p–ISSN: 2723 - 6609 e-ISSN: 2745-5254

Vol. 5, No. 10, October 2024 http://jist.publikasiindonesia.id/

Indonesian Journal of Social Technology, Vol. 5, No. 10, October 2024 3881

The Use of Artificial Intelligence in Financial Statement Audit

Nurul Fachriyah

, Octadila Laily Anggraeni

Universitas Brawijaya, Indonesia

Email: [email protected]

, [email protected]

*Correspondence

ABSTRACT

Keywords: artificial

intelligence; audit;

financial statement.

The rapid advancement of Artificial Intelligence (AI) has

transformed various industries, including financial auditing,

by improving efficiency, accuracy, and fraud detection. This

study investigates the extent of AI adoption in financial

audits in Indonesia, with a focus on both Big 4 audit firms

and smaller, local firms. Through a literature review and

interviews with auditors from eight firms, the research

explores the current state of AI utilization and the barriers to

its implementation. The results indicate that while Big 4

firms are in the developmental phase of integrating AI into

their auditing processes, smaller firms face significant

obstacles, such as financial limitations, lack of expertise, and

regulatory uncertainties, which hinder AI adoption. Despite

the challenges, auditors from larger firms anticipate that AI

will play a crucial role in future audits. The study concludes

that AI adoption in Indonesian financial audits is uneven,

and further efforts are required to support smaller firms

through accessible AI tools, clearer regulations, and targeted

training. These measures are essential for closing the gap in

audit quality between large and small firms, ensuring

broader AI implementation in the auditing sector.

Introduction

The rapid advancement of information technology has brought transformative

changes to various sectors, with the financial industry being among the most affected

(Rukmana et al., 2024). A key driver of this transformation is the rise of Artificial

Intelligence (AI), which has permeated multiple domains, including banking, investment

management, accounting, and notably, the auditing of financial statements. AI’s ability

to analyze vast quantities of data, identify hidden patterns, and optimize traditionally

manual and time-consuming processes holds immense potential for enhancing both the

efficiency and accuracy of financial audits (Khokhar & Khan, 2022). Despite its

advantages, the adoption of AI in financial auditing in Indonesia remains limited and

fragmented. As the complexity and volume of financial transactions increase globally,

and especially in Indonesia, traditional audit methods, which often rely on sampling and

manual review, may no longer be sufficient. One of the critical areas where AI can bring

about substantial improvements is in fraud detection. Traditional auditing techniques

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Indonesian Journal of Social Technology, Vol. 5, No. 10, October 2024 3882

typically involve examining a subset of transactions, which can leave room for undetected

fraud. In contrast, AI allows for the analysis of entire datasets, enabling auditors to

identify anomalies and potential fraud more effectively (Beemamol, 2024). This shift

from selective sampling to full population testing significantly enhances the reliability of

the audit process (Tiwari et al., 2019). However, Indonesia’s financial audit sector still

relies heavily on conventional methods, which may not fully address the challenges posed

by increasing transaction volumes and the demand for real-time analysis. Additionally,

AI has the potential to improve compliance with financial regulations and standards, such

as the International Financial Reporting Standards (IFRS). Given the frequent updates to

financial regulations and the regional variations in compliance requirements, staying up-

to-date can be challenging. AI tools can be programmed to automatically incorporate

these updates, ensuring that financial statements are prepared according to the latest

guidelines, thereby reducing the risk of non-compliance (Wilform Jr, 2023). Despite these

clear advantages, there is limited research on the barriers and facilitators of AI adoption

in financial audits in Indonesia. Factors such as regulatory constraints, technological

infrastructure, auditor expertise, and organizational culture may contribute to the slow

uptake of AI in the country (Pasyarani, 2023).

This research is important for several reasons. First, the financial sector plays a vital

role in Indonesia's economic development, and the integrity of financial audits is essential

to maintaining investor confidence and regulatory compliance. As the complexity of

financial transactions grows, so does the need for advanced audit tools that can analyze

large volumes of data quickly and accurately. AI has been proven to improve the

efficiency and accuracy of financial audits by automating routine tasks, identifying

anomalies, and providing deeper insights into financial data. (Pratama et al., 2023).

Second, the regulatory environment in Indonesia is becoming increasingly stringent, with

new laws and standards aimed at improving corporate governance and financial

transparency. The adoption of AI in audits could help auditors comply with these

regulations by ensuring more rigorous oversight and detection of non-compliance.

However, without a clear understanding of the challenges faced by auditors and firms in

adopting AI, it will be difficult to realize these benefits. Finally, the research will

contribute to the broader field of accounting and auditing by addressing a significant gap

in the literature. While there has been extensive research on the use of AI in auditing in

developed economies, the specific factors that affect AI adoption in emerging markets

like Indonesia have not been adequately explored. This study aims to fill that gap by

providing insights into the Indonesian context and offering recommendations for

practitioners and policymakers.

Research Methods

This research will employ a mixed-methods approach, combining a comprehensive

literature review with semi-structured interviews. The objective is to explore the current

use of Artificial Intelligence (AI) in financial audits and the factors affecting its adoption

in Indonesia. The research method will be divided into two parts: (1) a literature review

The Use of Artificial Intelligence in Financial Statement Audit

Indonesian Journal of Social Technology, Vol. 5, No. 10, October 2024 3883

to compare and analyze recent studies on AI in financial auditing, and (2) interviews with

auditors from both large (Big 4) and small audit firms in Indonesia to gather practical

insights on AI adoption.

Figure 1 Research Method Design

The first phase of the research involves a systematic review of the latest academic

articles, industry reports, and relevant publications on the use of AI in financial audits.

The focus will be on identifying key advancements in AI technology as applied to

auditing, including fraud detection, automation of routine tasks, compliance with

regulations, and predictive analytics. Specifically, the literature review will compare

findings from studies conducted in developed economies with those from emerging

markets, with a special emphasis on any existing research regarding Indonesia’s audit

sector. The literature review will serve three main purposes:

1. Identify Best Practices: Review current AI applications in financial audits to outline

best practices, especially in areas such as fraud detection, compliance, and

automation.

2. Highlight Gaps in Research: Examine the gaps in the literature concerning the

adoption of AI in auditing in Indonesia and other emerging markets.

3. Develop Interview Themes: The findings from the literature will inform the

development of interview questions, ensuring the interviews are grounded in existing

theoretical and practical insights.

The second phase of the research involves conducting semi-structured interviews

with auditors to gain a deeper understanding of AI adoption in Indonesia’s financial audit

sector. The interviewees will include auditors from both Big 4 audit firms and smaller

audit offices, ensuring a diversity of perspectives regarding the challenges and

opportunities associated with using AI in financial audits. A total of 8 auditors will be

interviewed, divided as follows:

1. 4 auditors from Big 4 firms: Auditors from the Big 4 firms are expected to have

exposure to global best practices and cutting-edge AI technologies in auditing.

2. 4 auditors from small audit firms: Auditors from smaller firms may face unique

challenges in AI adoption, such as limited resources or access to technology, and their

insights will provide a comparative understanding of the local auditing environment.

The semi-structured interviews will include open-ended questions to allow

participants to provide detailed responses while also covering key themes identified in

the literature review. The interview questions will focus on the following areas:

Literature

Review

Semi Structured

Interview

Data Analysis

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1. Current AI Adoption: What AI tools are currently used in their auditing processes,

and what tasks are they used for?

2. Barriers to AI Implementation: What challenges do auditors face in adopting AI

technologies, such as cost, lack of technical expertise, or resistance from

management?

3. Perceived Benefits: How do auditors perceive the benefits of AI, such as improved

audit efficiency, fraud detection, and compliance with regulations?

4. Future Outlook: How do auditors see the future of AI in financial auditing, and what

factors might drive or hinder its wider adoption in Indonesia?

The interviews will be conducted either in person or via video conferencing

platforms, depending on the availability and preference of the respondents. Each

interview is expected to last approximately 45 to 60 minutes, and all sessions will be

recorded (with the consent of the participants) to ensure accurate transcription and

analysis.

The findings from the literature review will be organized into thematic areas,

including AI applications in fraud detection, audit automation, compliance, and predictive

analytics. A comparative analysis will be conducted to assess how AI adoption in

financial audits in Indonesia aligns with or differs from global trends, highlighting

specific contextual factors that may affect its implementation. The interview transcripts

will be analyzed using a thematic coding approach. Key themes such as barriers to AI

adoption, benefits of AI, and differences in adoption between large and small firms will

be identified and categorized. The data will then be compared across respondents from

Big 4 and smaller firms to identify any significant differences or commonalities in their

experiences with AI. The combination of literature review and interview data will allow

for a comprehensive understanding of the current state of AI adoption in financial auditing

in Indonesia, while also providing practical insights into the challenges and opportunities

auditors face. The research will adhere to ethical guidelines for conducting interviews,

including obtaining informed consent from all participants, ensuring confidentiality, and

allowing participants the right to withdraw from the study at any time. The findings from

the interviews will be anonymized to protect the identities of the respondents and their

organizations.

This mixed-methods approach, incorporating a literature review and semi-

structured interviews, will provide a comprehensive analysis of AI adoption in financial

auditing in Indonesia. The literature review will offer a global perspective on AI

advancements, while the interviews will provide valuable insights into the practical

challenges and benefits of AI implementation in the Indonesian audit context. The results

of this study will contribute to the broader understanding of AI's potential in emerging

markets, with a focus on the specific factors influencing its adoption in the financial audit

sector in Indonesia.

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Indonesian Journal of Social Technology, Vol. 5, No. 10, October 2024 3885

Results and Discussion

AI Utilization in Financial Audit

The use of Artificial Intelligence (AI) in financial auditing has gained significant

attention in recent years, as the auditing profession experiences rapid technological

advancements. (Bakri et al., 2023). The literature reveals that AI is revolutionizing

traditional audit processes by enhancing efficiency, improving accuracy, and offering

deeper insights into financial data. AI’s ability to process vast datasets, detect anomalies,

automate repetitive tasks, and predict future risks has positioned it as a valuable tool for

auditors, particularly in markets where financial transactions and regulations are

becoming increasingly complex. This section provides a detailed summary of how AI is

being utilized in various aspects of financial auditing. (Juliyani et al., 2024).

Fraud Detection and Anomaly Identification

One of the primary applications of AI in financial audits, as highlighted in the

literature, is its capability to enhance fraud detection and anomaly identification.

Traditional audit techniques often rely on sampling methods, where a subset of financial

transactions is selected for review. This approach, while practical, leaves room for

oversight, as only a fraction of the total transactions are scrutinized. In contrast, AI allows

auditors to conduct full population testing, analyzing all financial data for irregularities,

thus significantly increasing the likelihood of identifying fraudulent activities. Emphasize

that AI systems equipped with machine learning algorithms can detect subtle patterns in

financial records that may indicate fraudulent behavior, such as unusual transaction

patterns, unauthorized access, or irregular accounting entries. This capability is

particularly valuable in large organizations where the volume of transactions can make

manual reviews impractical. AI tools continuously learn from historical data, refining

their fraud detection capabilities over time and becoming more accurate in identifying

emerging fraud schemes. As a result, AI-powered audits provide auditors with a robust

mechanism to enhance the integrity of financial reporting.

Automation of Routine Audit Tasks

Another significant contribution of AI in financial auditing is its ability to automate

repetitive and time-consuming tasks. Tasks such as data entry, transaction reconciliation,

and document verification are integral to the audit process but typically require substantial

manual effort. AI tools have proven effective in automating these routine processes,

thereby freeing up auditors to focus on more complex, analytical, and judgment-intensive

aspects of the audit. AI-driven automation reduces the risk of human error, which can be

prevalent in manual processes, especially when dealing with large volumes of data.

Furthermore, automation enhances the speed and efficiency of audits, allowing auditors

to meet tight deadlines without compromising the quality of their work. For instance,

(Hariyanto & Hidayatullah, 2024) Discuss how AI-powered systems can quickly cross-

check thousands of transactions against set criteria, flagging any inconsistencies or

unusual patterns for further review. This level of automation not only increases the

precision of audits but also reduces the workload for audit teams.

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Enhancing Compliance with Financial Regulations

AI is also playing a critical role in helping auditors navigate the complexities of

financial regulations, which are subject to frequent updates and vary significantly across

different jurisdictions. Compliance with these regulations, such as the International

Financial Reporting Standards (IFRS), is essential for maintaining transparency and

accountability in financial reporting. However, keeping up with regulatory changes can

be challenging, especially for multinational corporations operating in multiple countries.

AI systems can be programmed to automatically update audit procedures to reflect the

latest regulatory changes, ensuring that financial statements are always in compliance

with current standards. This is particularly beneficial in industries where regulations are

stringent and non-compliance can result in hefty fines or reputational damage. (Wilform

Jr, 2023). AI tools can also analyze financial statements for discrepancies that may

indicate non-compliance, alerting auditors to potential issues before they escalate. By

reducing the complexity associated with compliance, AI helps auditors provide more

accurate and timely assessments of a company’s adherence to regulatory requirements.

Predictive Risk Management and Decision Support

AI’s predictive capabilities represent another area where it is making a significant

impact on financial audits. Through machine learning and advanced data analytics, AI

systems can identify trends, predict future risks, and provide auditors with valuable

decision-making insights. This predictive function allows auditors to move from a

reactive approach, where they address issues after they arise, to a proactive approach,

where they can anticipate potential problems before they occur. For instance, AI can

analyze a company’s financial performance over time and predict areas of potential risk,

such as cash flow shortages, debt repayment difficulties, or market volatility. These

predictive insights enable auditors to focus their attention on high-risk areas, ensuring

that audits are more targeted and effective. (Fotache & Bucsă, 2024) Further argue that

AI’s ability to simulate various financial scenarios helps auditors make better-informed

recommendations to management, ultimately leading to more robust risk management

strategies.

Improving Audit Quality and Efficiency

AI’s capacity to handle large datasets and complex analyses significantly improves

both the quality and efficiency of audits. Traditional audit methods are often time-

consuming and may miss critical insights due to the limitations of manual data analysis.

AI, on the other hand, can process massive amounts of structured and unstructured data

at unprecedented speeds, uncovering patterns and relationships that might otherwise go

unnoticed. This capability is particularly important as financial markets become more

complex and interconnected, increasing the volume and diversity of data that auditors

must review. (y Mpofu, 2023) highlight that AI tools can process not only numerical

financial data but also unstructured data sources, such as contracts, emails, and other

documents, to provide a more comprehensive view of a company’s financial health. This

broader scope of data analysis contributes to more accurate audit conclusions and allows

auditors to deliver higher-quality reports. Moreover, AI-driven audits can be conducted

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more efficiently, enabling audit firms to reduce costs and allocate their resources more

effectively.

AI Learning and Adaptation

One of the unique advantages of AI in financial auditing is its ability to learn and

adapt over time. As AI systems are exposed to more data and audit scenarios, they

continuously refine their algorithms, improving their accuracy and relevance. This

adaptive learning process is especially beneficial in dynamic financial environments,

where new types of fraud, regulations, and financial instruments are constantly emerging.

(Yang et al., 2024). As AI systems become more sophisticated, they can provide auditors

with increasingly precise insights, making them indispensable tools for navigating the

complexities of modern financial audits. Yang, Amrollahi, and (Yang et al., 2024) Predict

that future advancements in AI, such as deep learning and natural language processing,

will further expand AI’s capabilities in areas such as analyzing contracts, understanding

financial narratives, and detecting subtle financial irregularities. As AI continues to

evolve, its role in financial auditing is expected to grow, making it an integral part of audit

firms’ operations worldwide.

Current AI Adoption in Indonesia

To gain a comprehensive understanding of how AI is utilized in financial auditing

in Indonesia, interviews were conducted with auditors from both Big 4 audit firms and

smaller, local audit firms. These interviews provided critical insights into the current state

of AI adoption, the challenges faced by auditors in integrating AI into their workflows,

and the prospects of AI implementation in the Indonesian auditing industry.

The interviews revealed that contrary to global trends observed in the literature, the

use of AI in financial audits in Indonesia remains in its infancy. Neither Big 4 firms nor

small audit firms in Indonesia have fully adopted AI tools in their audit processes.

However, there is a clear difference in the approach between larger and smaller firms.

While Big 4 firms are actively working on integrating AI into their audit tools and

processes, smaller firms have not yet begun the development or adoption of AI

technologies due to various constraints. The auditors from Big 4 firms indicated that AI

implementation is currently in the developmental phase. These firms are investing

resources into research and development to create AI-driven audit tools that will enhance

their ability to handle large datasets and perform more sophisticated analyses. Although

AI is not yet fully operational in these firms, there are ongoing pilot projects that aim to

incorporate specific AI features, such as machine learning algorithms for data analysis

and anomaly detection. The auditors expressed optimism about the future, with

expectations that AI will become an integral part of their auditing processes within the

next few years. In contrast, auditors from smaller firms admitted that they have not made

any significant strides toward AI adoption. Due to limited financial resources, technical

expertise, and infrastructure, these firms face substantial barriers to integrating AI into

their operations. While they recognize the potential benefits of AI, such as increased

efficiency and improved audit quality, the cost and complexity of implementing AI tools

have hindered their ability to adopt this technology. As a result, smaller audit firms

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Indonesian Journal of Social Technology, Vol. 5, No. 10, October 2024 3888

continue to rely on traditional manual auditing methods, which are more labor-intensive

and less efficient compared to AI-powered audits.

Development and Integration of AI

The interviews with auditors from Big 4 firms revealed that these global firms are

at the forefront of AI development in Indonesia’s auditing sector. Although they have not

yet fully integrated AI into their auditing processes, several initiatives are underway to

incorporate AI features into existing audit tools. (Rasyid, 2024). These initiatives are

driven by the need to stay competitive in an increasingly digital and data-driven business

environment, where the ability to process vast amounts of financial data quickly and

accurately is crucial. According to the interviewees, Big 4 firms are focusing on

developing AI tools that can automate repetitive tasks, such as data entry, transaction

matching, and account reconciliation. (Fajrillah et al., 2024). These tasks are time-

consuming and prone to human error, making them ideal candidates for AI-driven

automation. By automating these processes, auditors can allocate more time and resources

to high-risk areas of the audit that require professional judgment and analysis.

Furthermore, these firms are exploring the use of AI in anomaly detection, fraud

prevention, and compliance checks, where AI can provide a more comprehensive review

of financial data compared to traditional sampling methods. One key area where AI is

being developed is fraud detection. Auditors from Big 4 firms highlighted that fraud

detection is a high priority in their AI development efforts, as AI’s ability to analyze entire

datasets and detect irregular patterns offers a significant advantage over conventional

auditing techniques. Machine learning algorithms, which can learn from historical data to

identify suspicious transactions, are being integrated into audit tools. These AI-driven

tools can flag anomalies in real time, enabling auditors to investigate potential fraud more

quickly and thoroughly. Another focus of AI development in Big 4 firms is the

enhancement of audit quality through predictive analytics. By using AI to analyze

historical financial data, auditors can identify patterns that may indicate potential risks or

financial misstatements in the future. This proactive approach allows auditors to address

risks before they materialize, improving both the efficiency and effectiveness of the audit

process. The interviewees believe that these predictive tools will play a critical role in the

future of financial auditing, as they shift the focus from reactive auditing to a more

forward-looking, risk-based audit approach.

Despite these advancements, the interviewees also acknowledged several

challenges in fully implementing AI within their firms. These challenges include the high

cost of developing and maintaining AI systems, the need for specialized training for

auditors to effectively use AI tools, and concerns about data security and privacy.

Additionally, they mentioned that the regulatory environment in Indonesia is still catching

up with technological developments, and there is uncertainty about how AI-driven audits

will be viewed by regulatory bodies. Nonetheless, the interviewees expressed confidence

that these challenges will be addressed in the coming years, and AI will eventually

become a core component of their auditing processes.

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In contrast to Big 4 firms, smaller audit firms in Indonesia face significant

challenges in adopting AI technologies. The interviews with auditors from small firms

revealed a consensus that AI, while desirable, is currently beyond their reach due to

several key barriers. The most frequently cited challenge was the high cost of AI

implementation. AI tools, especially those tailored for financial auditing, require

substantial investment in software, hardware, and infrastructure. For small firms with

limited budgets, these costs are prohibitive. Unlike the Big 4 firms, which have the

resources to invest in cutting-edge technologies, smaller firms must prioritize their

expenditures, often focusing on more immediate operational needs rather than long-term

technological advancements.

Another major barrier is the lack of technical expertise. Implementing and

maintaining AI systems requires specialized knowledge in data science, machine

learning, and software development skills that are not typically found within small audit

firms. The auditors interviewed indicated that their firms do not have the personnel or

resources to hire AI specialists, making it difficult to even begin exploring the use of AI

in their audits. This lack of expertise also extends to understanding how AI can be

integrated into existing auditing practices, further limiting the potential for AI adoption.

Smaller firms also expressed concerns about the regulatory and legal implications of

using AI in financial audits. They noted that there is little guidance from Indonesian

regulators on how AI-driven audits should be conducted or how the results should be

evaluated. This uncertainty creates hesitation among small firms, as they fear that reliance

on AI could lead to regulatory challenges or conflicts. Additionally, auditors from these

firms voiced concerns about data security and the risk of breaches, which could be

exacerbated by the use of AI systems that require access to sensitive financial data. While

small firms acknowledge the potential benefits of AI in improving audit efficiency and

accuracy, they emphasize that without external support such as government incentives,

industry partnerships, or affordable AI solutions they are unlikely to adopt AI shortly.

The interviewees suggested that collaborations between larger firms and smaller firms

could help bridge this gap by providing access to AI tools and training for small firm

auditors. Furthermore, they advocated for clearer regulatory guidelines on AI use in

auditing, which could provide the necessary assurance to explore AI technologies.

Future Prospects for AI in Indonesian Auditing

Despite the current lack of widespread AI adoption, the interviewees from both Big

4 and small audit firms expressed optimism about the future of AI in Indonesian auditing.

Auditors from Big 4 firms, in particular, are confident that within the next few years, AI

will be fully integrated into their audit processes. They believe that as AI technology

matures and becomes more affordable, it will become an essential tool for auditors across

firms of all sizes. For smaller firms, while the path to AI adoption is more challenging,

there is hope that as AI tools become more accessible and cost-effective, they too will be

able to leverage these technologies to improve their audit practices. The interviewees

emphasized that government support, industry collaboration, and ongoing education and

training will be critical in helping smaller firms overcome the barriers to AI adoption. In

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Indonesian Journal of Social Technology, Vol. 5, No. 10, October 2024 3890

summary, while AI is not yet fully utilized in financial audits in Indonesia, the interviews

indicate that efforts are underway, particularly in Big 4 firms, to develop and integrate AI

tools into the audit process. However, smaller firms face significant challenges, including

financial constraints, lack of expertise, and regulatory uncertainty, which must be

addressed before AI can be widely adopted in the Indonesian auditing industry.

Conclusion

This research aimed to examine the adoption of Artificial Intelligence (AI) in

financial audits in Indonesia, revealing a significant gap between global trends and local

practices. While AI has demonstrated its potential to enhance audit efficiency, fraud

detection, and data analysis globally, its use in Indonesia remains limited. Big 4 audit

firms are in the early stages of AI integration, whereas smaller firms face barriers such as

high costs, limited expertise, and regulatory uncertainty, preventing adoption. The

findings indicate that AI adoption in Indonesia's audit sector is uneven, with larger firms

leading the way and smaller firms lagging due to resource constraints. To close this gap,

future research should focus on developing accessible AI tools, fostering industry

collaboration, and establishing clear regulatory guidelines. Additionally, targeted training

programs could equip auditors with the skills needed to effectively utilize AI, ensuring

broader adoption and improved audit quality across the industry.

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