Emerging IT Technologies for Accounting and Auditing Practice

2020 ◽  
Vol 18 (160) ◽  
pp. 731-751
Author(s):  
Lavinia Mihaela CRISTEA ◽  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Data Analytics ◽  
Research Method ◽  
New Technologies ◽  
Predictive Analytics ◽  
Interesting Case ◽  
Prescriptive Analytics ◽  
Financial Audit ◽  
Big Four

The IT impact can be noticed in all activity fields of this world, and the audit is no exception from the evolution of this technological trend. Motivation: Given that professionals are progressively pursuing experimentation in working with new technologies, the development of Artificial Intelligence (AI), Blockchain, RPA, Machine Learning through the Deep Learning subset is a particularly interesting case, on which the researcher argues for debate. The objective of the article is to present the latest episode of the new technologies impact that outline the auditor profession, the methods and tools used. The quantitative, applied and technical research method allows the analysis of the emerging technologies impact, completing a previous specialized paper of the same author. The results of this paper propose the integration of AI, Blockchain, RPA, Deep Learning and predictive analytics in financial audit missions. The projections resulted from discussions with auditing and IT specialists from Big Four companies show how the technologies presented in this paper could be applied on concrete cases, facilitating current tasks. Machine Learning and Deep Learning would allow a development for prescriptive analytics, revolutionizing the data analytics process. Both the analysis of the literature and the conducted interviews admit AI as a business solution that contributes to the data analytics in an intelligent way, providing a foundation for the development of RPA.

Robust Data-Driven Machine-Learning Models for Subsurface Applications: Are We There Yet?

2021 ◽  
Vol 73 (03) ◽  
pp. 25-30
Author(s):  
Srikanta Mishra ◽  
Jared Schuetter ◽  
Akhil Datta-Gupta ◽  
Grant Bromhal
Keyword(s):  
Machine Learning ◽  
Data Analytics ◽  
Oil And Gas ◽  
Model Building ◽  
Model Updating ◽  
Predictive Analytics ◽  
Energy Resources ◽  
Data Driven ◽  
Classical Statistics ◽  
Prescriptive Analytics

Algorithms are taking over the world, or so we are led to believe, given their growing pervasiveness in multiple fields of human endeavor such as consumer marketing, finance, design and manufacturing, health care, politics, sports, etc. The focus of this article is to examine where things stand in regard to the application of these techniques for managing subsurface energy resources in domains such as conventional and unconventional oil and gas, geologic carbon sequestration, and geothermal energy. It is useful to start with some definitions to establish a common vocabulary. Data analytics (DA)—Sophisticated data collection and analysis to understand and model hidden patterns and relationships in complex, multivariate data sets Machine learning (ML)—Building a model between predictors and response, where an algorithm (often a black box) is used to infer the underlying input/output relationship from the data Artificial intelligence (AI)—Applying a predictive model with new data to make decisions without human intervention (and with the possibility of feedback for model updating) Thus, DA can be thought of as a broad framework that helps determine what happened (descriptive analytics), why it happened (diagnostic analytics), what will happen (predictive analytics), or how can we make something happen (prescriptive analytics) (Sankaran et al. 2019). Although DA is built upon a foundation of classical statistics and optimization, it has increasingly come to rely upon ML, especially for predictive and prescriptive analytics (Donoho 2017). While the terms DA, ML, and AI are often used interchangeably, it is important to recognize that ML is basically a subset of DA and a core enabling element of the broader application for the decision-making construct that is AI. In recent years, there has been a proliferation in studies using ML for predictive analytics in the context of subsurface energy resources. Consider how the number of papers on ML in the OnePetro database has been increasing exponentially since 1990 (Fig. 1). These trends are also reflected in the number of technical sessions devoted to ML/AI topics in conferences organized by SPE, AAPG, and SEG among others; as wells as books targeted to practitioners in these professions (Holdaway 2014; Mishra and Datta-Gupta 2017; Mohaghegh 2017; Misra et al. 2019). Given these high levels of activity, our goal is to provide some observations and recommendations on the practice of data-driven model building using ML techniques. The observations are motivated by our belief that some geoscientists and petroleum engineers may be jumping the gun by applying these techniques in an ad hoc manner without any foundational understanding, whereas others may be holding off on using these methods because they do not have any formal ML training and could benefit from some concrete advice on the subject. The recommendations are conditioned by our experience in applying both conventional statistical modeling and data analytics approaches to practical problems.

Paradigms of Machine Learning and Data Analytics

2020 ◽  
pp. 156-174
Author(s):  
Pawan Kumar Chaurasia
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Deep Learning ◽  
Critical Review ◽  
Data Analytics ◽  
Disease Patient ◽  
Learning Tools ◽  
Heart Disease Patient ◽  
Tools And Techniques ◽  
New Hypothesis

This chapter conducts a critical review on ML and deep learning tools and techniques in the field of heart disease related to heart disease complexity, prediction, and diagnosis. Only specific papers are selected for the study to extract useful information, which stimulated a new hypothesis to understand further investigation of the heart disease patient.

FinTech Adoption in China

2021 ◽  
pp. 166-173
Author(s):  
Gagan Kukreja
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Financial Services ◽  
New Technologies ◽  
Large Population ◽  
Financial Inclusion ◽  
High Penetration ◽  
Digital Payments ◽  
Almost All

Almost all financial services (especially digital payments) in China are affected by new innovations and technologies. New technologies such as blockchain, artificial intelligence, machine learning, deep learning, and data analytics have immensely influenced all most all aspects of financial services such as deposits, transactions, billings, remittances, credits (B2B and P2P), underwriting, insurance, and so on. Fintech companies are enabling larger financial inclusion, changing in lifestyle and expenditure behavior, better and fast financial services, and lots more. This chapter covers the development, opportunities, and challenges of financial sectors because of new technologies in China. This chapter throws the light on opportunities that emerged because of the large population of 1.4 billion people, high penetration, and access to the latest and affordable technology, affordable cost of smartphones, and government policies and regulations. Lastly, this chapter portrays the untapped potentials of Fintech in China.

scholarly journals COMPARATIVE ANALYSIS AND EVALUATION OF THE APPLICATION OF DEEP LEARNING TECHNIQUES TO CYBERSECURITY DATASETS

10.6036/10007 ◽  
2021 ◽  
Vol 96 (5) ◽  
pp. 528-533
Author(s):  
XAVIER LARRIVA NOVO ◽  
MARIO VEGA BARBAS ◽  
VICTOR VILLAGRA ◽  
JULIO BERROCAL
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
High Performance ◽  
New Technologies ◽  
Short Term Memory ◽  
Machine Learning Techniques ◽  
Short Term ◽  
Term Memory ◽  
Learning Techniques ◽  
Long Short Term Memory

Cybersecurity has stood out in recent years with the aim of protecting information systems. Different methods, techniques and tools have been used to make the most of the existing vulnerabilities in these systems. Therefore, it is essential to develop and improve new technologies, as well as intrusion detection systems that allow detecting possible threats. However, the use of these technologies requires highly qualified cybersecurity personnel to analyze the results and reduce the large number of false positives that these technologies presents in their results. Therefore, this generates the need to research and develop new high-performance cybersecurity systems that allow efficient analysis and resolution of these results. This research presents the application of machine learning techniques to classify real traffic, in order to identify possible attacks. The study has been carried out using machine learning tools applying deep learning algorithms such as multi-layer perceptron and long-short-term-memory. Additionally, this document presents a comparison between the results obtained by applying the aforementioned algorithms and algorithms that are not deep learning, such as: random forest and decision tree. Finally, the results obtained are presented, showing that the long-short-term-memory algorithm is the one that provides the best results in relation to precision and logarithmic loss.

Digital Investigation of Cybercrimes Based on Big Data Analytics Using Deep Learning

2018 ◽  
pp. 79-101 ◽  
Author(s):  
Ezz El-Din Hemdan ◽  
Manjaiah D. H.
Keyword(s):  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Digital Forensics ◽  
New Technologies ◽  
Early Stage ◽  
Big Data Analytics ◽  
Learning Techniques ◽  
Timely Fashion ◽  
Digital Investigation

Big Data Analytics has become an important paradigm that can help digital investigators to investigate cybercrimes as well as provide solutions to malware and threat prediction, detection and prevention at an early stage. Big Data Analytics techniques can use to analysis enormous amount of generated data from new technologies such as Social Networks, Cloud Computing and Internet of Things to understand the committed crimes in addition to predict the new coming severe attacks and crimes in the future. This chapter introduce principles of Digital Forensics and Big Data as well as exploring Big Data Analytics and Deep Learning benefits and advantages that can help the digital investigators to develop and propose new techniques and methods based on Big Data Analytics using Deep Learning techniques that can be adapted to the unique context of Digital Forensics as well as support performing digital investigation process in forensically sound and timely fashion manner.

An Introduction to Data Analytics

2017 ◽  
pp. 1-14 ◽  
Author(s):  
A. Sheik Abdullah ◽  
S. Selvakumar ◽  
A. M. Abirami
Keyword(s):  
Social Media ◽  
Data Analytics ◽  
Predictive Analytics ◽  
Analytical Techniques ◽  
Educational Institutions ◽  
Social Media Analytics ◽  
Text Analytics ◽  
Raw Data ◽  
Industrial Organizations ◽  
Prescriptive Analytics

Data analytics mainly deals with the science of examining and investigating raw data to derive useful patterns and inference. Data analytics has been deployed in many of the industries to make decisions at proper levels. It focuses upon the assumption and evaluation of the method with the intention of deriving a conclusion at various levels. Various types of data analytical techniques such as predictive analytics, prescriptive analytics, descriptive analytics, text analytics, and social media analytics are used by industrial organizations, educational institutions and by government associations. This context mainly focuses towards the illustration of contextual examples for various types of analytical techniques and its applications.

In-Memory Analytics

2019 ◽  
pp. 381-389
Author(s):  
Jorge Manjarrez Sánchez
Keyword(s):  
Machine Learning ◽  
Data Management ◽  
Data Analytics ◽  
Information Discovery ◽  
Research Directions ◽  
Prescriptive Analytics ◽  
Fast Processing ◽  
Statistical Algorithms

Analytics is the processing of data for information discovery. In-memory implementation of machine learning and statistical algorithms enable the fast processing of data for descriptive, diagnostic, predictive, and prescriptive analytics. In this chapter, the authors first present some concepts and challenges for fast analytics, then they discuss some of the most relevant proposals and data management structures for in-memory data analytics in centralized, parallel, and distributed settings. Finally, the authors offer further research directions and some concluding remarks.

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