Finance centralization—research on enterprise intelligence

Abstract

In the context of digital transformation, financial centralization has emerged as a crucial strategy for enhancing the efficiency and effectiveness of corporate financial management. This paper explores the role of Artificial Intelligence (AI) in financial centralization within Chinese enterprises, particularly under the framework of digital corporate financial management. By analyzing multiple case studies, the research demonstrates how AI applications streamline financial processes, mitigate risks, and improve decision-making accuracy. The study focuses on three key AI-driven applications: automated reconciliation, intelligent report generation, and big data-based risk management. Additionally, the research examines the unique challenges and opportunities that private and state-owned enterprises face during AI-driven financial transformation. Through in-depth analysis, the paper offers valuable insights into how AI is reshaping the financial landscape, providing both practical and theoretical contributions. These findings offer valuable implications for financial managers, policymakers, and researchers seeking to leverage AI in the evolving field of intelligent financial management.

Introduction

As the world’s second-largest economy, China’s corporate financial management practices are rapidly evolving and maturing. The integration of Artificial Intelligence (AI) into centralized finance is ushering in a multitude of intelligent applications tailored to various scenarios. This era, characterized by the advent of Cloud 2.0, has seen large enterprises expanding their operations to meet real-time business demands and enhancing security, while SMEs increasingly require comprehensive one-stop services and collaborative office capabilities. This paper aims to provide an overview of Enterprise Intelligence (EI) within digital corporate financial management in China, identify and discuss three AI applications based on centralized finance, and present a practical case study on AI centralization. Ultimately, this study analyzes the costs and benefits of finance centralization with AI, offering insights into how these technologies are reshaping the financial landscape.

To address these evolving needs, China has seen the integration of EI with a Cloud + AI + 5G + IoT (Internet of Things) framework. This multifaceted approach to corporate financial management encompasses a wide array of financial concepts essential for the development and maintenance of various business functions and aspects. Recognized as a pivotal element in enterprise value creation, corporate financial management necessitates an ongoing update of financial roles and the development of talent to meet the challenges posed by external regulation and technological advancements. Amidst the rapid advancement of technology and the pressing needs for digital transformation and value growth, financial managers are assuming increasingly diversified and comprehensive roles tasked with navigating challenges and uncertainties.

This paper is driven by three primary objectives: first objective is to provide an up-to-date overview of EI within the realm of digital corporate financial management in China; second objective is to identify and discuss three AI applications based on centralized finance within the context of corporate financial management, and third objective is to offer a practical case study on AI centralization in China. Ultimately, this study aims to analyze the costs and benefits of finance centralization with AI in the sphere of digital corporate financial management in China, offering insights into how these technologies are reshaping the financial landscape.

Literature review

The evolution of financial management literature has significantly transformed with the digital finance era and the incorporation of AI. This shift has introduced new paradigms for understanding and enhancing financial decision-making processes in enterprises, moving beyond traditional corporate financial management theories that have historically been the bedrock for interpreting organizational finance management. Notably, Froot et al. (1993) laid a foundational framework for analyzing corporate risk management policies, which has been pivotal in shaping subsequent research. Building on this foundation, Emery et al. (2004) provided further insights into corporate financial strategies, supported by additional contributions from Kent (2012), and Barclay et al. (2020), who underscored the rationality behind corporate managers’ financial decisions. Giambona et al. (2018) delved into risk management strategies and priorities, highlighting the significance of operational hedging across various risk domains. Gao (2022) marks a significant evolution by investigating the shift from financial accounting to management accounting in the big data era, underlining the functional differences and strategizing for a seamless transition to enhance management accounting’s role in enterprise development.

While corporate finance practices have been extensively explored, with particular emphasis on European contexts (Graham and Harvey, 2001; Brounen et al., 2004), research on corporate finance practices in Asia remains relatively scant. Baker et al. (2004) ventured into the realms of behavioral corporate finance and financial management systems, pointing to a geographic research gap that this paper seeks to address, particularly within the context of China’s rapidly evolving market. Recent research has broadened the scope of corporate finance to include applied aspects. For instance, Nezafat et al. (2021) explored the impact of short-selling activities on corporate investments, while Hu et al. (2020) investigated the nuanced relationship between control levels and cash holdings, uncovering a non-linear association. Lie and Liu (2018) provided insights into acquirers’ announcement returns in relation to cash holdings and stock payments. Sasaki (2015) examined anticipated liquidity shocks’ effects on corporate investment and cash holdings, demonstrating that such shocks are considered in managerial decision-making processes. Graham (2022) surveys CFOs to study contemporary corporate decision-making, revealing a trend towards conservative, market-timed decision rules and an emphasis on short-term reliable internal forecasts and stakeholder-oriented corporate objectives, which could refine academic models of corporate finance.

The digital age heralds a paradigm shift in financial management, extending academic inquiry into intelligent finance. Researchers like Pan et al. (2006), Pan (2011), and Guo and Polak (2023) have scrutinized the predictability of financial markets and the role of AI in finance. Yu et al. (2008) addressed the crucial early-warning problem and the indispensable role of AI in crisis forecasting. Xu et al. (2009) introduced innovations like context-awareness and rule engine technology to bolster traditional financial oversight mechanisms. Bahrammirzaee (2010) provided a comprehensive analysis of AI approaches, including neural networks, while Polak et al. (2020) sought to unify AI knowledge and terminology within finance. Kunduru (2023) delves into AI’s application in streamlining financial document processing, emphasizing AI’s transformative potential in financial operations. The insightful contributions of Guo and Polak (2021b) and Mohammad Nabil et al. (2023) illuminate the burgeoning development of AI in finance, particularly under the lens of the COVID-19 pandemic’s challenges.

EI, closely associated with business intelligence (BI), has captured increasing attention. Biere (2003) was seminal in introducing the concept of EI, offering an exhaustive perspective on BI within enterprises. Nofal and Yusof (2012) conducted a comprehensive review of BI and ERP system integration literature from 2002 to 2012, pinpointing critical integration strategies. Casturi and Sunderraman (2019) proposed the Enterprise Hybrid Business Intelligence Model (HBIM), a flexible reporting framework aligning with broader enterprise technological architectures. Zhou et al. (2023) underscores the pivotal role of BI and decision support systems in leveraging data for operational and strategic purposes, also discussing AI-related privacy concerns and evidencing AI’s capacity to refine financial management, cost reduction, and enhancement of accuracy and efficiency in corporate financial decision-making.

This study’s contribution is its holistic integration of digital finance, AI, and EI within financial management, fostering the modernization of traditional financial theories. By thoroughly exploring financial decision-making, global practices, and the application of intelligent technologies, this paper aims to equip scholars, practitioners, and policymakers with comprehensive insights and tools to adeptly navigate financial challenges in the digital era, enriching and advancing the financial management literature landscape.

Enterprise intelligence with digital corporate financial management

AI technology changes the structure of financial personnel—the proportion of personnel engaged in repetitive tasks decreases, and AI will replace some of them. Hence, corporate financial management also changes—from focusing on the financial statement to process, from standardizing systems to emphasizing discovery and decision support, monitoring and controlling internal management to coordinate strategic execution (Budiasih 2024; Alkaraan et al., 2023). In summary, corporate financial management pays more attention to external communication, business development, financing strategy, and digital decision-making. Digital corporate financial management provides synergy for the implementation of enterprise development goals. AI has brought corporate finance to the forefront, and enterprises’ demand for a professional financial manager has expanded. With the development of AI, financial managers will integrate data-driven AI applications into all aspects of corporate financial management, such as the automatic reconciliation of financial robots, intelligent reports, intelligent decision-making and intelligent decision-making and risk management based on big data and scenarios. As the specific integration point of business and finance, paying attention to the middle stage’s construction is significant (Elghaish et al., (2022)). The middle office supports corporate finance to conduct a more accurate analysis of customers, thereby improving the internal financial management of the enterprise. The financial manager could reconstruct the financial information system based on the concept of the central office, operate the data supply chain through the central office, improve financial responsiveness, and meet innovation needs.

However, the process of transitioning corporate financial management to a digital paradigm presents a set of complex challenges, particularly in the context of private and state-owned enterprises. While private enterprises often benefit from having a younger cadre of financial managers who possess a high degree of agility, enabling them to swiftly adapt to digital transformation, they may face a relative scarcity of digital resource support. In contrast, state-owned enterprises possess certain advantages in terms of resource allocation, yet they encounter their own unique set of hurdles (Lazzarini and Musacchio 2020; Solarino and Boyd 2020).

For private enterprises, the relatively limited access to digital resources can pose constraints on the extent and pace of their digital transformation initiatives (Hai et al., 2021; Margiono, 2020; Brunetti et al., 2020). While youthful financial managers are more open to embracing digital technologies and rapidly upskilling themselves, the shortage of resources may hinder their ability to fully leverage the potential benefits of digital corporate financial management. Furthermore, the rapid pace of technological evolution can exert high pressure on private enterprises to constantly adapt, which, while a source of agility, can also be a source of strain.

On the other hand, state-owned enterprises, despite their access to more abundant resources, face their own set of challenges (Lin et al., 2020; Yun et al., 2024). Often characterized by a more extensive workforce and longer transformation cycles, state-owned enterprises may experience difficulties in fostering a culture of digital innovation. Moreover, the higher-pressure environment driven by technology substitution may be demanding for financial managers in state-owned enterprises, especially when the transition necessitates substantial changes in established practices.

It is important to note that the challenges and opportunities associated with digital transformation in corporate financial management are multifaceted and nuanced, extending beyond the private versus state-owned enterprise distinction (Apriliyanti et al., 2023; Liao et al., 2023). For a more comprehensive analysis, it is essential to delve deeper into the specific obstacles, such as resource allocation, skill development, and technology adoption, and to explore the potential synergies that may arise from bridging these gaps. By addressing these intricacies, a more robust understanding of digital corporate financial management can be developed, facilitating better-informed strategies for both private and state-owned enterprises.

Centralized digital finance

In the digital economy, financial centralization stands out as a crucial aspect garnering increasing attention in the financial sector. Enterprise managers need to reconsider past digital strategies and enhance their operational models. Grounded in data, resources, and capabilities, they should actively respond to the swift development of the external environment and information technology, playing a role in transformation planning, strategic decision-making, business support, risk management, data, and technology. This proactive engagement aims to drive the digital transformation of enterprises.

Consequently, managers must redefine a new set of processes and redesign financial activities and organizational structures around the entire spectrum of technologies. The introduction of automation alters organizational structures, leading to job changes and layoffs. Technology can provide data from intelligent data lakes, hastening decision-making, improving process efficiency, and aiding managers in demonstrating the value of the finance department.

Against the backdrop of centralized digital finance, enterprise managers need to contemplate a reimagining of the organizational structure and operational mode of financial activities. Anchored in data, resources, and capabilities, they should actively respond to the rapid developments in the external environment and information technology, playing a pivotal role in transformation planning, strategic decision-making, business support, risk management, data, and technology. This involvement is aimed at propelling the digital transformation of enterprises.

Hence, managers must formulate a new set of processes adapted to the digital economy and redesign financial activities and the entire organizational structure around new technologies. The introduction of automation induces changes in the fundamental structure of the organization, resulting in job restructuring and layoffs. The application of technology can leverage intelligent data lakes to provide data, expedite decision-making processes, enhance operational efficiency, and assist managers in validating the value of the finance department. This transformation necessitates managers to delve deeply into contemplation and adaptation to the new digital environment, ensuring that enterprises remain competitive amid the tide of centralized digital finance.

Centralized network finance

In the platform economy, enterprises increasingly prioritize the construction of ecosystems and the management of value chains. Commercial competition has shifted towards competition in supply chains, industrial chain platforms, and even the entire ecosystem. The application of platform-based thinking to the financial sector and organizational management has given rise to a “financial center” serving as a bridge between business activities and financial management. This center constitutes a digital financial and intelligent treasury open platform.

The ecosystem is interconnected through various technologies such as AI applications, IoT, RPA, blockchain, collaborative networks, and other technologies, employing open models such as API/SDK/ISV. This integration facilitates intelligent forecasting, automated reporting and transactions, and forward-looking ecological partner management. Its overarching goal is to assist companies in reducing costs, managing risks, discovering new sources of value, and transforming the financial department into a new value creation center and an empowerment platform for the enterprise.

Within this paradigm, the financial center transcends its traditional role as a mere hub for financial management. Instead, it evolves into an intelligent platform that leverages emerging technologies like AI and the Internet of Things, along with open models and collaborative networks, to provide comprehensive support for enterprises. This platform not only focuses on the recording and processing of financial data but places a greater emphasis on ecosystem construction and management, enabling enterprises to maintain agility and innovation in a highly competitive business environment.

By harnessing the power of AI, IoT, robotic process automation, blockchain, and other technologies, the financial center achieves intelligent forecasting, automated reporting and transactions, and deep integration with the enterprise’s ecosystem. Simultaneously, through open models like API/SDK/ISV, the financial center offers enterprises the ability to rapidly integrate with external partners and service providers, fostering the collaborative development of the ecosystem.

The advantages of this intelligent platform lie in its provision of efficient financial management tools for enterprises, coupled with forward-looking ecological partner management that enhances collaboration with external partners. This collaboration not only helps reduce operational costs but also facilitates the joint exploration of new sources of value, propelling enterprises to new heights.

Therefore, within the framework of centralized network finance, the financial center serves not only as the core of financial management but also as the engine driving innovation and value creation for the enterprise. By integrating various technologies and open collaboration models, the financial center not only facilitates the transformation of enterprises but also provides robust support for the sustainable development of the entire ecosystem.

Centralized agile finance

Agile finance relies on driven planning, real-time data, and the availability of new technologies. Agile financial functions enable the enterprise to use new technology to obtain information without waiting for the financial department to communicate. In agile finance, applying multiple technologies automates the financial process, reduces manual and repetitive tasks, and releases highly skilled talents to focus on high-value tasks, thereby reducing costs and improving service delivery quality.

Here is an example analysis of the agile finance transformation in China’s retail banking. The development of the retail banking business faces a “new” trend, affecting the business operation model. As China’s economy has wholly entered the “new normal” (Han et al., 2017), the banking industry has bid farewell to more than 10 years of rapid growth. Banks are transforming to find new business growth points and achieve intensive development. Agile transformation can quickly respond to and solve the problems of banking business development. The agile transition of the retail banking industry primarily takes place because front office marketers will rapidly learn about consumer desires, and consumers can adapt effectively to their needs by offering customers competent financial services, theoretical frameworks and processes. The significant link between the front office and the back office, the most important thing is to build a retail business middle office (see Fig. 1) and create the core productivity of retail data: build a useful control model through the digital capabilities of the head office brain, empower the retail business marketing team, and improve customer experience, and finally realize the transformation from “data production materials” to “data productivity”, and promote the bank’s agile transformation.

Fig. 1 [Images not available. See PDF.]

Retail agile business middle office presents the significant link between the front office and the back office, the most important thing is to build a retail business middle office and create the core productivity of retail data.

Centralized intelligent finance

Centralized intelligent finance represents a transformative paradigm within the financial landscape, leveraging a synergistic integration of big data, cloud computing, mobile Internet, AI, and blockchain technologies. This novel approach aims to propel traditional finance into an era of enhanced data sharing, automated information transmission, and the intelligentization of financial processes, all culminating in a machine learning-driven transformation. The primary goal is to unlock the maximum value embedded within financial data.

Internally, the core value of centralized intelligent finance manifests in fortifying intelligence within financial management. Through the application of cutting-edge technologies, this paradigm facilitates the intelligentization of key financial functions, such as accounting, financial analysis, forecasting, and risk management. The consequential reduction in manual operations diminishes the likelihood of errors and misjudgments, thereby effectively liberating human resources to enhance overall operational efficiency and reliability within enterprises.

Externally, centralized intelligent finance engenders two distinct values. Firstly, it fosters financial intelligence to catalyze business development and fortify the grid management of business information. Secondly, it entails the collection of customer transaction characteristics, risk preferences, and channel preferences, facilitated by the digital capabilities of enterprises. This collected information is then harnessed to construct a systematic model. This model, in turn, contributes to heightened customer satisfaction, facilitates resource sharing, and paves the way for secondary development initiatives.

In essence, centralized intelligent finance emerges as a transformative force, not only reshaping the internal dynamics of financial management but also contributing significantly to the external realms of business development and customer-centric strategies. Through the judicious application of innovative technologies, it navigates the financial landscape towards a more intelligent, automated, and value-maximizing future.

Digital corporate financial management with AI applications

With the rapid development of new technology, AI has brought corporate finance to the forefront, and enterprises’ demand for a professional financial manager has expanded. The new technologies include big data, cloud, OCR (Optical Character Recognition), RPA (Robotic Process Automation), and ERP (Enterprise Resource Planning).

OCR (optical character recognition)

OCR refers to analyzing and recognizing image files of text data to obtain text and layout information (Fateh et al., 2023). According to the recognition application scenario, OCR is divided into dedicated OCR to identify specific scenarios (see Table 1) and universal OCR to identify multiple scenarios.

Table 1. Example of dedicated OCR to identify specific application scenarios.

Application Scenarios	Advantages
ID authentication	• Multi-category card identification—text recognition of various cards, such as ID card, passport, driver’s license.
	• High recognition accuracy—advanced deep learning algorithms, optimized business scenarios, and high text recognition accuracy.
	• ID text recognition in scenes includes complex backgrounds, distortions, and tilt.
	• Save manual entry, improve efficiency, reduce the cost of user real-name authentication, accurate, fast and convenient.
Financial reimbursement review	• Multi-category bill recognition—text recognition under the same type, different types of invoices, cards and any combination of assorted stickers.
	• High recognition accuracy—with advanced deep learning algorithms to optimize business scenarios and high text recognition accuracy.
	• Complex background—bill recognition in scenarios, such as stamping, tilting.
	• Effectively save manual entry costs and improve efficiency.
Insurance	• High recognition accuracy—with advanced deep learning algorithms to optimize business scenarios and high text recognition accuracy.
	• Complex background—document text recognition in scenarios like stamping and tilting.
	• To apply for insurance reimbursement, required paper materials, such as certificates, reimbursement forms, and medical documents. Through the OCR, the automatic input and verification of information can improve efficiency.
Contract entry and review	Contract integration identification—automatically recognize the contract text and detect the signature and stamping area, complete the automatic contract review

Although the OCR has many advantages in application scenarios with great potential, it also has shortcomings, especially in multiple scenarios. For example, the image background is rich, with low brightness, low contrast, uneven lighting, perspective distortion, and incomplete occlusion; the layout of the text may distort, or text with a variety of fonts, font size, weight and color.

RPA (robotic process automation)

The human body needs the coordination and cooperation of multiple systems, such as the motor, nervous, digestive, and immune systems, to facilitate the regular functioning of different complex human body life activities. People need the brain to think, the eyes to see, the ears to hear, the mouth to speak, the legs to walk, the arms to hold things, and the stomach to digest food. Furthermore, all these systems are closely connected with nerves and blood vessels to ensure communication. Automation and AI are bound to reshape financial function (see Table 2). The perfect combination of RPA + AI triggers significant changes in finance and finance, reshaping the financial function and future financial model.

Table 2. Example of RPA to application scenarios.

Application	Scenario	Advantages
Data entry and data extraction	RPA can automate data entry tasks by extracting data from documents, forms, and websites and populating it into databases or software systems. This is commonly used in industries like finance and healthcare for processing invoices, claims, and customer data.	Improved data accuracy, reduced manual data entry errors, increased efficiency, and faster data processing.
Invoice processing	In the finance and accounting departments, RPA can automate the processing of invoices. It can extract data from invoices, validate it, and initiate payment processing, reducing the manual effort required.	Faster invoice processing, reduced human errors, cost savings, and improved compliance with payment timelines.
Customer service and support	RPA can assist in customer service by handing routine inquirles, processing orders, and managing support tickets. Chatbots, powered by RPA, can provide 24/7 customer support.	24/7 customer support, faster response times, reduced operational costs, and improved customer satisfaction.
HR and payroll processing	RPA can streamline HR tasks by automating employee onboarding, payroll processing, and benefits administration. It can also help in resume screening and candidate shortlisting during the recruitment process.	Streamlined HR processes, reduced administrative workload, improved accuracy in payroll, and faster recruitment processes.
Inventory management	RPA can automate inventory management tasks such as order tracking, stock level monitoring, and demand forecasting, which are critical for retail and supply chain management.	Enhanced supply chain visibility, optimized inventory levels, improved demand forecasting, and reduced carrying costs.
Report generation	RPA can generate reports by collecting, analyzing, and presenting data from various sources. This is useful in financial reporting, business analytics, and compliance reporting.	Automated report generation, reduced manual data analysis, faster decision-making, and increased data accuracy.
Claims processing in insurance	Insurance companies use RPA to handle claims processing efficiently. RPA bots can validate claims, calculate payouts, and update policyholder information.	Faster claims processing, reduced fraud, improved customer satisfaction, and cost savings.
Data migration	When organizations need to migrate data between systems, RPA can automate the data transfer process, ensuring data accuracy and consistency.	Error-free data migration, reduced data loss, and faster system transitions.
Email and communication handling	RPA bots can sort, categorize, and respond to emails, making email management more efficient. They can also automate appointment scheduling and follow-up communications.	Efficient email management, reduced response times, and improved communication with customers.
Quality control and testing	RPA can be used to test software applications and perform quality control checks. It can help identify and report issues and inconsistencies in software systems.	Efficient software testing, error detection, faster issue resolution, and improved software quality.
Compliance monitoring	RPA can continuously monitor regulatory changes and ensure that organizations comply with legal requirements by automating compliance checks and reporting.	Continuous compliance monitoring, reduced risk of non-compliance, and automated reporting.
Data backups and recovery	RPA can automate data backup processes and assist in data recovery in the event of system failures or data loss.	Timely data backups, faster recovery in case of data loss, and improved data security.
Healthcare claims processing	In the healthcare industry, RPA can process insurance claims, verify patient information, and manage billing and payments.	Faster processing of healthcare claims, reduced administrative workload, and improved accuracy in billing and claims management.
Banking and financial services	RPA can be used for various banking functions, including customer onboarding, transaction processing, fraud detection, and account reconciliation.	Streamlined banking processes, improved transaction accuracy, reduced fraud, and enhanced customer service.
Supply chain management	RPA can optimize supply chain processes, such as order fulfillment, inventory tracking, and demand forecasting.	Optimized supply chain operations, reduced inventory costs, improved demand forecasting, and enhanced supply chain visibility.

We liken graphics processing to the eye. IVSR (Interactive Voice and Speech Recognition) to the ear. The chatbot is equivalent to a mouth. Data is equivalent to blood. API (Application Programming Interfaces) is equivalent to nerves. Blockchain is equivalent to a social network. RPA is equivalent to hand (Ray et al., 2023). If the two engines of RPA and AI are connected, then RPA will become IPA (Intelligent Process Automation), which makes robot decisions based on experience rather than binary rules. For example, suppose the RPA connect with the chatbot/IVSR. In that case, the robot will be able to participate in the interaction actively and collect data, and ultimately expand the scope of its knowledge and delivery capabilities.

ERP (enterprise resource planning)

In the past, large enterprises generally used ERP systems for internal management to improve work efficiency. The previous ERP systems were more like financial management systems. With the development of enterprise informatization, currently, the functions of ERP systems cover different departments. In all aspects of production and operation, more and more small and medium-sized enterprises use ERP systems to improve production efficiency and reduce operating costs (see Table 3).

Table 3. Example of ERP to application scenarios.

Application	Scenario	Advantages
Financial management	ERP systems are used for financial management, including accounting, financial reporting, cost control, cash management, and tax management.	Provide comprehensive financial data analysis and monitoring, enhance the accuracy and timeliness of financial decisions.
Supply chain management	ERP is used for supply chain management, including inventory control, order processing, procurement management, and supplier relationship management.	Optimize inventory, reduce costs, improve delivery speed, and enhance supply chain visibility.
Production planning and control	ERP supports production planning and control, including production orders, process management, and resource allocation.	Improve production efficiency, reduce production delays, and optimize the utilization of production resources.
Sales and customer relationship management	ERP systems are used for sales management, customer relationship management (CRM), order processing, and sales analysis.	Enhance collaboration within the sales team, provide customer insights, and automate sales processes.
Human resources management	ERP supports human resources management, including employee information, payroll management, recruitment, performance evaluation, and training.	Improve employee management efficiency, optimize payroll processing, and support employee development.
Project management	ERP is used for project management, including project planning, resource allocation, budget management, and project execution.	Provide project visibility, control project costs, and optimize project resources.
Quality management	ERP systems support quality management, quality control, and quality audits.	Improve product quality, reduce defects, and enhance quality control.
Reporting and analysis	ERP systems are used for data reporting and analysis, including business intelligence and decision support.	Provide real-time data analysis, support decision-making, and optimize business performance.
Purchasing management	ERP systems support the purchasing process, supplier management, purchase contracts, and purchase analysis.	Reduce purchasing costs, improve supplier relationships, and optimize the purchasing process.
Inventory management	ERP systems are used for inventory control, inventory tracking, and inventory optimization.	Reduce inventory costs, decrease excess inventory, and improve inventory turnover.

In financial management, many enterprises have realized computerized management. The information level of financial management has an individual foundation and practice in each enterprise. Nevertheless, to better integrate with the information management system of other businesses, the ERP financial system can better realize the collection and sorting of integrated and functional financial data, adopting the rolling cost calculation algorithm (Guo, 2024). The physical account and the capital account are generated at the same time. The seamless management of logistics and capital flow dramatically reduces the workload of financial managers, improves the timeliness and accuracy of financial data processing, and provides financial management with pre-budget, in-process control, and post-analysis. With information materials, an intuitive financial analysis report can finally form automatically by the system so that decision-makers can understand the correct and accurate business operation status at any time.

The role of large language models in financial centralization

With the rapid advancement of AI technologies, Large Language Models (LLMs), such as GPT-3 and GPT-4, have revolutionized numerous fields, including finance. LLMs, known for their capacity to process and generate human-like text, are transforming financial centralization by automating key processes, supporting decision-making, and enhancing customer service. In the context of financial centralization, LLMs offer substantial benefits, particularly in financial report automation, decision support, and customer service.

Financial report automation

One of the most time-consuming tasks in financial management is the preparation of detailed financial reports. Traditionally, this process requires extensive manual work, including data compilation, interpretation, and report writing. However, with the integration of LLMs, financial reporting can be significantly automated. LLMs use Natural Language Generation (NLG) to generate comprehensive financial reports based on raw data inputs, enabling organizations to produce accurate and consistent reports with minimal human intervention.

For instance, LLMs can automatically summarize financial statements, create variance analyses, and generate audit reports by interpreting large volumes of structured and unstructured data. This not only reduces the time and labor costs involved in report generation but also minimizes errors commonly associated with manual data handling. By automating the reporting process, financial managers can focus more on strategic decision-making rather than routine tasks, enhancing overall efficiency within centralized financial systems.

Decision support

Financial decision-making often involves analyzing vast amounts of data to predict trends, assess risks, and determine optimal investment strategies. LLMs enhance decision support systems by processing complex financial data, identifying patterns, and providing predictive insights. Unlike traditional decision support tools, LLMs can interpret both quantitative data (e.g., financial metrics) and qualitative information (e.g., market news, analyst reports) to offer a holistic view of financial situations.

In centralized finance, LLMs assist in generating real-time insights, enabling managers to make informed decisions swiftly. For example, they can be used to generate financial forecasts, evaluate market conditions, and assess the potential impacts of regulatory changes. This capacity to synthesize vast datasets into actionable intelligence allows organizations to stay ahead in a rapidly evolving financial landscape, improving decision-making precision and agility.

Customer service enhancement

Customer service in the financial sector has traditionally relied on human agents, but LLMs are increasingly being deployed to enhance this area. By using Natural Language Processing, LLMs can interact with customers, answer queries, provide personalized financial advice, and even manage complaints in real-time, mimicking human-like conversation.

In financial centralization, LLMs can streamline customer support operations by handling routine inquiries, such as balance checks, transaction histories, and loan application statuses. Additionally, LLMs can assist with more complex tasks, such as providing tailored investment advice based on the customer’s financial goals and risk tolerance. This not only improves customer satisfaction but also reduces the workload on human agents, allowing them to focus on more complex cases requiring personal attention.

Challenges and opportunities

While LLMs present numerous advantages, there are also challenges that organizations must address when integrating these technologies into centralized financial systems. Issues such as data privacy, model accuracy, and interpretability need to be managed carefully to avoid potential pitfalls. Additionally, training LLMs on domain-specific financial data is critical for ensuring they provide accurate and relevant insights.

However, the opportunities presented by LLMs far outweigh these challenges. As LLMs continue to evolve, their capacity to handle even more sophisticated financial tasks will increase, potentially leading to the development of fully autonomous financial systems. Organizations that effectively implement LLMs will be better positioned to optimize their financial operations, reduce costs, and enhance decision-making capabilities, giving them a competitive edge in the digital economy.

By integrating LLMs into financial centralization processes, enterprises can significantly automate financial reporting, enhance decision support, and improve customer service. These models offer unique advantages in handling large datasets, interpreting both structured and unstructured data, and generating human-like responses. While challenges remain, the continued development and application of LLMs in finance will further streamline operations and help organizations navigate complex financial landscapes. The addition of LLMs to financial centralization is not only a technological advancement but a strategic necessity for organizations aiming to remain competitive in a rapidly evolving digital world.

Case studies with application

Alibaba’s intelligent finance: a paradigm of digital transformation

Alibaba Group faces significant challenges in financial operations due to diverse accounting rules across its ventures and the complexities of mergers and acquisitions. To address these, Alibaba has undertaken a digital transformation of its financial management, leveraging big data, cloud computing, and AI. This transformation includes financial sharing services that integrate finance with business operations, setting a benchmark for digital, interconnected, and intelligent financial management. Specifically, Alibaba employs AI-driven automation for financial reporting, risk management, and resource allocation, resulting in improved accuracy and efficiency. Data for this case study was sourced from Alibaba’s internal financial reports and interviews with key financial managers.

Huawei Cloud with ROMA connect and Yonyou NC financial sharing: tailored solutions for the Chinese market

Huawei Cloud, in collaboration with ROMA Connect and Yonyou NC, offers a comprehensive framework for corporate financial transformation. This system supports high concurrency and integrates with electronic invoicing, tax management, and e-commerce platforms. The financial shared services provided excel in business analysis, financial planning, and transaction processing, enabling detailed investment and cost analysis and strategic financial planning for CEOs and CFOs. This case study is based on Huawei’s project reports and interviews with the implementation team, highlighting the system’s impact on financial performance and decision-making processes.

Critical overview

Technology is the agent of social change. New technologies will undoubtedly make people’s work and life more efficient, improve the integration and utilization of social resources, and inject vitality into the development of the digital economy. However, it also inevitably creates costs and challenges in network security. In this sense, it must take action on problems, and technical advancement should be seen as a solution. International cooperation must be improved or social values more suited to the context of emerging technology must be established increasingly. A whole more reliable world of interactions must be created. At the same time, new technologies face many challenges in the industry in terms of technology applications. The combination of technology and industry knowledge fundamentally changes the way companies operate. The industrial ecology under the background of digitalization has entered the track of reshaping, and various industries are staggered and reconstructed to form a brand-new ecosystem. Companies that go on their own will be eliminated. Only by actively participating in the ecosystem can they have the opportunity to survive and live better. With the integration of industries and the upgrading of consumer demand, companies must become open and flexible and establish long-term advantages for the future by choosing a sustainable ecosystem.

Conclusion

This study provides a comprehensive analysis of the role of AI in transforming financial centralization within Chinese enterprises, specifically in the context of digital corporate financial management. The integration of AI into financial processes offers significant benefits, including enhanced operational efficiency, improved risk management, and more informed decision-making. Through case studies, we have identified three critical AI applications—automated reconciliation, intelligent reporting, and big data-driven risk management—that are reshaping corporate financial practices.

Moreover, the findings highlight the differing challenges faced by private and state-owned enterprises during the digital transformation. While private enterprises benefit from their agility, they often face limitations in digital resources. On the other hand, state-owned enterprises have abundant resources but may struggle with longer transformation cycles and cultural resistance to technological change. These challenges underscore the need for tailored strategies to fully leverage AI’s potential in financial centralization.

This research contributes to the growing literature on intelligent finance, offering both practical insights and theoretical advancements. The adoption of AI in financial centralization is not only a technological shift but also a strategic imperative for organizations aiming to enhance their competitiveness in a rapidly evolving digital economy. Future research should focus on the long-term impact of AI-driven financial systems and explore how emerging technologies, such as LLMs, can further enhance financial management.

Acknowledgements

This work is supported by the the Fundamental Research Funds for the Central Universities (No.3214002411B3), the National Natural Science Foundation of China (No.72173018) and National Key Research and Development Program of China (2021QY2100). This work is supported by the Fund for Open Access Publishing at the Mendel University in Brno, Czech Republic. All the support is gratefully acknowledged.

Author contributions

All authors contributed to the paper conception, methodology, and formal analysis and investigation. The first draft of the manuscript was written by HG, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Conceptualization: HG and PP; Methodology: HG and PP; Writing—Original Draft: HG; Writing—Review & Editing: PP; Formal analysis and investigation: HG and PP.

Competing interests

Petr Polak was a member of the Editorial Board of this journal at the time of acceptance for publication. The manuscript was assessed in line with the journal’s standard editorial processes, including its policy on competing interests.

Ethical approval

This article does not contain any studies with human participants performed by any of the authors.

Informed consent

This article does not contain any studies with human participants performed by any of the authors.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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