High Performance CDR Processing with MapReduce by Mulya Agung, Achmad Imam Kistijantoro

“…It contains valuable information for many purposes in several domains, such as billing, fraud detection and analytical purposes. However, in the real world these needs face a big data challenge. …”
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Trigger Factors of Fraud Triangle Toward Fraud On Financial Reporting Moderated by Integration Of Technology Industry 4.0 by Agoestina Mappadang, Yuliansyah Yuliansyah

“…As Industry 4.0 ushers the use of new technology, the use of computerized systems for huge data analysis has advantage and disadvantage in audit and fraud detection. Therefore, our research uses the integration of technology 4.0 as a moderating variable on fraudulent financial reporting. …”
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FinTech enablers, use cases, and role of future internet of things by Jagadeesha R. Bhat, Salman A. AlQahtani, Maziar Nekovee

“…By the inception of technology, several major financial services and processes such as lending, verification, fraud detection, quality maintenance, credit scoring, and many more will be simplified and augmented. …”
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Object Detection in Online Proctoring Through Two Camera Using Faster-RCNN by I Wayan Suardinata, Vivien Arief Wardhany

“…The evaluation of the training results using the COCO evaluator showed the average of the bbox-AP is 59,169. The fraud detection process is carried out using 6 exam videos with a total of 192,929 frames, producing two outputs, namely object detection videos and csv files. …”
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Automated Data Slicing for Model Validation: A Big data - AI Integration Approach by Chung, Yeounoh, Kraska, Tim, Polyzotis, Neoklis, Tae, Kihyun, Whang, Steven Euijong

“…Applications include diagnosing model fairness and fraud detection, where identifying slices that are interpretable to humans is crucial. …”
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Outlier detection based on neighborhood proximity by Nguyen, Hoang Vu

“…In general, outlier detection has many practical applications, especially in domains that have scope for abnormal behavior, such as fraud detection, network intrusion detection, medical diagnosis, marketing, customer segmentation, etc. …”
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Forensic Accounting And Corporate Governance Maturity: Case Of Public Listed Companies In Oman by Rehman, Ali

“…Traditional audit activities have overlooked the role towards fraud detection which created expectation gaps; and due to this reason, inclusion of forensic accounting can be considered as an integral part in governance management system. …”
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Validity and reliability of the utilitarian value, hedonic value, brand satisfaction, emotional attachment, brand trust and mobile phone brand loyalty scales by Seduram, Linda, Perumal, Selvan, Shaari, Hasnizam

“…This paper examines the impact of fraud specific problem representation (FSPR) on the relationship with Accountants’ Skills Requirement (SR) and Fraud risk assessment (FRA) in the Nigerian public sector.The research methodology is quantitative with cross-sectional design and survey.The respondents are accountants (i.e. auditors and forensic accountants) in the public sector accounting and auditing institutions.The study addresses the gap in the literature by highlighting the significant influence of FSPR on SR and FRA regarding fraud detection, prevention and response.The findings from a second generation statistical analysis tool of the Partial Least Square-Structural Equation Modelling (PLS-SEM) confirm the direct relationship of accountants’ skills on fraud risk assessment. …”
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An empirical evaluation of sampling methods for the classification of imbalanced data. by Misuk Kim, Kyu-Baek Hwang

“…For example, positive examples are rare in the fields of disease diagnosis and credit card fraud detection. General machine learning methods are known to be suboptimal for such imbalanced classification. …”
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Locality and Consistency Based Sequential Ensemble Method for Outlier Detection by LIU Yi, MAO Ying-chi, CHENG Yang-kun, GAO Jian, WANG Long-bao

“…Outlier detection has been widely used in many fields,such as network intrusion detection,credit card fraud detection,etc.The increase in data dimensions leads to many irrelevant and redundant features,which will obscure the relevant features and result in false positive results.Due to the sparseness and distance aggregation effects of high-dimensional data,the traditional outlier detection algorithms based on density and distance are no longer applicable.Most of the outlier detection research based on machine learning focuses on a single model,which has certain deficiencies in anti-overfitting ability.The ensemble learning model has good generalization ability,and in actual application shows better prediction accuracy than the single model.This paper proposes an outlier detection sequence integration method LCSE based on neighborhood consistency (locality and consistency based sequential ensemble method for outlier detection).Firstly,it constructs a basic model of outlier detection based on diversity,secondly,selects the abnormal candidate points according to the global integration consistency,and finally considers the local neighborhood correlation of the data to select and combine the basic model results.Experiments verify that LCSE has an average outlier detection accuracy increase of 20.7% compared with traditional methods.Compared with the ensemble methods LSCP_AOM and iForest,the performance is increased by 3.6% on average.Therefore,it is better than other ensemble methods and neural network methods.…”
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Implementation of artificial intelligence technologies in corporate finance: classification by spheres of activity by Tutueva Darya, Viktorova Natalya, Sayakbaeva Ayganysh

“…Namely, in the areas of: accounting (accounts receivable management, accounts payable control, reporting, financial planning and analysis), external finance processes, internal audit, forensic accounting and fraud detection, work of CFO. The main benefits of such technological implementation are also defined for three groups: efficiency, control and decision-making. …”
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The OCS-SVM: An Objective-Cost-Sensitive SVM With Sample-Based Misclassification Cost Invariance by Shuang Yu, Xiongfei Li, Xiaoli Zhang, Hancheng Wang

“…On the other hand, many real-world problems, such as credit card fraud detection, intrusion detection, oil-spill detection and cancer diagnosis, usually involve substantially unequal misclassification costs. …”
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Urdu Wikification and Its Application in Urdu News Recommendation System by Safia Kanwal, Muhammad Kamran Malik, Zubair Nawaz, Khawar Mehmood

“…Many natural language processing applications, including question-answering systems, information retrieval, fraud detection, and recommendation systems(RS), can benefit from this information extraction technique. …”
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Deep Learning-Based Anomaly Detection in Video Surveillance: A Survey by Huu-Thanh Duong, Viet-Tuan Le, Vinh Truong Hoang

“…There has been a variety of surveys of anomaly detection, such as of network anomaly detection, financial fraud detection, human behavioral analysis, and many more. …”
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Fraud prediction using machine learning: The case of investment advisors in Canada by Mark Eshwar Lokanan, Kush Sharma

“…The findings are particularly relevant to regulators seeking new and effective fraud detection techniques while providing enhanced clarity to Canada’s financial markets’ self-regulation.…”
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Fingerprint recognition based on shark smell optimization and genetic algorithm by Bakhan Tofiq Ahmed, Omar Younis Abdulhameed

“…It is also possibly applied to other research topics such as fraud detection, e-payment, and other real-life applications authentication.…”
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FIA-ESI-MS Fingerprinting Method with Chemometrics for the Characterization of Adulterated Coffee Samples by Nerea Núñez, Josep Pons, Javier Saurina, Oscar Núñez

“…This work will focus on the theme of fraud detection in coffee, one of the most popular beverages in the world. …”
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Accelerating Pattern Matching Using a Novel Multi-Pattern-Matching Algorithm on GPU by Merve Çelebi, Uraz Yavanoğlu

“…Therefore, it is important to analyze packet contents in applications that require control over payloads, such as content filtering, intrusion detection systems (IDSs), data loss prevention systems (DLPs), and fraud detection. This technology, known as deep packet inspection (DPI), provides full control over the communication between two end stations by keenly analyzing the network traffic. …”
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Interpretable Single-dimension Outlier Detection (ISOD): An Unsupervised Outlier Detection Method Based on Quantiles and Skewness Coefficients by Yuehua Huang, Wenfen Liu, Song Li, Ying Guo, Wen Chen

“…A crucial area of study in data mining is outlier detection, particularly in the areas of network security, credit card fraud detection, industrial flaw detection, etc. Existing outlier detection algorithms, which can be divided into supervised methods, semi-supervised methods, and unsupervised methods, suffer from missing labeled data, the curse of dimensionality, low interpretability, etc. …”
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A Participation Degree-Based Fault Detection Method for Wireless Sensor Networks by Wei Zhang, Gongxuan Zhang, Xiaohui Chen, Xiumin Zhou, Yueqi Liu, Junlong Zhou

“…In wireless sensor networks (WSNs), there are many challenges for outlier detection, such as fault detection, fraud detection, intrusion detection, and so on. In this paper, the participation degree of instances in the hierarchical clustering process infers the relationship between instances. …”
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