Impact of data analytics on reporting quality of forensic audit: a study focus in Malaysian auditors

. The primary objective of this research is to study the impact of data analytics on reporting quality of forensic audit, a study focussed on auditors in Malaysia. In this research, the researcher intends to discover the impact caused by emerging technology, in particular the data analytics, in audit profession, as audit field is the most complex field where tremendous volume of data, and research is conducted to investigate a certain situation, to report findings. The researcher uses the primary method to conduct the data. In this research, four different variables that causes impact to data analytics usage in forensic audit reporting quality, are tested. The data was collected from forensic audit and accounting service providers, operating around Malaysia. To analyse the findings, statistical tool, Statistical Package of the Social Sciences (SPSS) is used, to further analyse the responses, and to derive at a conclusion relating to impact of data analytics to reporting quality of forensic audits. The findings discovered that all the variables have significant relationship with the data analytics and its impact to the forensic audit reporting quality, among forensic auditors and practitioners in Malaysia. The conclusion has been discussed in this research which caters for and provides confidence and data privacy, for users of data analytic tools for their forensic audit work. This research creates the awareness of the use of data analytics in order to assist forensic auditors in their routine data gathering and analysis, in preparing reports, statistics and trend analysis etc, by saving their efforts and time, to focus more on performing further analysis and findings based on case or audit assignment.


Introduction
Technological advancement with emerging technologies of Industry Revolution 4.0 (IR 4.0) received a global call by international accounting bodies, the International Federation of Accountants (IFAC), requiring professions to quickly adopt into the new platform to stay relevant. The initiatives and efforts taken to embrace, adopt and implement technologies validating their opportunities to research on better options to manage, and data analytics comes in as most useful assistance for external auditors. "Analytics" is defined as systematic computational analysis of data, by the OXFORD dictionary.
Data analytics plays primary role to give confidence and support to assist auditors to improve their service quality and making sure to allocate added value to clients. To understand client operations, and to identify various risk factors in business environment, data analytics allows auditors to perform thorough extract and analysis on large data sets.
ICAEW has supported the fact that audit firms should now focus on developing the capabilities of human capital since audit will be performed significantly different from the past which results in the need of higher IT proficiency by the auditors (ICAEW, 2018). When auditors find technology to be convenient in application on daily tasks, they would move on to improving forecasting and analysis, and increasing fraud detection through audit testing as well (ACCA, 2019). Past studies on task-technology fit between auditor's knowledge and perceived importance on technology revealed a gap. In Malaysia (Ismail & Abidin, 2009) and in Kuwait (Al-Duwaila & Al-Mutairi, 2017), auditors' knowledge on general office automation, accounting firm office automation, audit automation, e-commerce technology and systems design and implementation were found to be lesser than their perceived importance. Comparatively, Big Four auditors demonstrated a higher likelihood of technology usage (Al-Duwaila & Al-Mutairi, 2017) The following table shows data analytics and its specific benefits provided for auditors and their services.
Within data analytics, there are various emerging approaches which provides specific assistance and features to serve audit processes. Some of the approaches, deep learning, predictive analytics, text mining, smart contracts via blockchain. are discussed below. (CPA Journal, 2022). First approach is the predictive analytics. This approach gives auditors an ease of time management, by offering highly validated and accurate accounting results. By having such results, auditors can be at ease in selecting their materiality limits to audit tests, can trust on management assertions on a high note, and have higher confidence in the data audited. Second is the deep Learning, also called as "cognitive computing", consist of automation and human interpretation blended. This approach of data analytics utilizes the basic audit worksheet analysis, for example estimations to bad debts, abnormal contract analysis, lease classifications etc to turn it into advanced methods as neural networks, to analyse the situation in much deeper and multiple layers insights. As its unique feature to perform ground search engines, auditors are investing higher finances towards these technologies. It uses storage and power on a big scale, many businesses outsource this deep leaning project to experts and research hubs, to handle their data with high quality, and care. IBM Watson is one famous service provider. Third is called the Blockchain / Smart Contracts. Blockchain became more famous with the introduction of the virtual currency bitcoin. The features of blockchain supported the bitcoin trading, as it facilitates to store data public and replicate transactions using encryption methods. Specific feature in blockchain is the smart contracts. With the combined technology, blockchain and smart contracts manages automated data management independently, without human assistance. As an example, when errors larger than the materiality limit is detected, blockchain sends a signal of red flag to auditors to perform extensive testing. Last approach is the text mining, also called as text analytics. This is another AI which use natural language processing (NLP) to turn unstructured data text into structured forms and provides a meaningful text for analysis purpose. Combining all the unique features of big data, unstructured to structured, provides greater support for management in terms of validating data quality, and providing trusted information for auditor's work. Initially, before the spectrum of Data Analytics began, basic automation was a hypewhere rule-based automation was used. Then, business process automation was practiced and today Robotic Process Automation (RPA) is used to inaugurate the beginning of Data Analytics in enterprise (Vaidyanathan, 2018).
In order to drive business strategy and performances, and to manage enterprise risk assessments, and to achieve success, Data Analytics assists in smoothing the process, by providing range of solutions are approaches to data. (Deloitte, 2022).
The true nature and use of data analytics begin with assisted intelligence and then intelligence process automation when RPA is combined with DATA ANALYTICS. Then, according to Zaani, Rios and Sampanthar (2018), DATA ANALYTICS evolves to being augmented intelligence and the future of DATA ANALYTICS lies in autonomous and algorithmic intelligence.
Today, the developments of data analytics reach at augmented intelligence where it influences the abilities of Machine Learning (ML) for the growth of human analytical competencies and will soon be completely integrated into operations grant full authority to automate processes through a combination of influential machines, bots, and schemes (Vaidyanathan, 2018).

The Data Analytics Outlook in Forensic Audit Reporting
In general, DATA ANALYTICS is often used as an over-arching term, preached as an advanced computing capability with machines being able to think for themselves. However, auditors' at large, views data analytics as machine experts which carries abilities to make decisions on thinking, learning, understanding, reasoning, and perception. (Pan et al., 2017).
As viewed by auditors, upsurge in data analytics is not necessarily signify an ultimate change in our routines. As fundamentals, financials for business deals on data, where auditors use data to compile and analysing data, and providing conclusions on it, since the beginning of our profession. While the concept is ancient, it is unprecedented, on the volume of data handled by data analytics, the data analysing speed, & transformed.
In its recently produced report by KPMG, related to topic on Audit 2020 Ä focus of Change", discovered that 53% of respondent's executives views data analytics as a transformer in audit industry, where audit quality and effectiveness is enhanced. The scope on financial information available for testing is increase with the use of automated routine transaction. Also, the special feature of data analytics which covers deeper, and larger sets of data is very helpful tool for accountants and auditors. Additionally, the exposures and expertise held by auditors on data analytics allows them to review on risk assessments and audit procedures in a more advanced way. Moreover, enhancement to data analytics provides better quality on audit discussions and gives an opportunity for auditors to collect deeper insights on company's operations. Influential visualisation tools within data analytics, helps to deliver the data history, and provides basic assistance to evaluate relationships and to gain real time details to flows of transactions.
One way for auditor to detect high risk transactions on a wider landscape, within a shorter timeframe, is by directly extract effective data from financial systems of a company, analysing the data with external and internal data from third parties, using data analytics proficiencies. (Roger O'Donnell, KPMG LLP, 2017).
Past audit related research in IT was focussed on specific areas of assurance and IT audit. On recent findings, it was found that IT exposure and knowledge are fundamental requirement in IT auditing platform. ( (Messier et al., 2004); developing information system metrics, and identifying risk modules. (Sherer and Paul, 1993;Havelka et al., 1998;Salterio, 1998;Stockton, 1998).
Further research were done on attributes which influence quality of audit, but not much was done to rationalize and consolidate the factors into system of IT review quality, Merhout and Havelka did a discovery on developing IT review process theory, utilizing techniques of bulk data gathering, with the assistance of practitioners from IT audit, external and internal users, to build a framework. Havelka and Merhout, 2007;Merhout and Havelka, 2008;Havelka and Merhout, 2009). In their research, they distinguished huge dataset characteristics (more than 260 -alluded to as variables by Merhout and Havelka), that were proposed by experts as "basic" to the IT review process.

Data Analytic creates opportunity
Data Analytics advances accountant's intellectual and assist in drawing precise and decisions in routine operations, and this assist in accomplishing objectives but also to resolve unexpected issues of real world. (Greenman, 2017 and Baldwin, Brown and Trinkle, 2006). Forensic auditors job requires high risk evaluation, analysis and findings relating to fraud and criminal events. (Glover, Prawitt, & Drake, 2014;Yoon, Hoogduin, & Zhang, 2015). They need to carefully plan crucial designing and implementing procedures to help them detect the unlawful activities. Accuracy of information need to be taken care to draw right conclusions. (Appelbaum et al., 2017). With the evolving technology, Data analytics provides greater opportunity to forensic auditors, by offering new tools to analyse data, allowing it to excavate into bigger, non-traditional data sets and execute more intricate analysis. (PwC, 2015). Forensic auditors can perform their jobs with high quality and effectively, to produce quality audit reports, as they can better understand business environments, and reduce the risk of drawing incorrect conclusion, hence improves audit quality. (Dagilienė & Klovienė, 2019). Audit Data Analytics characterizes the "analysis of data underlying financial statements, together with related financial or non-financial information, to identify potential misstatements or risks of material misstatement" (Stewart, 2015, p. 108).
Christine E. Earley, 2015, defined 4 four key advantages of DA usage, in audit, where auditors can test a bigger set of numbers compared to manual techniques, and compile audit evidence easily, with the application of risk-based models, and sampling techniques via the data analytic tools. Second advantage relates to audit quality which can be improved by offering deeper findings and insights to client's processes. Third advantage is where detection of fraud and error can be done easily as auditors are able to leverage the tools and E3S Web of Conferences 389, 09033 (2023) https://doi.org/10.1051/e3sconf/202338909033 UESF-2023 technologies used. Lastly, it allows auditors to provide services and resolve issues smoothly for clients, Thus, we can propose the first hypothesis based on the statement above to test the first purpose of this research is as follows: H1: There is significant relationship between Data Analytics as opportunity creator for forensic audit reporting quality

Influence of Data Analytics on Audit Reporting Quality
Obviously, Data Analytics which creates a greater opportunity to forensic accountants, may have a concern on its influences too to the reporting quality. Forensic accountants' role is classified as very unique role and profession, and the data analytic influence can be based on the accountants skill and level of expertise in their field of work. (Yaninen, 2018). An obvious possible challenge in Data Analytics usage can be shortage of expertise in handling the digitalized tool. This poses a greater challenge to education providers to incorporate such skills in their curricular system. Another challenge seen data analytic usage and influence is in the danger of data lost, due to cyber-attack, or while processing data. Influence of data analytics can be on data sanity. The sources of information gathered by Data analytic tools, need to be re-examined by human analytical intelligence. (Appelbaum, 2016). A vast amount of data created by Data Analytics may not necessarily helpful to provide quality reporting. . Using the socially linked platforms when collecting and analysing information involves costs, which may influence on companies' budgets and fund allocation. (Yoon et al., 2015).
Under the audit, the main requirement of planning is for auditors to gather financial information of client, to "diagnose" the financial health and sustainability growth of a firm. (Lehmann et al., 2006). This process involves tedious tasks, where auditor need to connect their examination techniques to various sources to gather data. Past studies discovered that auditors judgement is closely dependant on the type of evidences collected, for a going concern matter to be reported in planning stage. (Tsao, G. (2021).
Thus, we can propose the second hypothesis based on the statement above to test the second purpose of this research is as follows: H2: There is level of influence on data integrity using Data Analytics on forensic audit reporting quality.

Risk managed by data analytics on reporting quality of audit
Relatively few individuals comprehend that information examination is significant in risk the executive's control and methodology. Individuals who don't have business foundation will not comprehend that the right information can work on an organization in the greatest manner, while some unacceptable information can send everything in twisting and mayhem. In the present worldwide stage, where everything depends such a great amount on information, it is significant that information logical and the board can be advanced and figured out proficiently. (Risk Management Article, 2022).
As a matter of first importance, the right information can lead you to settling on choices of how to manage your significant data -which can prompt better network safety and assurance, as well as the preventive techniques for how to manage the potential dangers. Second, the information you have can lead you to make more astute and more innovative routes in alleviation procedures as well as better business systems. Third, the information can assist you with working on your business in the most proficient ways, without you stressing over more serious dangers as all that has been limited and arranged out cautiously. (Risk  Management Article, 2022) A review led by an analyst, Rozarion,A. M., and Issa, H. (2020), inspects the use of information investigation in the public authority area. Accordingly, it adds to the public authority bookkeeping writing by proposing a gamble-based structure to work on the proficiency and viability of reviews of government consumptions. In addition, the utilization of the proposed prioritization approach can possibly moderate the issue of data over-burden and low handling familiarity that openness to huge datasets can make. At long last, this study recommends a few promising examination roads that future investigations can investigate.
Thus, we can propose the third hypothesis based on the statement above to test the second purpose of this research is as follows: H3: There is significant relationship on risk managed by Data Analytics in reporting quality of forensic audit.

Credibility of independent data access and extraction using data analytics on reporting quality of audit
Many opinions nowadays are targeted at the doubt of in-secured feelings crop up in human thoughts, on how much does the technology take over on human roles. There are few opinions from researchers on this matter. The AI and Data Analytics are growing super quick, without a need for rest unlike human. This poses greatest advantage to the digitalization, to replace human efforts. Additionally, the data analytics possess learning and teaching attributes too, replacing human, causing unemployment in larger scale and income effect in the population. (Shukla and Jaiswal, 2013). Thus, with progression in technological supercomputer and artistic inventions of processes towards Data Analytics and forensic accountants' profession, is expected to vanish. Machine and deep learning are greatly dependent on proficiency in data inset into AI, so AI is a great threat to forensic accountants' skills. (Wisskirchen, 2017 and Miranda and Aldea, 2016).
A study by New Vantage Group (2012) observed that organizations were more stressed over the unstructured idea of information instead of the volume of information. Zhao et al., 2014) recommended that organizations should manage difficulties relating to the incorporation of inner (e.g., conditional records) and outer information (e.g., informal organization information). Obviously, new innovation is expected to address new difficulties brought about by attributes of enormous information; nonetheless, large data specific innovation has advanced colossally over the most recent couple of years. (R.L. Mitchell, 2014). While we are sure that large information explicit innovation will keep on advancing, it is the ideal opportunity for associations to zero in on different assets, other than innovation, which are expected to construct firm-explicit "difficult to impersonate" BDA capacity. Thus, we can propose the fourth hypothesis based on the statement above to test the second purpose of this research is as follows: H4: There is significant credibility of independent data access and extraction using Data Analytics, in reporting quality of Forensic Audit

Methodology/Materials
This research employs positivism philosophy as the basic foundation for this study. The approach of this research is deductive. Using the deductive approach, the hypotheses and theory were developed. This study uses a survey strategy to obtain relevant data from respondents to test the hypotheses. Furthermore, this research implements the mono method which is a quantitative study as this research involves collecting and assessing numerical data. Cross-sectional research will be employed to gather primary data by constructing a selfadministered questionnaire (SAQ). The SAQ will be created via Google Forms and distributed through links and emails to the respondents. Likert scale were used ranging from Scale 1-Completely disagree, 2-Disagree, 3-Neutral, 4-Agree, 5-Completely agree. Lastly, data collection will be conducted among commercial banks employees and relevant data analysis will be implemented by using various statistical techniques to analyze the numerical data. The Table 1 illustrate the frequency and mean for the agreement of respondents on the dependent variable which reporting quality of forensic audit. There consists of 5 statements for the dependent variable which emphasise on the current difficulties faced in reporting quality of forensic audit. Based on the Table 4.2.1, the highest mean value is 4.63 for S1,S2 and S5 and the lowest is 4.54 for S3. It means most of the respondents agrees on the requirement of high reporting quality on forensic audit needs. The second highest of mean is S4 which is 4.59 that shows the data analytic tools are important for forensic auditors. The lower mean is at 4.54 which indicate the data analytics do significantly assist in data analysis and to assist auditors draw proper audit conclusions.  Table 2 show the frequency and mean for the respondents' opinion on the independent variable which is opportunities created by data analytics. 5 statements are prepared for this independent variable to researcher to understand the knowledge of respondents on the data opportunities created to forensic auditors. The highest mean of 4.57 is S1 where Data Analytics allows auditors to build descriptive, prescriptive, and predictive analytics models to gather answers and evidence relating to key investigation areas. This has been strongly agreed by most the respondents. Followed by next mostly supported statement, S5 "The results of analytics performed gives an opportunity for auditors to presented to the management of an entity which may not be involved in the daily operations or its finance function" which scored a mean of 4.45. Other areas S2, S3 and S4 ranges within means 4.51 and 4.50, which shows still holding strong support from the respondents. Their responses clearly show their awareness on Data Analytics and its benefits to forensic accountant's service.   Table 3 shows the level of data integrity using data analytics. The most significant statement is S5 that says data integrity allows key area of audit risk and identifying likely warnings through breaking down examples and connections between different arrangements of information in a client's business. This finding scored the highest mean of 4.56. S1 on Data analytics software makes it simple to examine and remove information from numerous sources so inspectors can run custom fitted investigations productively and really is rated second with mean of 4.54 which differs by 0.02 from S5. Third ranking is based on S3 which says it is additionally considered normal to make visual showcases of the information in which anomalies and exemptions can be all the more handily recognized when contrasted with a mathematical show of information, is rated at mean of 4.51. The rest of the S4 and S2 recorded means of 4.50 and 4.44 respectively, where respondents show strong support in terms of data integrity using data analytics. The Table 4 illustrate on risks associated with data analytics and means of respondent's agreements on dependant variable of reporting quality of forensic audit. Under this area, there were 5 sub statements tested. The highest mean value is 4.54 for S5 and the lowest is 4.47 for S4. It means most of the respondents were supportive on the risks associated with data analytics as important factor for dependant variable of study. The second highest of mean is S1 which is 4.53 that shows that under the risk-based auditing, risk associated with a particular forensic investigation can be tested with a greater focus, using Data Analytic tools. Table 5. The credibility of independent data access and Extraction using Data Analytics. The Table 5 represents the frequency and mean for the respondents results on dependent variable which is reporting quality of forensic audit. There consists of 5 statements for the dependent variable which emphasise on credibility of independent data access and extraction using Data Analytics Based on the Table 4.2.5, the highest mean value is 4.57 for S5 and the lowest is 4.47 for S4. It means most of the respondents agree that by using data analytics, as forensic auditor, we infer that an improvement of seen believability of supportability reports is conceivable with assistance of big data analytics. The second highest of mean is S3 which is 4.54 that shows Data Analytic tool offers secured IT environment to access data independently, and ensures data is always protected. The moderate agreement for the statement of the credibility of data analytics usage are S1 and S3 with mean of 4.26.

Multiple regression
In the testing of developed hypothesis of this research, multiple regression analysis is performed to measure if the independent variable in individual can affect the dependant variable when other variables are held constant. R refers to the coefficient of multiple correlation ranging between -1 to +1 while R Square refers to the variability in outcome by the predictors ranging between 0 to 1. Table 5.2(a) shows that the R Square is 0.707 which indicates 70.7% variation in the DV can be explained by the IVs in this research which are opportunity, data integrity, risk managed and credibility of data access, whereas the remaining 29.3% is explained by other variables.  As per Field (2008), the importance level of assessing the outcome can be tried through Analysis of Variance (ANOVA). It is capable in comparing samples relating to numerical dependant variables and to determine if the results are explained precisely. In view of Dallal (2012), the Regression Sum of Squares in ANOVA is the error between Total Sum of Squares and Residual Sum of Squares. Other than that, the Total Sum of Squares alludes to the amount of fluctuation sum in the reaction and Residual Sum of Squares that not ready to be considered after the relapse model is taken on while the Regression Sum of Squares alludes to the changeability sum in the reaction that is considered by the regression model. The df is address the levels of opportunity which is the quantity of independent variables. The df is determined by taking away 1 from the quantity of factors (df = n-1) (Statistic How To, 2018). Additionally, F proportion is utilized to portray the changes between the variables. The significance level (p-value) of ANOVA ought to be not exactly or equivalent to 5% (p-value ≤ 0.05) which shows that the connection between two variables is significant and the null hypothesis should be rejected (Minitab Inc, 2016). Table 5.2 (b) shows the df is 4 (df = 5-1 = 4) which means the degree of freedom generated by five variables involve opportunities created, data integrity, risk managed, credibility of data and reporting quality of forensic audit. In addition, the residual of degree of freedom is in accordance to the sample size for responses received which is 68. From this 68 of sample size, the 5 variables are deducted from the sample size to obtain 63. Next, the aggregate of 5 for degree of freedom calculation and 63 produce the outcome of 68 for the total of degree of freedom. Hence, it can be concluded that when the sample size increases, the degree of freedom, df also increases.
Furthermore, the F-ratio which presented in Table 5.2 (b) is 37.929 which is calculated by using the Regression of Mean Square divides with the Residual of Mean Square (2.360/.062=37.929). Besides that, based on Table 5.2 (b), it indicates the significance level is <0.001 which is lower than 0.05, so that the relationship between the dependent variable and independent variables which included opportunities created, data integrity, risk managed, credibility of data and reporting quality of forensic audit is significant among forensic auditors in Malaysia. Hence, the null hypothesis should be rejected.

Conclusion
As this study focussed on impact of data analytics to reporting quality of forensic audit, the research was dedicated on four independent variables which involves opportunity creation via data analytics, data integrity, risk management and credibility on data extraction. These independent variables are not sufficient to evaluate the overall radius on data analytics, as firstly data analytics is fast evolving with newer updates and technologies, and secondly, there are still wide room for detailed and more insightful research can be carried out to improve the findings for future researchers. On the demography population, more range of professionals and industries can be added into the research to provide more concrete findings and responses on data analytics, to look for more itemized data to improve the topic of the exploration. Literature reviews can be expanded more into significant and relevant areas and sub-areas in digital evolving economy, which is suggested for future study. In terms of sample and population sizes, can be increase too, to gauge more accurate and real time results for the study.
In this study, 68 respondents gave their views and feedbacks into the study, is less adequate coverage for Malaysia. Recommendation to future researchers to boost the respondent's size, to cover more areas, and collect more views to reach an exhaustive determination and present the exact characterized information in the review. Additionally, the study was focussed on impact of data analytics on a specific field of industry, suggestion is to expand the coverage to cater for more fields globally.
On the data analysis tools, the statistical analysis software used in this research is SPSS. The future analysts are proposed to endeavour other programming to direct the information examination to give more dependable and huge outcomes for this review. Along these lines, it ready to make the more noteworthy mindfulness on impact on data analytics on reporting quality of forensic audit.
Data analytics are very important tool to assist real time business operators, to provide quality and realistic data reflection about their business and future. The benefits of data analytics usage offer tremendous improvement and growth, with stronger sustainability for business to strive through future. This gives confidence to auditors, especially in forensic field, to perform their investigations and findings to confirm reliability of financials of their customers. Not only improves the integrity and relationship between customers and forensic audit service providers, but it also gives a trust and satisfactions to stakeholders to invest and continue to support businesses to grow. It can be concluded that Data Analytics are strongly recommended for businesses, and for professional accountants and audit fields, to continue to adopt to the technology to have improved future.

Recommendation
The forensic accountants are a home of experts that is supposed to show an excited reaction towards the advancing savvy and computerized innovation; proceeded with globalization of detailing and exposure norms; and the new types of guidelines that accompany AI. This is on the grounds that forensic accountants have forever been a calling that plans to work on the nature of business and venture choices drastically. To understand this potential, the calling needs to zero in on basic business issues it intends to tackle and consider upon how advances like AI can increase their methodologies. The following recommendations are for forensic accountants, to guide them in diverting their privilege towards making advanced progress, which is basically acquiring a harmony among disruption and business.
Firstly, to have advanced technique planning -includes distinguishing, figuring out and carrying out smart arrangement that directs each decision-production to remember contemplation for how innovation could further develop the navigation of forensic analysis and reporting. This is basic as it doesn't just test yet upgrade the digital maturity of the profession -thereby improving digital capability development.
Second, pilot project creation -includes in doling out projects for model to assist forensic accountants to analyse and find what works best in analysing and reporting business reports.
Third, to harness the right skillsets in using data analytics -forensic accountants are required to equip themselves with the required skills on technology application, in prioritizing the tech-capabilities needed for organization, people, process and technology.
Fourth, turning into an information virtuoso -features that forensic accountants is an information escalated calling, thus is supposed to can distinguish and accumulate the right information, send it for the right reason and actually investigate it. This should be possible by zeroing in on prescient examination and estimating, prescriptive investigation, business driven independent direction and mechanized criticism to the association.
Fifth, technological enterprise development -in this, forensic accountants and organisations who provide the services should endure on transforming towards the digital culture which preserves talent, demonstrate strong and clear leadership skills, involvement and ownership in discovering a balance between data analytics and businesses.
Additionally, is based on biological system arranging -features that since disturbance is an environment peculiarity, rather than zeroing in on even and vertical mix inside association, the forensic accountants should shift their focus into broader prospective in supporting client needs and use digital technologies to create and deliver value to client in an integrated, innovative way.
As emerging technology is a wide subject and there is no sureness and consistency in the degree of reception in each firm, future examination should be possible to concentrate on a how evaluators in a comparative review firm see a specific innovation, for example, AI or RPA. Cooperation should be possible with a particular firm to get worker interest cross country so a higher reaction rate could be accomplished. This would permit the firm in intended for acknowledge how the workers are embracing AI in their work scope.
This study which reveals significance of timing in the usage of emerging technology could also be further researched to find out if training, for instance, can be improved to facilitate auditors' adoption. The level of training that firms provide to auditors in handling emerging technology systems to detect and prevent fraud and error, can be incorporated into audit testing and as research area because updates on accounting standards and policies or ways to complete procedures for specific account balances will often be emphasized but rarely looking from the IT proficiency viewpoint.

Limitation of the Study
There are a few restrictions conveyed by this research which limits the validity of the findings. The participants of this research were moderately minority population, which can't be summed up as the view of all Malaysian Forensic Accounting bodies and service providers. Even though this research were confined to most forensic service providers, their responses might not have been balanced between firms, therefore, there are possibility of over representation of one firm to another. Major forensic service providers could likewise give socially advantageous reactions to safeguard their profession and organisations they service. Furthermore, each firm would shift in the gathering level of arising innovation in review testing which would bring about irregularity in answering to the questionnaire. Furthermore, the usage of emerging technologies used to be an optional element for organisations. There are additionally numerous different factors fit to impact misrepresentation identification yet are not tried in this examination.