1. Background to the Study

ERP systems have developed much earlier, and SAP’s S/4 HANA FICO module is well-known for its efficient financial as well as controlling function. Businesses also use AI to improve these systems, especially for internal control to facilitate the achievement of their business objectives. They are important in preventing the firm from violating the set legal and business regulations, reducing and controlling risks involving finances and boosting operation efficiency. As the rules and regulations of the international business environment become increasingly complex, the conventional approach of manual check and balance fails to meet the complexity that the compliance function faces today, therefore enabling the integration of AI solutions and automation in compliance functions. This means that using AI jointly with S/4 HANA FICO makes it possible to use real-time monitoring, perform analytic prediction, and identify anomalies. This paper explores how AI can revolutionise internal control within S/4 HANA FICO and automatically conform to regulatory measures.

1. Overview

In further detail, the research focuses on three aspects of applying AI for automating internal controls in S/4 HANA FICO offering a reference model of real-time compliance and risk management. The study then discusses the traditional internal control problems and how AI solutions like; machine learning and natural language processing can help to fix them. The framework suggested for improving compliance checks, fraud detection and regulatory reporting will be based on integrating AI algorithms with S/4 HANA FICO.

1. Problem Statement

Many companies continue to fight with problems like compliance issues based on limitations of traditional internal controls, including poor recognition of changes in regulatory environments and lack of ability to identify complex fraud. S/4 HANA FICO has strong financial management though internal control can gain from the implementation of AI.

1. Objectives

The main goals of this study are: 1. To identify the limitation of traditional internal controls in the S/4 HANA FICO model and explore areas where AI can provide benefits. 2. To construct effective procedures that would allow the AI technologies integration into S/4 HANA FICO to monitor and control in real time. 3. To evaluate how such related concepts in artificial intelligence as predictive analytics as well as anomaly detection can help mitigate compliance risks and identify fraud. 4. To evaluate the effectiveness of AI-based internal controls in improving regulatory compliance, optimizing operations, and managing financial risk.

1. Scope and Significance

The scope of this method presents AI incorporated in internal controls of S/4 HANA FICO, particularly on compliance checks, fraud prevention & detection, and real-time financial analysis. This includes settings, application, and assessment of the use of AI to improve control effectiveness with limited reliance on the human factor. On the other side, the significance is that it helps the organisation in saving for possible legal and financial constraints and generating adequate results from financially efficient operations.

II. Literature Review

1. Limitations of traditional internal controls in S/4 HANA FICO

Most of the internal controls implemented in ERP systems like S/4 HANA FICO have prescribed rules that are set in advance to monitor the transactions. Although these controls reflect well in bringing a static environment all controls have a weakness when it comes to responding to dynamic issues such as changes in regulation or elaborate frauds. The author points out that these controls are rigid, and fixed in nature, and cannot support large amounts of financial data hence the inefficiencies [1]. Moreover, manual interventions also involve more risk of the human factor and time delays in detecting and preventing compliance or non-compliance [1]. These gaps can be closed through the application of AI technologies because of the reliability of automated data analysis and monitoring, which eliminate the drawbacks of conventional controls.

1. Integration of AI technologies for real-time monitoring

    

   Figure 1: Available communication technologies

   [2]

   Implementing AI in S/4 HANA FICO requires creating approaches for the effective interaction of AI applications with conventional finance systems. The author states that opines that through Integration of AI such as the use of machine learning into the ERP modules can enable the real-time analytical processing of transactional data without intervention [2]. This integration should entail strong communities to safeguard data preprocessing, model training, and validation to reduce inaccurate outcomes. Further, real-time monitoring essentialities require a robust IT support system, and integrating line functions needs to harmonise AI applications with organisational goals [2]. Automated systems for which it does not only improve financial controls but also better compliance management.

   1. The role of predictive analysis and anomaly detection for mitigating risk

    

   Figure 2: System Architecture for Density and Distance-based Anomaly Detection

   [3]

   Predictive analysis and anomaly detection are very crucial in compliance risk assessment and fraud detection. Risk analytics is the use of historical and live data to create scenarios that enable the organisation to prevent risks from occurring. On the other hand, anomalies unusual patterns or fluctuations in financial reviews are detected by anomaly detection algorithms that give clues of fraud or regulatory violation [3]. This method shows this in its research on unsupervised learning models in S/4 HANA FICO These can identify concealed structures and alert organisations more quickly to suspicious occurrences. These technologies decrease dependence on the reactive control system and help to apply proactive approaches in managing compliance and financial risks [3].

   1. Measuring the effectiveness of AI-integrated internal controls

   AI audit can therefore reveal levels of effectiveness of AI internal controls through indicators like; compliance, operations, and risks. The author also believes that the advanced adoption of AI provides minimal formats of audit trails and reporting hence enhancing the extent of compliance with regulation [4]. Additionally, the optimisation of internal controls that use artificial intelligence decreases the time needed for control activities and increases data quality as well as minimises operations’ constraints. Research also shows that persistent evaluation of the AI models and making appropriate model changes that fit regulatory and organisational needs are critical [4].

   III. Methodology

   1. Research Design

   The research design of this study adopts an explanatory approach with a secondary data analysis approach for explaining the application of AI-based internal controls in SAP S/4HANA FICO for automating compliance. The approach entails the development of the relevant theories hence a collection of the relevant articles, reports, and case studies to establish the best perception of current thoughts on the application of AI in financial and controlling modules. Areas related to understanding the role of AI in compliance, mitigation of fraud and prevention of errors [2]. The analysis is done to build an understanding of the research topic and show the best practices and major issues of AI-tuned controls in S/4HANA FICO to perform governance.

   1. Data Collection

   In case of collecting qualitative data, secondary research in the form of refereed academic journals, cases, papers, white papers, and standard industry reports highlighting the usage of AI in SAP S/4HANA FICO further curtain be carried out. Some of the sources of information used include research articles, and professional opinions from giants in the field like SAP, Deloitte and PwC among others. To ensure that information is focused only on the relevant themes, items such as compliance automation, risk management and internal controls will be extracted from the tweets [8]. In the case of quantitative examinations of internal controls by utilizing AI in S/4 HANA FICO, quantitative analysis includes the use of various figures of financial transactions, number and percentage of compliance, and risk ratings. Graphs and other statistical instruments were used to compare the effectiveness of AI-based controls with the applications of conventional controls [8]. The sources of data are hardcopy documents, financial records, system logs and reports and compliance reports to compare improvements in efficiency gains, reduced risk, and better compliance.

   1. Case Studies/Examples

   Case Study 1: Fujitsu’s Migration of SAP S/4HANA Finance

   The information and communication technology company, Fujitsu adopted SAP S/4HANA Finance to improve its financial management activities. This migration was to ensure that data flowed in the right way throughout the process in a very efficient manner [5]. But the likes of standardised process models, and obsolete user interfaces were faced. To meet these concerns Fujitsu modified the interfaces of various applications and offered extensive training to its employees to better the outlook that is seen by users and make the working process more efficient. 

   Case Study 2: Integration of AI with SAP S/4HANA at Deutsche Bank.

   AI solutions by Deutsche Bank combined working with SAP S/4HANA and affected its financial data managing and risky assessment. This integration allowed the bank to automate the more complicated parametric allocations that include depreciation and tax configurations [7]. Further, the implementation of Artificial Intelligence and the use of predictive analytics led to the improvement of decisions made in advance, insight into possible trends observed in the financial market, as well as possible risks that can be paid attention to by Deutsche Bank [7]. The application of AI for the SAP S/4HANA showed new possibilities for the optimisation of bank processes and enhanced the competitive position in the financial market. 

   Case Study 3: SAP Business Suite Implementation by Accenture

   SAP S/4HANA has received wide application across Accenture’s company business lines not forgetting the corporate functions such as analytics and cloud services [6]. The integration has enhanced internal communication and business decision-making processes as well as establishing strategic, organisational and operational coherence and quality. SAP solutions have helped Accenture to manage its processes effectively hence improving its capacity to deliver its services to clients [6]. 

   These case studies show indeed how the combination of SAP S/4HANA FICO and AI can revolutionise the process, creating higher compliance levels, more efficiency and better decisions.

   1. Evaluation Metrics

   In assessing the effectiveness of AI-integrated internal controls in SAP S/4 HANA FICO the following metrics have apply under each with an independent focus. Metrics like Automation efficiency are evaluated through the following decrease in errors, processing time, and automation to manual operation ratio [9]. Measuring compliance accuracy involves counting the number of compliance issues observed, reducing penalties, and auditing by AI to confirm adherence to regulatory standards. Cost-effectiveness pays attention to the possible savings in terms of fines for non-compliance and other operating costs channeled towards implementing AI innovations [10]. Risk mitigation is measured by the efficiency of distinguishing financial fraud and other practices with the percentage of fraud detected, accuracy in identifying the anomalies, and time taken to manage the risks. Scalability is assessed based on the effectiveness of the AI systems as Operation [11]. Lastly, user adoption examines the extent of users’ acceptance and usage of AI-controlled systems, their training course completion, satisfaction rate, and comments on critical organisational aspects concerning the systems. Each of the above metrics gives an evaluation of how AI has influenced internal controls in S/4 HANA FICO [12].

   IV. Results

   1. Data Presentation

   SAP FICO applies AI to develop data inputs, complicated financial analysis, reconciliation, and data forecasting. Artificial intelligence in NPL, ML, and predictive analytics generates contemporary initiatives to highlight oddity, and risk anticipation, and streamline compliance systems [2]. AI alters internal controls in S/4 HANA FICO by identifying anomalies, automation in the system, 24/7 monitoring, and predictive compliance.

   Figure 3: Companies Investing in AI

   [13]

   The above graph highlights the total international corporate investment made in AI. Investment rose in 2021 to $276.1 billion however, a downgrade was identified in 2022 [13]. Thus, international corporate investment in AI has increased effectively in recent years with $934.2 billion investment made between 2013 to 2022 [13].

   Figure 4: SAP HANA Application

   [14]

   The above graph shows insights into the SAP HANA Application, where 29% of companies have applied it in the process of migration, 45% are evaluating it and 27% of companies have no plans to implement it [14].  However, there are market threats such as complexity in the integration process, challenges with Data Migration and Integration, and Change Management Resistance [15]. AI and ML, Emphasis on Industry-Specific Solutions, and Extension of Ecosystem and Partnerships have been identified as market trends of this adaptation.

   Figure 5: Factors and Benefits of Implementing SAP

   [16]

   Figure 4 shows 10 major elements attached to the implementation of SAP including, required functionality, common platform, total cost of ownership, and others. This also highlighted the leading position of SAP with around 72% of customers reporting their facilities [16]. The international ERP software market is anticipated to enlarge by 8.4% CARG between 2020 to 2026 increasing from $38.84 billion in 2020 to $63.42 billion in 2026 [15]. Demand forecasting integrates AI to interpret past sales information and market trends to anticipate further product recruitment, which is major to managing manufacturing planning and inventory.

   1. Findings

   AI in SAP S/4HANA is pivotal to developing accuracy and productivity rates in process management or PM around frameworks, such as SCM, procurement, and finance. AI in these domains thrives beyond seamless automation, to streamlining and improving the whole workforce. Figure 3 shows the investment rate made by companies in AI. According to Stanford University analysis, entire mergers and acquisitions, private investments, and public offerings were worth around $934.2 billion from 2013 to 2022 [13]. Investment in AI increased most in 2021 with $276.1 billion investment [13]. This tracks investment records of 8 million global public and private sectors. Figure 4 has highlighted the deployment options of SAP S/4HANA where 29%of companies applied it in its system of migration [14]. Lastly, SAP has become the leader in creating facilities for companies after the application with around 72% of customers reporting positive feedback [16]. 

   1. Case Study Outcomes

   Case Study	Company	Key Outcome
   Migration of SAP S/4HANA Finance	Fujitsu	SAP Japan and Fujitsu Limited announced that Fujitsu has proactively finished design efforts in the management of full-scale integration of “SAP S/4HANA® Manufacturing solution” for production of operations and engineering [17]
   Integration of AI with SAP S/4HANA at Deutsche Bank	Deutsche Bank	Deutsche Bank has initiated AI linked with SAP S/4HANA and impacted its risky assessment and financial data management. Integration of AI for the SAP S/4HANA highlighted new scopes to optimising banking procedures and improved competitiveness in the financial landscape [18].
   SAP Business Suite Implementation	Accenture	SAP S/4HANA integration has improved business decision-making, internal communication, and operational, and organisational quality. SAP mitigations have guided Accenture to manage its processes leading to performance enhancement in service delivery [6].

   Table 2: Case Study Outcomes

   1. Comparative Analysis

    

   Aspects of literature review	Focus	Key Findings	Challenges highlighted	Proposed solutions
   [1]	Focuses on the application of SAP S/4 HANA module FICO in the Accounting and Finance division with the help of “Delone and Mclean Information System Success Model.”	Commonly SAP system did not have networking issues and this helped companies to run their business process effectively [1].	The speed and format of responses were some negative views from the user [1].	The success of the SAP system with the S4/HANA version at  Perum  Jasa Tirta  1  Malang added with the  Delone and Mclean models was declared effective [1].
   [2]	Focuses on creating a “comprehensive overview of the current state and future directions of smart city development.”	IoT and AI have positive implications for the urban environment and the wellness of inhabitants [2].	Urbanisation struggles to provide a convenient, sustainable, and secure lifestyle because of the lack of necessary smart technologies [2].	IoT as mitigations [2].
   [3]	Focuses on 2 core methodologies: “a density and distance-based architecture” and “a model-based architecture.”	Trade-offs need to be considered when choosing the appropriate anomaly detection and predictive modelling contexts [3].	Creating an effective AI-based system for predictive analytics and anomaly detection is a major threat [3].	A considerable architectural design with proper algorithms is imposed [3].
   [4]	Focuses on the transformative role of (AI) in improving the effectiveness of international internal audit functions.	AI is pivotal in forming the future of the internal auditing process on a large- scale [4].	Lacks in audit accuracy and time management [4].	AI-driven technologies created facilities for data analysis and risk mitigation [4].

   Table 3: Elaboration of Literature sources

   V. Discussion

   1. Interpretation of Results

   Predictive and analytics instruments in SAP S/4HANA develop data management and create foresight in the operations of companies, demonstrating a sector-wide transformation to more data-based operational planning and informed decision-making. Internal controls applied in ERP systems such as S/4 HANA FICO have prescribed norms that were created in advance to track transactions. Traditional mitigations include more risk of delays and risk of the human factor and decreasing non-compliance [1]. On the other hand, SAP S/4HANA has been connected with AI-based analytics which signifies its potential [14]. Thus, AI in SAP S/4HANA has significantly developed accuracy and efficiency rates in process management around several domains including, supply chain management, procurement, finance, and others. The application of AI in SAP S/4HANA with user interface specifically in SAP Fio marks a major improvement in the user communication with the ERP methods.    

   1. Practical Implications

   Automating workflows for activities such as approval processes as well as mollification decreases the chances of manual flaws and develops operational capabilities. Additionally, real-time observation of variation specifies a seamless resignation of uncertainties, improving fraudulence mitigation. Real-time dashboards and ongoing data monitoring processes smoothen authoritative reporting and preparation of regular audits, decreasing further charges and time. FICO is a module that is major in managing accounting and financial processes [19]. However, effective implementation of its need’s major employee training, strong data quality, and smooth application of data with pre-existing mechanisms.

   1. Challenges and Limitations

   The present work had its boundaries. The researcher has applied secondary qualitative and quantitative data collection and analysis methods and this draws a major limitation with the involvement of descriptive and bias in information. Additionally, a few case study instances such as Fujitsu, Deutsche Bank, and Accenture restricted the generality and wider aspects of the research outcomes [6]. 

   1. Recommendations

   Organisations and exerts willing to integrate AI in S/4 HANA FICO will impose data governance to specify the reliability and quality of financial information for AI frameworks. However, the governance of digitalisation cannot depend effectively on data-centric economic frameworks [20]. Daily maintenance and upgradation will be proactive by the algorithms of AI to comply with emerging financial policies. Additionally, the application of an ongoing monitoring system to approach AI-improved controls for large sectors and SMEs. Lastly, employee training and awareness of AI in S/4 HANA FICO specify its usage and adaptation rate.

   
   

[1] Rarantika, L.C. and Firmanto, Y., 2024. THE MANIFESTATION OF DELONE AND MCLEAN MODEL IN EVALUATING THE IMPLEMENTATION OF ERP SAP TO FINANCIAL STATEMENTS. International Journal of Research on Finance & Business, 2(1), pp.85-101.

[2] Alahi, M.E.E., Sukkuea, A., Tina, F.W., Nag, A., Kurdthongmee, W., Suwannarat, K. and Mukhopadhyay, S.C., 2023. Integration of IoT-enabled technologies and artificial intelligence (AI) for smart city scenario: recent advancements and future trends. Sensors, 23(11), p.5206.

[3] Agrawal, S., 2022. Enhancing payment security through AI-Driven anomaly detection and predictive analytics. International Journal of Sustainable Infrastructure for Cities and Societies, 7(2), pp.1-14.

[4] Ghafar, I., Perwitasari, W. and Kurnia, R., 2024. The Role of Artificial Intelligence in Enhancing Global Internal Audit Efficiency: An Analysis. Asian Journal of Logistics Management, 3(2), pp.64-89.

[5] Medium.com, 2020, Finance Assignment Examining SAP S/4HANA Finance

, Available at: https://medium.com/%40rowetim1/finance-assignment-examining-sap-s-4hana-finance-c1fb670f6272 [Accessed on: 15 January 2025]

[6] Accenture.com, 2025, Powered by SAP, Available at: https://www.accenture.com/us-en/case-studies/about/powered-sap [Accessed on: 15 January 2025]

[7] Ijrpr.com, 2024, Integrating Sap, AI, And Data Analytics for Advanced Enterprise Management, Available at: https://ijrpr.com/uploads/V5ISSUE10/IJRPR33869.pdf [Accessed on: 15 January 2025]

[8] Begna, D., Bacha, T., Boki, S. and Bekana, K., 2025. Characterization of indigenous chicken phenotypes in Liban Jawi District, Ethiopia: A qualitative and quantitative analysis. PloS one, 20(1), p.e0307793.

[9] Rane, N.L., Choudhary, S.P. and Rane, J., 2024. Artificial Intelligence-driven corporate finance: enhancing efficiency and decision-making through machine learning, natural Language Processing, and robotic process automation in corporate governance and sustainability. Studies in Economics and Business Relations, 5(2), pp.1-22.

[10] Dharmasiri, P., Phang, S.Y., Prasad, A. and Webster, J., 2022. Consequences of ethical and audit violations: Evidence from the PCAOB settled disciplinary orders. Journal of Business Ethics, pp.1-25.

[11] Bello, O.A. and Olufemi, K., 2024. Artificial intelligence in fraud prevention: Exploring techniques and applications challenges and opportunities. Computer Science & IT Research Journal, 5(6), pp.1505-1520.

[12] Lewis, J.R. and Sauro, J., 2021. Usability and user experience: Design and evaluation. Handbook of human factors and ergonomics, pp.972-1015.

[13] Statista.com, 2023. How Much Are Companies Investing in AI? Available at: https://www.statista.com/chart/31314/global-corporate-investment-in-artificial-intelligence/ (Accessed on: 15 January 2025).

 [14] 2.deloitte.com, 2025. The CFO guide to SAP S/4HANA® and Central Finance, Available at: https://www2.deloitte.com/us/en/pages/finance-transformation/articles/cfo-guide-sap-s4hana-central-finance.html(Accessed on: 15 January 2025).

[15] Marketresearchintellect.com, 2025. SAP S-4HANA Application Market Size By Product, By Application, By Geography, Competitive Landscape And Forecast, Available at: https://www.marketresearchintellect.com/product/global-sap-s-4hana-application-market-size-forecast/(Accessed on: 15 January 2025).

[16] Kunchala, M.R., 2024. SAP ECC to S4 HANA Changes in Finance Best Practices: Business Users-PART 1. Available at SSRN 4807972.

[17] Fujitsu.com, 2019. Fujitsu Takes Key Step Toward Implementing SAP S/4HANA® Manufacturing for Production Engineering and Operations to Streamline Aircraft Production at Kawasaki Heavy Industries, Available at: https://www.fujitsu.com/global/about/resources/news/press-releases/2019/1205-02.html (Accessed on: 15 January 2025).

[18] Itcoursetraining.hashnode.dev, 2024. How Deutsche Bank Leverages SAP HANA To Manage Its Financial Data?Available at: https://itcoursetraining.hashnode.dev/how-deutsche-bank-leverages-sap-hana-to-manage-its-financial-data(Accessed on: 15 January 2025).

 [19] Vaka, D.K., 2023. Empowering Food and Beverage Businesses with S/4HANA: Addressing Challenges Effectively. J Artif Intell Mach Learn & Data Sci, 1(2), pp.376-381.

[20] Purtova, N. and van Maanen, G., 2024. Data as an economic good, data as a commons, and data governance. Law, Innovation and Technology, 16(1), pp.1-42.