility. When market sentiment changes or

unexpected events occur, HFT systems may quickly

amplify price fluctuations, leading to market

instability. For example, the 2010 "flash crash" was

triggered by the automated operation of the HFT

system, in which the Dow Jones Industrial Average

fluctuated wildly in 13 minutes, in part due to the

rapid withdrawal of HFT when the market fluctuated

wildly, resulting in decreased liquidity and amplified

price movements. The incident prompted regulators

to take measures, including a "circuit breaker"

mechanism and increased monitoring of HFT, to

prevent similar market turmoil. This shows that while

HFT provides market liquidity and efficiency, it may

amplify market risk during wild swings.

Artificial intelligence is powerful and can quickly

use data. Behind it is vast amounts of data and

powerful algorithms as a support, but the power

behind there is a crisis of confidence, The application

of artificial intelligence technology in the financial

field has led to risks such as trust crisis and

insufficient supervision due to the lack of

transparency. (Ma, 2018) The algorithms applied by

artificial intelligence in the trading market are

relatively complex and difficult to understand. It is

difficult for people to know how artificial intelligence

processes data. Finally, it leads to the crisis of the

application of artificial intelligence in the financial

industry.

Artificial intelligence application in the market is

also facing the uncertainty risk of machine learning to

bring. Although the artificial intelligence system has

the powerful data processing ability, but these systems

cannot predict all the possible market events,

especially in extreme events. For example, when

encounter extreme market conditions, artificial

intelligence model may not be able to adapt to change,

resulting in prediction and decision-making errors. In

addition, the machine learning may occur in the

process of intermittent technical reasons, these

uncertainties may affect the stability of the system and

efficiency, so as to have a negative impact on financial

market, which caused an economic loss of customers.

3.2 The Artificial Intelligence in the

Risk of Financial Products

Personalized Applications

Artificial intelligence is based on large data,

inevitably in the process of its business on the

magnitude of the personal information collection,

processing and utilization, so to the customer's

privacy and personal information protection (Zhao,

2024). Financial institutions and the relevant

regulatory department attaches great importance to

and take corresponding measures to deal with.

Data privacy and security risks are the main

challenges in applying artificial intelligence in

personalizing financial products. Personalization

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services rely on collecting and analyzing a large

amount of users' personal data, including their

financial information, consumption habits, and social

activities. Although these data can help AI algorithms

better understand and predict user needs, it will pose

a serious threat to user privacy once the data is leaked

or misused. In recent years, multiple data breaches

have shown that even top financial institutions cannot

completely avoid data security issues. The disclosure

of personal privacy information can lead to serious

consequences such as poor social situation and even

loss of job opportunities for consumers. The illegal

trade and abuse of personal information seriously

infringes the rights of financial consumers and

destroys social relations. Because it has a complete

independent legal personality, people enjoy the legal

protection of the personal and property relationship of

rights independently. As an independent object of

rights, personal information is an important part of

individual rights and should be protected by law.

However, in the era of artificial intelligence, many

consumer financial institutions use "big data" and

"artificial intelligence" and other means to illegally

trade and abuse consumers' personal information,

resulting in consumers suffering from frequent

consumer financial phone calls and SMS harassment,

as well as malicious collection and violent collection

problems. This kind of behavior not only cause

significant damage to the financial consumers' social

life, and in the case of without the consent of the

parties to the trafficking and abuse of information, but

also a serious violation of financial consumer rights.

Therefore, financial institutions need to strengthen

data protection measures to ensure the security of data

in the process of collection, transmission, and storage,

as well as to prevent data from unauthorized access

and use.

Secondly, the algorithm of prejudice and

discrimination problem is an important risk in AI

personalized applications. ai systems rely on

historical data for training, which may contain bias

and discrimination. If left unchecked, AI systems can

inadvertently amplify these biases, leading to unfair

treatment of certain groups of users when accessing

financial services. For example, in a credit scoring

system, certain groups may be assessed as high risk

because of biases in historical data, making it difficult

to get loans. For example, in Nick Bostrum and Elieze

Udkowski's hypothetical experiment on whether a

machine learning algorithm should accept or reject a

home mortgage loan, it is easy to find "algorithmic

discrimination" when examining the neural network

decision making process and the results: the loan

application approval rate of blacks is significantly

lower than that of whites (Cheng, 2021). Once the

user is considered as a group, "top" or "bottom", they

have received financial information, advertising and

product recommendations, etc., will only with

machine learning algorithms for its default identity.

Machine learning algorithms complexity and

limitation of led to consumer finance in the scene

"algorithm discrimination", it will lead to the "digital"

bottom always stay on "digital" bottom. Qualification

of these potential customers may be because

"discrimination" to be fair to obtain consumer

financial services, may also be because of "price

discrimination" inability to properly enjoy consumer

financial services. To avoid this situation, financial

institutions need to be introduced in data processing

and algorithm design principles of fairness and

transparency, and regularly review and adjust the

model, to ensure that the AI system fair to all users.

3.3 On the Artificial Intelligence

Applied in Risk Management of

Risk

Although the application of artificial intelligence in

the field of risk management has greatly improved the

efficiency and effectiveness, it also brings a series of

significant potential risks and challenges. The quality

and bias of data are inevitable key issues in the risk

management of artificial intelligence. These systems

rely on a large amount of high-quality data for training

and performing tasks, and any bias or uncertainty in

the data may seriously affect the model's output.

Financial data of the complexity and diversity, as well

as universality and inconsistencies in the quality of

the data sources, data problems may lead to risk

assessment and prediction results appeared deviation,

leading to wrong decisions. Model transparency is

another challenge faced by AI in risk management.

Based on the deep learning model, in particular, they

"black box" feature makes the decision making

process is difficult to explain. In the financial field, it

is important to address the lack of transparency

because regulators and users around the world need to

understand the decision basis of the model. Suppose

ai system decision-making process is not transparent.

In that case, financial institutions will be difficult to

verify and explain the risk assessment results, may

lead to users and regulators mistrusted artificial

intelligence system, causing legal risk. Although the

artificial intelligence in the application of risk

management has great potential and advantages, its

potential risks and challenges are nots allowed to

ignore. Financial institutions need to ensure that the

application of AI technology in risk management is

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both efficient and safe through comprehensive risk

management measures and continuous technological

optimization to promote the sustainable development

of the financial industry.

4 SUGGESTIONS AND

SOLUTIONS

Artificial intelligence is bound to bring great progress

to the financial industry, but at the same time it will

bring potential risks, so the regulation of artificial

intelligence is essential, and regulation is an

important guarantee for financial innovation and

development. In order to promote the application of

artificial intelligence technology in the financial field

in an orderly way, this paper gives suggestions from

the following perspectives: First, formulate relevant

laws, regulations and regulatory rules then, focusing

on protecting users 'personal information and privacy

security; The second is to improve the accountability

mechanism for the application of artificial

intelligence technology in the financial field,

formulate and improve the detection method of

artificial intelligence technology, and remove

obstacles for the application and promotion of

artificial intelligence technology in the financial field.

The deep integration of artificial intelligence and the

financial industry will greatly improve the scope of

automation and the level of intelligence in the

financial industry, effectively improve production

levels, reduce costs, promote business innovation and

growth, generate economies of scale and scope, and

greatly improve the service quality and operational

efficiency of financial institutions. The application of

ai in the financial field is still in its early stage, and it

still faces great challenges. For the violation of

consumer privacy by artificial intelligence, consumer

financial institutions should fully respect the personal

financial information security rights of financial

consumers. The right to personal financial

information security is an important right of financial

consumers, and it is also the institutional basis for the

protection of consumer rights at the level of consumer

finance. Only when financial institutions obtain

formal authorization or consent of financial

consumers in advance, they can collect or provide

financial consumers' personal information to third

parties in compliance, to better protect the personal

financial information security right of financial

consumers (Xing, 2018). Algorithm discrimination

and black box problems are two major challenges in

the application of artificial intelligence in finance.

The problem of algorithm discrimination can be

solved from the source, the algorithm. Before

artificial intelligence is formally used in financial

products, data cleaning and preprocessing are carried

out to ensure the accuracy of data, while ensuring the

diversity of data, and avoiding the calculation bias

caused by data.

5 CONCLUSIONS

This paper conducts a systematic analysis of the

application and current development of artificial

intelligence in the financial sector using a literature

review approach. Firstly, this paper studies the main

application scenarios of artificial intelligence in the

financial field, including trading markets,

personalized financial products, and risk management.

These studies show that artificial intelligence

technology has significant advantages in improving

trading speed, optimizing resource allocation, and

improving market prediction ability and personalized

service. However, we also note that there are certain

risks and challenges in these applications of AI, such

as the risk of market volatility caused by high-

frequency trading, privacy protection issues in

personalized financial products, and algorithmic

discrimination and black box issues. On the basis of

summarizing the findings of the study, this paper

proposes several solutions to reduce the potential

risks of AI in financial applications. For example,

reducing algorithmic bias through data cleaning and

preprocessing, adopting explainable algorithms to

improve transparency, and developing stricter

policies and measures in terms of privacy protection.

These solutions not only help solve the current

problems, but also provide directions for future

research. Finally, the future development of artificial

intelligence in the financial field is prospected. With

the continuous progress of technology, the application

of artificial intelligence in the financial field will be

more extensive and in-depth. Future research can

further explore how to minimize risks while

maintaining technological advantages. The research

of this paper provides an important reference for this

field, and it is hoped that it can provide valuable

guidance for subsequent research and practice to

promote the sustainable development of ai in the

financial field. By combing and summarizing existing

research, this paper hopes to provide reference for

further research and promote the sustainable

development of artificial intelligence in finance.

Future research should focus on the combination of

technology and ethics to ensure that artificial

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intelligence technology can bring convenience. At the

same time, it does not damage users' interests and the

market's stability.

REFERENCES

Cheng X. J. 2021. Deep Involvement of artificial

intelligence in consumer finance: motivation, risk and

prevention and control. Journal of Shenzhen University

(Humanities and Social Sciences Edition), 38(03): 67-

76.

Dwivedi Y. K., Kshetri N., Hughes L., et al. 2023. “So What

If ChatGPT Wrote It?” Multidisciplinary Perspectives

on Opportunities, Challenges and Implications of

Generative Conversational AI for Research, Practice

and Policy. International Journal of Information

Management, 71: 102642.

Ko H., Lee J. Can ChatGPT Improve Investment Decision?

From a Portfolio Management Perspective. Available at

SSRN: https://ssrn.com/abstract=4390529.

Liao G. K., Li T. H. 2023. Research progress on the

application of artificial intelligence in financial field.

Economic Trends, (03): 141-158.

Ma S. L., Wei F. Y. 2018. The application of artificial

intelligence technology in the financial field: Main

Difficulties and Countermeasures. Southern Finance,

(03): 78-84.

Ou Yang R. H. 2024. Emergent artificial intelligence and

financial depth fusion: theoretical mechanism and

development path. Probe, (01): 57-65.

Wang S. Y., Li M. C., Yang K., et al. 2023. ChatGPT +

financial: eight noteworthy research directions and

problems. Journal of Management Review, 35(4): 3-11.

Wang X. H., Xiao B. 2017. Artificial Intelligence and its

application in the Financial Field. The Banker, (12).

Xing H. Q. 2018. Financial regulation reform in the era of

artificial intelligence. Exploration and Contention,(10):

21-23+141.

Zhao B. H. 2024. Regulation and legal regulation of

artificial intelligence application in the financial field.

Journal of Zhengzhou University (Philosophy and

Social Sciences Edition), 57(03): 51-57+142.

Xiao X. R., Li Y. W. 2023. How does artificial Intelligence

Challenge Investment Management? Discussion based

on Investment decision framework. Financial Market

Research,(3): 136-140.

Research on the Application of Artiﬁcial Intelligence in the Financial Field

313