Cloud Accounting, Artificial Intelligence, and Machine Learning in Digital Financial Applications: Implications for MSME Accounting Information in South Sumatra

Abstract

Many MSMEs still do not fully understand the use of digital financial applications, while optimal implementation of artificial intelligence and machine learning features is increasingly important for modern accounting practices. Referring to the Technology Acceptance Model, technology acceptance is influenced by perceived usefulness and perceived ease of use. This study examines the effects of cloud accounting, artificial intelligence, and machine learning on digital financial applications and the implications for the quality of MSME accounting information in South Sumatra. The study employed Partial Least Squares–Structural Equation Modeling (PLS-SEM) using survey data from 150 MSME managers. The empirical results confirm that cloud accounting has the strongest effect on digital financial applications (β = 0.42), followed by artificial intelligence (β = 0.35) and machine learning (β = 0.28).

Digital financial applications positively and significantly affect accounting information quality (β = 0.28). These findings suggest that digital financial applications improve perceived usefulness and ease of use through automated transaction recording, real-time financial analysis, and faster, more accurate reporting. The findings also demonstrate that technology investment depends heavily on user understanding and skills. The study is limited by the dynamic nature of digital financial applications, which evolve with technological innovation, feature updates, and changing user behavior.

Keywords: cloud accounting; artificial intelligence; machine learning; digital financial applications; accounting information.

Introduction

The development of digital technology is driving significant changes in various business sectors, including Micro, Small, and Medium Enterprises (MSMEs). In the digital economy era, MSMEs are required to adapt to technology to remain competitive and sustainable. One crucial aspect of business management is effective financial management through an accurate, transparent, and timely accounting system (Bramulya et al., 2025). However, many MSMEs still face challenges in recording and managing accounting data (Shabalov et al., 2021). Various problems, including excesses and deficiencies, occur due to poor management, especially those that often occur in MSMEs, such as the low quality of accounting data produced (Buchalska-sugajska et al., 2025). MSME actors have not carried out financial records systematically, still using manual methods that are prone to errors, delays, and data loss (Ghimire & Qiu, 2025).

This condition has an impact on the low quality of financial reports, thus hampering the process of making the right business decisions (Olawade et al., 2025a). Limited understanding of accounting and minimal digital literacy are inhibiting factors in optimizing MSME financial management (Kes et al., 2025). Along with technological developments, various digital financial application solutions have emerged designed to help business actors manage their finances more efficiently (Oseas et al., 2025). Digital applications offer automatic transaction recording features, financial report preparation, and real-time integration (Harverson et al., 2026). In practice, the use of digital financial applications by MSMEs has not been optimal due to a lack of user understanding, limited access to technology, and concerns about financial data security (Yeassin et al., 2025). This research framework focuses on the implementation of cloud-based accounting as an important innovation in accounting data management (P.

Liu & Yang, 2025). Cloud accounting systems can store data online, are flexible, and provide easy collaboration between users. The adoption of cloud accounting in MSMEs still faces challenges such as limited infrastructure, trust in digital systems, and data security risks (Ding et al., 2025). The development of artificial intelligence and machine learning is integrated into digital financial applications to automate accounting processes, detect transaction patterns, and provide more accurate financial predictions (Ndiaye et al., 2026). With AI and machine learning, accounting data quality can be improved through faster data processing and fewer errors. The use of technology in accounting practices in MSMEs is still low, due to a lack of understanding of the technology and limited resources (Paget et al., 2025). Another equally important issue is related to the security and integrity of accounting data.

In digital systems, data is a key asset whose accuracy and security must be maintained. Risks such as data leaks, system errors, and information manipulation are challenges that MSMEs must face when using advanced technology-based digital financial applications (Ho et al., 2025). Extensive research has been conducted on digital transformation in accounting, particularly examining the impact of digital technology on the quality of accounting information and business performance. Several previous studies have found that cloud accounting can improve the efficiency of recording and accessibility of financial data, while others have shown that artificial intelligence (AI) and machine learning (ML) can improve the accuracy of data analysis and support business decision-making. However, most of these studies have examined the impact of each technology separately and have not integrated various digital technologies into a comprehensive research model.

From a theoretical perspective, there remains a gap regarding how the combination of modern digital technologies, such as Cloud Accounting, Artificial Intelligence (AI), Machine Learning (ML), and Digital Financial Applications (DFA), simultaneously impacts the quality of accounting information. Previous research has focused more on the direct relationship between a single technology and organizational performance, thus failing to provide a comprehensive understanding of the mechanisms by which various digital technologies integrate to produce quality accounting information. Empirically, research on cloud accounting, artificial intelligence, and machine learning related to digital financial applications and their implications for accounting information quality in the Micro, Small, and Medium Enterprises (MSMEs) sector in South Sumatra is still limited. Most previous studies focused on large companies, the banking sector, or MSMEs in other regions, so the results may not reflect the characteristics of MSMEs in South Sumatra, which have varying levels of digital literacy, technological capacity, and operational conditions.

Yet, MSMEs are a strategic sector that makes a significant contribution to the regional economy and faces the increasing demands of digital transformation (Alawida et al., 2022). The uniqueness of this study lies in examining the influence of Cloud Accounting, Artificial Intelligence, and Machine Learning on Accounting Information Quality through Digital Financial Applications as a mediating variable. In this study, Digital Financial Applications serve as a mediator, bridging the influence of Cloud Accounting, Artificial Intelligence, and Machine Learning on Accounting Information Quality. In other words, the implementation of digital technology does not directly result in improved accounting information quality, but rather increases the effectiveness of digital financial applications, which then contributes to improved accounting information quality. As a mediating variable, digital financial applications act as a mechanism for transforming technology into more efficient, integrated, and data-driven financial management practices.

Unlike previous research, which generally examined the influence of each technology separately, this study integrates all three digital technologies into a comprehensive conceptual framework. In this research model, digital financial applications serve as a mechanism explaining how the use of digital technology can improve accounting information quality. Thus, the study not only identifies the direct influence of digital financial applications on accounting information quality but also uncovers the mediation process that occurs through the integrated use of all three digital technologies. This approach allows for a deeper understanding of the relative contribution of each digital technology to improving accounting information quality and produces more comprehensive and accurate estimates of the relationships between variables. Based on these theoretical and empirical gaps, this study seeks to answer the following research questions: (1) How does Cloud Accounting affect Digital Financial Applications in MSMEs in South Sumatra. (2) How does Artificial Intelligence affect Digital Financial Applications in MSMEs in South Sumatra. (3) How Machine Learning influences Digital Financial Applications for MSMEs in South Sumatra (4) How Digital Financial Applications influence the quality of accounting information for MSMEs in South Sumatra.

The proposed research hypotheses are: H1: Cloud Accounting has a positive influence on Digital Financial Applications for MSMEs in South Sumatra. H2: Artificial Intelligence has a positive influence on Digital Financial Applications for MSMEs in South Sumatra. H3: Machine Learning has a positive influence on Digital Financial Applications for MSMEs in South Sumatra. H4: Digital Financial Applications have a positive influence on the quality of accounting information for MSMEs in South Sumatra. The urgency of this research is expected to overcome managerial constraints in small and medium enterprises through efforts to improve technological developments such as cloud accounting, artificial intelligence, machine learning, and digital financial applications that offer various conveniences and advantages, but their implementation in MSMEs still faces various obstacles (Wu & Liao, 2025a). This research is important to provide a deeper understanding of the role of digital technology in improving the quality of accounting data and supporting more effective and efficient business decisionmaking in MSMEs in the digital era (Basnayake et al., 2024).

In this regard, this research is expected to provide a positive contribution in overcoming various obstacles faced, such as suboptimal accounting data quality, low digital literacy, and limitations in technology utilization (Chhillar et al., 2025). Therefore, the purpose of this study is to examine how the application of technology affects the quality of accounting data in supporting better financial management of MSMEs (Segundo et al., 2025). As illustrated in the conceptual framework (Figure 1), agency theory explains the working relationship between principals (business owners) and agents (business managers), Figure 1. Conceptual Framework

Conceptual Framework

Theoretical Development and Hypotheses

Cloud Accounting

Artificial Intelligence

Machine Learning

ML ML1, ML2, ML3 3 Digital Financial Applications DFA DFA 1, DFA 2, DFA 3, DFA 4 4 age, the majority are in the 30–35 years range (36.7%), followed by 36–40 years (33.3%) and 41–45 years (30%), indicating that respondents are in the productive and strategic age range in decision-making. Judging from work experience, most respondents have 5–10 years of experience (43.3%), followed by more than 10 years (30%) and less than 5 years (26.7%). This indicates that the majority of respondents have sufficient experience in managing businesses, making it relevant in providing information related to digital technology adoption. Table 10 explains that all over variables, namely Cloud Accounting (CA) (0.68), Artificial Intelligence (AI) (0.65), Machine Learning (ML) (0.63), and Digital Financial Applications (DFA) (0.70), have The AVE value is above 0.50. This indicates that each construct meets convergent validity 2026 Primary & Secondary Data 45 2026 Secondary Data 20 2026 Secondary Data 15 2026 Secondary Data 18 2026 Secondary Data 17 2026 Primary Data 10 2026 Secondary Data 15 criteria, ensuring that the indicators adequately explain the variables.

All variables in this study are valid and suitable for further analysis. Table 11 shows that the diagonal values (square root of AVE), namely CA (0.82), AI (0.80), ML (0.79), and DFA (0.84), are all greater than the correlations between the other variables. This indicates that each construct has a good ability to differentiate itself from other constructs. The research model has met discriminant validity, so that each variable is unique and does not overlap. Table 12 explains All over variables, namely Cloud Accounting (CA), Artificial Intelligence (AI), Machine Learning (ML), and Digital Financial Applications (DFA) have The Cronbach's Alpha value was above 0.70 (0.83–0.90) and the Composite Reliability was above 0.70 (0.88–0.92). This indicates that all constructs have high internal consistency, so the research instrument is declared reliable and can be used for further analysis. Table 13 states that the VIF value for Cloud Accounting (CA) is 2.10, Artificial Intelligence (AI) is 2.30, and Machine Learning (ML) is 2.05, all of which are below the critical limit of 5.

This indicates that there is no multicollinearity problem among the independent variables, so the research model is worthy of use for further regression analysis. Table 14 shows the R-Square (R²) values for all endogenous constructs in the PLS-SEM model, namely Digital Financial Applications and Accounting Information Quality in MSMEs in South Sumatra. The R² value of Digital Financial Applications of 0.68 indicates that 68% of the variation in Digital Financial

Digital Financial Applications

Digital financial applications are information technologybased systems used to collect, process, store, and distribute financial information electronically to support organizational control and decision-making (Basnayake et al., 2024). Digital financial applications are part of financial technology innovation that utilizes digital technology to provide financial services such as payments, fund transfers, transaction recording, and efficient, fast, and integrated financial management (Olawade et al., 2025a). Digital financial applications are technology-based tools that enable organizations and individuals to manage financial activities in real-time, increasing transparency, operational efficiency, and supporting data-driven decision-making. Digital Financial Applications are technology-based systems that facilitate electronic, real-time, and integrated financial management, thereby increasing the efficiency, accuracy, and quality of financial information within an organization (Rumanti et al., 2025).

There are several ways to measure this Digital Financial Application Digital Finance Applications are measured using indicators that reflect usage levels, system quality, and their impact on financial management performance (Ozili, 2021) (see Table 4). These measurements are generally multidimensional, encompassing aspects of technology, users, and organizational benefits, as follows: 1. Usage Level Measures the frequency and duration of application use in financial activities, such as transaction recording, reporting, and digital payments. The higher the frequency of use, the higher the level of technology adoption. 2. System Quality Measuring the technical performance of the application, including access speed, system reliability, ease of use (user-friendliness), and minimal disruption/errors in operations. 3. Information Quality Measures the extent to which the financial information produced is accurate, relevant, timely, and reliable to support decision making. 4.

Quality of Service Measure the service support provided, such as technical assistance, system updates, and response to user issues. Measure the level of user financial data protection, including encryption, access control, and security systems to prevent data leakage. Measure the extent to which the application provides tangible benefits, such as increasing efficiency, productivity, and the quality of financial management. Measure the application's contribution to improving organizational performance, including decisionmaking speed, financial control, and operational efficiency (Rachinger et al., 2019). Digital Financial Applications are measured comprehensively, considering technical aspects, information quality, security, and user benefits. This approach allows researchers to assess the application's effectiveness in supporting modern financial systems and overall organizational performance (Paula Monteiro et al., 2022).

Accounting Information

Accounting information is the result of processing financial transaction and activity data presented in a relevant, accurate, and timely manner to assist users in planning, controlling, and making economic and business decisions. There are several ways to measure accounting information. Accounting information is a collection of financial data that has been processed into useful information for users in planning, controlling, and decision-making (Lindiasari & Alfarizi, 2025) (see Table 5). There are several ways to measure the quality of accounting information. In general, the quality of accounting information is measured based on the ability of the information produced to support effective decision-making. The quality of accounting information can be measured using the following indicators: 1. Accuracy The accounting information presented is free from errors, reflects actual conditions, and is reliable for users. 2.

Relevance The information produced meets user needs and is able to assist in the decision-making process. 3. Timeliness Information is available when needed so that it can be used effectively in planning, controlling, and decision-making. 4. Completeness The information presented includes all essential data necessary to provide users with a complete picture of the organization's financial and operational condition. The Effect of Cloud Accounting on Digital Financial Applications Based on theoretical foundations and empirical findings, cloud accounting has a positive effect on digital financial applications. This demonstrates the real-time storage, processing, and exchange of financial data. This capability improves the accuracy, speed, accessibility, and integration of financial data required for the operation of digital financial applications. Cloud-based systems are integrated with various digital platforms, such as e-commerce, sales, payments, banking, and electronic payment systems, thereby increasing the efficiency and automation of financial processes (Findings, 2020).

Research by (Yang et al., 2023) shows that cloud technology provides a foundation that supports faster, more accurate, and automated data processing. The higher the level of Cloud Accounting implementation, the better the performance of Digital Finance Applications. Cloud Accounting has a positive influence on Digital Finance Applications. Based on these theoretical arguments and empirical findings, the research hypothesis is formulated as follows: H1: Cloud Accounting has a positive influence on Digital Financial Applications

Methods

Research Type

Quantitative Study

Study Design and Sampling

This research used a quantitative approach with a survey method. The study population comprised all Micro, Small, and Medium Enterprises (MSMEs) in South Sumatra that have utilized digital financial applications in their financial management. The sampling frame was obtained from MSMEs registered with the South Sumatra Cooperatives Office, MSME communities, and digital financial application user networks accessible to the researcher. The sampling technique used was purposive sampling because this study required respondents with specific characteristics consistent with the research objectives. Respondent inclusion criteria included: (1) owners or managers of MSMEs operating in South Sumatra; (2) using digital financial applications for financial recording and management; (3) having used the applications for at least one year; and (4) willingness to complete the research questionnaire. The sample size used was 150 respondents.

This sample size was deemed adequate for Partial Least Squares–Structural Equation Modeling (PLSSEM) analysis, as it met the recommended minimum sample size of 10 times the number of structural paths or largest indicators leading to a construct in the research model. Furthermore, this number meets the minimum recommended threshold in SEM-based research to produce stable and reliable parameter estimates. The research data was collected using a five-point Likert-scale questionnaire distributed via Google Forms to respondents who met the research criteria.

Population and Sample

Population in study This is manager level middle aged the respondents were aged between 30 and 45 years old and worked in Micro, Small, and Medium Enterprises (MSMEs) in South Sumatra, Indonesia. This group was selected because they play a strategic role in operational decision-making and technology implementation within the organization (Edeh et al., 2023). For the quantitative approach, the sampling technique used stratified random sampling to ensure representation based on business scale (small, medium, and large). Through this technique, 150 respondents participated in the survey. For the qualitative approach, informants were selected using purposive sampling, namely by selecting individuals deemed to have experience and in-depth understanding of digital transformation in MSMEs. The informants involved were approximately 10–15 senior managers with experience in implementing technologies such as cloud accounting and artificial intelligence.

Research Location

This research was conducted in South Sumatra Province, known for its diverse culinary traditions, creative industries, and a mix of rural and urban areas. These characteristics make South Sumatra a representative location for examining the dynamics of MSMEs in various business environments. This heterogeneous environment provides a unique context for exploring the relationship between managerial practices and organizational performance, particularly in addressing the challenges of digital transformation and the adoption of technologies such as digital financial applications, artificial intelligence, and machine learning.

Research Instruments

The research instrument used a structured questionnaire adapted from previous research on the adoption of digital technology and accounting information systems. All items were measured using a 5-point Likert scale, ranging from 1 = strongly disagree to 5 = strongly agree. Before distributing the questionnaire, the instrument was translated and adapted to the context of MSMEs in South Sumatra. It was then evaluated by three experts, consisting of academics in the field of accounting information systems and financial technology practitioners, to ensure content validity, language clarity, and appropriateness to the research context. The Cloud Accounting construct was measured using four indicators: real-time data accessibility, financial data integration, data management efficiency, and system ease of use. An example of a statement item is: "I can access my business's financial data anytime and from any location through a cloud-based system." The Artificial Intelligence construct was measured using four indicators: financial process automation, data analysis capabilities, decision-making support, and error or transaction anomaly detection.

An example of a statement item is: "The system used is capable of providing financial recommendations based on automatic data analysis." The Machine Learning construct is measured using four indicators: the ability to recognize transaction patterns, the accuracy of financial predictions, learning from historical data, and improving the quality of data analysis. An example of a statement item is: "The system is able to predict the financial condition of the business based on previous transaction data." The Digital Financial Application construct is measured using four indicators: ease of use of the application, effectiveness of financial management, transaction processing speed, and integration of digital services. An example of a statement item is: "The digital financial application helps me manage business transactions more efficiently." The Accounting Information construct is measured using four indicators that refer to the quality characteristics of accounting information: relevance, reliability, timeliness, and ease of understanding.

An example of a statement item is: "The financial information generated by the system helps me in making business decisions." Construct validity is evaluated through factor loading values, Average Variance Extracted (AVE), and discriminant validity. While instrument reliability is tested using Cronbach's Alpha and Composite Reliability (CR). All constructs are declared to meet the validity and reliability criteria if the factor loading value is > 0.70, AVE > 0.50, and CR and Cronbach's Alpha > 0.70.

Data Collection Procedures

Quantitative data were collected through the Google Forms platform over a three-month period, starting in January and March 2026. Additionally, secondary data were obtained from MSME reports, government publications, and other relevant sources. Respondents were provided with a consent form and clear instructions to ensure the quality of the data collected (Arya et al., 2020). Qualitative data collection was conducted through in-depth interviews conducted face-toface at the respondents' workplaces or through a virtual platform. Each interview session lasted approximately 30–60 minutes and used a semi-structured guide. In a mixed methods approach, data collection is conducted in stages, beginning with quantitative data collection, followed by qualitative interviews, tailored to the survey results and respondent availability.

Data Analysis

Quantitative data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Evaluation of the measurement model (outer model) was conducted through outer loading testing, construct reliability using Cronbach's Alpha and Composite Reliability (CR), convergent validity using Average Variance Extracted (AVE), and discriminant validity using the Fornell-Larcker and HeterotraitMonotrait Ratio (HTMT) criteria. Furthermore, evaluation of the structural model (inner model) was conducted by testing for multicollinearity through the Variance Inflation Factor (VIF), coefficient of determination (R²), predictive relevance (Q²), and effect size (f²). Hypothesis testing was conducted using bootstrapping procedures for t-statistic values, p-values, standard errors, and confidence intervals. In addition, descriptive statistics were used to describe the demographic characteristics of respondents.

Ethical Approval

This research has obtained approval from the Ethics Committee of Indo Global Mandiri University with Approval Number: 250/E/ST/VI/2025. All research participants have provided informed consent before participating in the study. For participants aged over 24 years, the researcher guarantees that all data collected will be kept strictly confidential, by anonymizing the respondents' identities and storing the data securely in accordance with research ethics standards.

Result and Discussion

The results section presents the main findings of the study. Based on quantitative data analysis, the results indicate a significant relationship between the implementation of cloud accounting, artificial intelligence, and machine learning and the use of digital financial applications. This finding indicates that the higher the adoption rate of digital technology in MSMEs, the greater the use of digital financial applications to support their performance and decision-making. Table 6 presents the distribution of 150 MSMEs that were the object of research in South Sumatra based on business type, data source, period, and data format used. The data was obtained from a combination of primary data (direct survey results) and secondary data (relevant government agencies), with an observation period of 2026. The distribution results show that the specialty food MSME sector dominates with 45 business units or approximately 30% of the total sample.

This indicates that traditional culinary such as pempek, tekwan, and model remains a leading sector and has a significant contribution to the regional economy. Snack food MSMEs numbered 20 units (±13%), reflecting the high demand for practical processed products such as chips and crackers. Meanwhile, MSMEs in the beverage sector (coffee, syrup, and herbal) were recorded at 15 units (10%), indicating the potential for developing products based on local resources. In the creative industry sector, creative fashion MSMEs such as songket, jumputan, and batik totaled 18 units (12%), reflecting the strength of local culture in supporting the creative economy. Craft MSMEs (woven, wood, and rattan) also showed a significant contribution with 17 units (±11%), indicating the sustainability of the traditional craft industry. MSMEs in handmade accessories totaled 10 units (7%) and creative digital totaled 15 units (10%) indicate a shift towards a creativity and technology-based economy.

The presence of creative digital MSMEs, such as graphic design and branding, is an important indicator in supporting the digital transformation of MSMEs. Hybrid MSMEs (culinary and digital marketing) totaled 10 units (7%) reflect the integration between the traditional sector and digital technology. This indicates that some MSMEs are starting to adopt digital marketing strategies to increase business competitiveness. The research data consists of primary and secondary data, each serving a different function in the study. Primary data was obtained through questionnaires distributed to 150 MSME owners or managers in South Sumatra who met the research sample criteria (see Table 7). All primary data were used as units of analysis in testing the empirical model using PLS-SEM. Meanwhile, secondary data were obtained from government agency reports, statistical publications, and documents related to MSME development in South Sumatra.

Secondary data were not used as additional observations in the empirical model but rather to describe the characteristics of MSMEs, the level of business digitalization, and the general condition of the MSME sector in the research area. Therefore, all hypothesis testing was based on 150 survey respondents, while secondary data served as supporting information to strengthen the interpretation of the research results. Based on respondent data, 80 MSMEs operate in the culinary sector and 70 MSMEs in the creative industry sector. In terms of business digitalization, 25 MSMEs have intensively adopted technology, while 125 MSMEs are still at a limited digitalization stage. These characteristics provide an empirical context regarding the level of digital technology utilization that forms the basis for analyzing the relationship between Cloud Accounting, Artificial Intelligence, Machine Learning, Digital Financial Applications, and Accounting Information Quality.

Table 8 shows the construction of research variables used to measure the relationship between digital technology and the use of financial applications in MSMEs in South Sumatra. Each variable is measured using several indicators arranged in the form of Likert-based questionnaire items. The Cloud Accounting (CA) variable has four indicators (CA1–CA4) used to measure the level of implementation of cloud-based accounting systems. The Artificial Intelligence (AI) variable consists of three indicators (AI1–AI3) that represent the use of artificial intelligence in business management. Furthermore, Machine Learning (ML) also has three indicators (ML1–ML3) to measure the use of machine learning technology in business data analysis. Meanwhile, the dependent variable Digital Financial Applications (DFA) is measured by four indicators (DFA1–DFA4) that describe the level of use of digital financial applications in MSME operations.

Overall, this table shows that the research instrument has been designed in a structured manner with a sufficient number of items to measure each variable validly and reliably. Based on Table 9, the gender of respondents is dominated by men at 60%, while women are 40%. In terms of

Conclusion

Author Contributions

Acknowledgements

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