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Posted on Apr 19, 2022

Write a paper on the following topic. The topic is The Implication of Machine Leaning in Auditing. Some important things to know about your research

Write a paper on the following topic.

The topic is The Implication of Machine Leaning in Auditing.

Some important things to know about your research paper:

1. have a good structure and organization

2. provide evidence to support your conclusions

3. Try to focus on current issues. For example, Sarbanes–Oxley Act (SOX) is an 18-year-old act, and if you talk about why we need this act is an old story. The focus should be the new development of this act in recent years

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Abstract This research explores the development of the machine learning process as a method of data analysis that automates the analytical model generation The complexity inclined to the audit processing mechanism guarantees significant need analysis based on integrating sustainable operating models across an organizational setting The integration of machine learning in the auditing profession fosters an artificial intelligence concept that configures idea generation mechanisms and integrates the learning processes incorporated from the underlying data analysis concept Sutton et al 2016 The auditing practice administers suitability parameters configured into fostering the current data proliferation parameters The significance outlined on the internet determines the intensity of the machine learning concept The SarbanesOxley Act SOX incorporation outlines futuristic enforcement of the automation concept fostered in the research model The recent developments in the auditing practice enhance suitability parameters gauging the current operating structures The implementation of machine learning in the auditing practice incorporates the applicability of algorithms and the enforcement of an analytical model assessing the monetary and nonmonetary parameters rather than the integration of a representative sampling model Brennan et al 2017 The analytical features determine the ability of auditors to assess an entire data processing system The research thus aims at determining a cohesive mechanism through which machine learning automates the data proliferation mechanism and enhances an efficient auditing process by fostering quality in data extraction Keywords Audit Machine Learning SOX internal controls Integration The Implication of Machine Learning in Auditing Introduction Machine learning provides an opportune moment for crucial improvements in audit speed and quality The machine learning parameters facilitate a sufficient means through which technology presents suitable transitioning in the audit processes at an organizational level According to Kokina and Davenport 2017 machine learning incorporates an artificial intelligence subset in configuring machines to operate as humans Machine learning has formed an integral part of the current auditing process following the integration of automated systems in the accounting profession The development of fundamental accounting processes determine the effectiveness within which machine learning advances data proliferation concepts The current auditing procedures incorporate machine learning in integrating email filtering procedures credit software tracking targeted advertising and news feeds The advancement of an auditing process has significantly conformed into the machine learning parameters to foster crucial technological components in improving audit quality Raphael 2017 Machine learning algorithms are essential in providing organizations with opportunities to review the auditing practice Once audit teams encompass the data population the practitioners can professionally perform appropriate tests Artificial intelligence conducts repetitive processes and provides efficiencies in delivering and enabling auditors to incorporate their skills knowledge and professional judgment SarbanesOxley Act SOX versus Machine Learning in Auditing The SarbanesOxley Act SOX of 2002 is a federal law launched in the auditing profession to determine monetary regulations for public companies The lawmakers established a legislation process seeking to protect the staff members and the public from auditing errors and dishonest practices According to Ndreca and Dibra 2017 the SarbanesOxley Act is conferred upon cracking down the corporate fraud and foster complementary monitoring of the accounting practices in the industry The act emerged in response to the highly publicized corporate monetary scandals in the organizational setting The act established stricter regulations and policies for auditing and accounting professionals The SarbanesOxley Act SOX covers multiple risks seeking to support the auditing execution system The system guarantees a meaningful part of ascertaining an entitylevel measurement determined by highlevel control parameters The empirical determination of auditing processes is inclined to the SarbanesOxley Act in assessing the underlying accounting channels in the corporate setting Ndreca Dibra 2017 SOX requires inferential testing to determine and provide reasonable assurance regarding the monetary assessment guaranteed by compliance models The act develops a suitable checklist used to evaluate the underlying compliance models by auditing assessment regulations Integrating viable channels confers a suitable model to facilitate a cohesive information technology model and determine absolute security control models in an organizational setting Machine learning provides the potential for administering significant improvement in audit speed and quality According to Raphael 2017 machine learning process guarantees a coherent embodiment where the mechanized systems determine the dynamism embedded in integrating the audit processes The rise of the internet has generated a profound need to administer digitalized mechanisms embodying the transfer of data applications in machine learning systems Data integration has positioned machine learning to become a crucial component of the current audit processes According to Bardelli et al 2020 machine learning is a determinate of knowledge extraction models The knowledge extraction concept is crucial for electronic invoice classification as shown in the Figure below Figure 1 Electronic Invoice Classification Bardelli et al 2020 The inclusivity measures derived through monitoring software guarantee a meaningful process of administering the targeted auditing procedures in the corporate setting Machine learning has disrupted the conventional systems and guaranteed a structural inclination to the auditing profession The system guarantees a meaningful conceptual analysis describing the current undertakings in the corporate setting and determining the underlying measures described in facilitating a cohesive auditing profession The machine learning process integrates the historical embodiment of information and generates cohesive parameters for assessing data viability The process ensures meaningful data exploration process and harnesses the existing models to highlighting absolute pattern identification measurement Minimal human intervention is involved in the data management process since the viability of information is guaranteed based on a cohesive measurement of the machine learning tools According to Kokina and Davenport 2017 the machine learning algorithms are designed as empirical techniques used to estimate the underlying target functionality in the data processing The algorithms further determine a profound output variability measurement designed per the underlying differential patterns Various algorithms are embedded to develop suitable assumptions in determining the functionality of the data under review The algorithms develop sustainable parameters for determining the linear and nonlinear variables in the data analysis The machine learning algorithms foster a determinate assumption to integrate the data output into the required auditing procedures According to Kokina and Davenport 2017 the enforcement of a particular machine learning problem involves a coherent assessment of the underlying output models designed per the responsive outputs in the data variables The automated systems ability to foster a responsive data outlay with minimal explicitly programmed models fosters a cohesive need to deliver sustainable output levels in the auditing process The development of computer programs fosters meaningful access to data suitable for a responsive auditing profession The main elements administered in the data learning curve are enforced following the integration of measurable concepts in the corporate setting Application of Machine Learning in Auditing Machine learning involves a determinate process of utilizing the underlying algorithms in assessing detailed data The process is suitable for assessing the entire monetary and nonmonetary data rather than incorporating representative sampling Brennan et al 2017 The underlying representation models foster a meaningful process of determining the underlying data variability across numerous data sets This process guarantees a suitable data integrity measurement which fosters responsive analytical modeling in the auditing process Also instead of relying on the representative sampling methodologies incorporating the machine learning algorithms provide entities with suitable options for reviewing a whole population and evaluating any underlying faults in the review processes The initialization of specific algorithms helps determine the viability of datasets and outlines the significance administered in the formulation process Audit professionals can significantly confer absolute data tests while relying on an entire population and determine the empirical measures in integrating the data sets Auditors work on an entire financial data while incorporating machine learning parameters This process guarantees a meaningful process of reviewing and evaluating the underlying shortfalls in the auditing process The empirical review integrated into the data processing mechanisms fosters a cohesive process of assessing the extent to which the data derivatives are administered in the machine learning phenomenon Brennan et al 2017 The empirical integration of viable datasets help configure the underlying measurements seeking to outline the viability parameters across the profession The auditors can integrate the entire data set and foster sustainable financial models to integrate the variability parameters Any underlying anomalies in the data review process are categorized based on their intensity and measurable models The empirical facilitation of sustainability schemes forms subjective modeling guaranteeing the assessment parameters in the auditing practice The algorithms derived through machine learning are automatically administered following the conclusions and opinions attached to certain accounting procedures According to Raphael 2017 the algorithms culminate into similar logic parameters conforming into critical items containing similar features The embodiment of the accounting parameters results in sustainable models conferred through the machine learning systems The machine learning concepts guarantee meaningful facilitation of the auditors procedural guidelines to administer the current inclusivity parameters in the data development model Most of the current auditing procedures contain a recurrent parameter which makes them suitable for the application formulations The empirical processes are integrated per the auditing concepts in fostering a recurrent process facilitated through the various transactional accounts in the business model Most of the accounting firms have made numerous investments fostered through the integration of machine learning systems The current operational parameters are determined by fostering suitable accounting systems across an organizational setting Incorporating optimal machine learning concepts fosters a cohesive modeling scheme suitable for the current auditing parameters According to Gordon 2021 the machine learning processes are also administered across the outsourcing procedures to determine the current operational output in the accounting systems The current application of machine learning fosters meaningful procedural guidelines seeking to outlay the viable parameters in the auditing process The machine learning parameters are outlined per the measurable models highlighting the current operational concepts and encompasses a meaningful growth of absolute channels in the auditing process Importance of Machine Learning in Auditing Integrating machine learning in the auditing process is important since the entire data is examined According to Kokina and Davenport 2017 fundamental data analysis is crucial for determining the viability parameters in the auditing process Auditors who involve the machine learning concept do not have to use conventional sampling techniques prone to inaccuracy and unreliability Administering subjective data derived through the machine learning parameters configures a defined necessity facilitating the cohesiveness of data integrity in the auditing process The integration of machine learning in the auditing process guarantees a meaningful data analysis embodied by enforcing entire informational models The auditors thus have an opportune moment in determining the anomalies and shortfalls inclined to an entire dataset rather than integrating segments of the data Reliable data confers a meaningful processing mechanism guided by the machine learning concept in facilitating the output parameters The process of delivering sustainable data processing procedures guarantees cohesive accuracy which enabled the auditors to deliver timely auditing reports Integrating machine learning in the auditing process guarantees a speedy audit process ... blur-text-image