Works about FRAUD investigation
Results: 1044
Identifying E-Commerce Fraud Through User Behavior Data: Observations and Insights: Identifying E-Commerce Fraud Through User Behavior Data: Observations and Insights: Z. Zhang et al.
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- Data Science & Engineering, 2025, v. 10, n. 1, p. 24, doi. 10.1007/s41019-024-00275-6
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ANALYSIS OF THE IMPACT OF FORENSIC ACCOUNTING IN DISCOVER AND PREVENTIVE ACTION TO REDUCE GRAY ECONOMY IN BOSNIA AND HERZEGOVINA.
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- Business Consultant / Poslovni Konsultant, 2024, v. 15, n. 133, p. 20
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Artificial Intelligence for Financial Accountability and Governance in the Public Sector: Strategic Opportunities and Challenges.
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- Administrative Sciences (2076-3387), 2025, v. 15, n. 2, p. 58, doi. 10.3390/admsci15020058
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MelCochleaGram-DeepCNN: Sequentially Fused Spectrogram and the DeepCNN Classifiers-based Audio Spoof Detection System.
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- IETE Journal of Research, 2025, v. 71, n. 1, p. 65, doi. 10.1080/03772063.2024.2412799
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Advanced Tax Fraud Detection: A Soft-Voting Ensemble Based on GAN and Encoder Architecture.
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- Mathematics (2227-7390), 2025, v. 13, n. 4, p. 642, doi. 10.3390/math13040642
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Evaluating Machine Learning Algorithms for Financial Fraud Detection: Insights from Indonesia.
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- Mathematics (2227-7390), 2025, v. 13, n. 4, p. 600, doi. 10.3390/math13040600
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Real Steps or Not: Auto-Walker Detection in Move-to-Earn Applications.
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- Sensors (14248220), 2025, v. 25, n. 4, p. 1002, doi. 10.3390/s25041002
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Probing the Predictors of Fraud Using the Fraud Diamond Theory: An Empirical Evidence from Local Governments in Ghana.
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- Forum for Development Studies, 2022, v. 49, n. 2, p. 291, doi. 10.1080/08039410.2022.2080759
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Weighted binary ELM optimized by the reptile search algorithm, application to credit card fraud detection.
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- Multimedia Tools & Applications, 2024, v. 83, n. 39, p. 86383, doi. 10.1007/s11042-024-19508-x
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Attention layer integrated BiLSTM for financial fraud prediction.
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- Multimedia Tools & Applications, 2024, v. 83, n. 34, p. 80613, doi. 10.1007/s11042-024-18764-1
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- Article
A semi-supervised Anti-Fraud model based on integrated XGBoost and BiGRU with self-attention network: an application to internet loan fraud detection.
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- Multimedia Tools & Applications, 2024, v. 83, n. 19, p. 56939, doi. 10.1007/s11042-023-17681-z
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A voting ensemble machine learning based credit card fraud detection using highly imbalance data.
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- Multimedia Tools & Applications, 2024, v. 83, n. 18, p. 54729, doi. 10.1007/s11042-023-17766-9
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A new electricity theft detection method using hybrid adaptive sampling and pipeline machine learning.
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- Multimedia Tools & Applications, 2024, v. 83, n. 18, p. 54521, doi. 10.1007/s11042-023-17730-7
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- Article
Double-weight LDA extracting keywords for financial fraud detection system.
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- Multimedia Tools & Applications, 2024, v. 83, n. 17, p. 50757, doi. 10.1007/s11042-023-17334-1
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- Article
Smart credit card fraud detection system based on dilated convolutional neural network with sampling technique.
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- Multimedia Tools & Applications, 2023, v. 82, n. 20, p. 31691, doi. 10.1007/s11042-023-15730-1
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Detection and localization of frame duplication using binary image template.
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- Multimedia Tools & Applications, 2023, v. 82, n. 20, p. 31001, doi. 10.1007/s11042-023-14602-y
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An abnormal surgical record recognition model with keywords combination patterns based on TextRank for medical insurance fraud detection.
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- Multimedia Tools & Applications, 2023, v. 82, n. 20, p. 30949, doi. 10.1007/s11042-023-14529-4
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Credit card fraud detection using ensemble data mining methods.
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- Multimedia Tools & Applications, 2023, v. 82, n. 19, p. 29057, doi. 10.1007/s11042-023-14698-2
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An efficient fraud detection framework with credit card imbalanced data in financial services.
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- Multimedia Tools & Applications, 2023, v. 82, n. 3, p. 4139, doi. 10.1007/s11042-022-13434-6
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DeepSignature: fine-tuned transfer learning based signature verification system.
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- Multimedia Tools & Applications, 2022, v. 81, n. 26, p. 38113, doi. 10.1007/s11042-022-12782-7
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- Article
A new honey adulteration detection approach using hyperspectral imaging and machine learning.
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- European Food Research & Technology, 2023, v. 249, n. 2, p. 259, doi. 10.1007/s00217-022-04113-9
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- Article
Votes Can Be Confidently Bought in Some Ranked Ballot Elections, and What to Do about It.
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- Political Analysis, 2024, v. 32, n. 4, p. 1, doi. 10.1017/pan.2024.4
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- Article
FRAUD DETECTION FOR ONLINE INTERBANK TRANSACTION USING DEEP LEARNING.
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- Journal of Syntax Literate, 2023, v. 8, n. 6, p. 4263, doi. 10.36418/syntax-literate.v8i6.12627
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A privacy preserving recommendation and fraud detection method based on graph convolution.
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- Electronic Research Archive, 2023, v. 31, n. 12, p. 1, doi. 10.3934/era.2023382
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- Article
A New Statistic for Detection of Aberrant Answer Changes.
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- Journal of Educational Measurement, 2017, v. 54, n. 2, p. 200, doi. 10.1111/jedm.12141
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- Article
Auditor Independence and Financial Fraud: Unraveling the Connection.
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- Optimization: Journal of Research in Management, 2024, v. 16, n. 2, p. 99
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面向异构社交网络的空-频域自适应图神经网络.
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- Journal of Frontiers of Computer Science & Technology, 2025, v. 19, n. 1, p. 169, doi. 10.3778/j.issn.1673-9418.2310047
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集成层级图注意力网络检测非均衡虚假评论.
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- Journal of Frontiers of Computer Science & Technology, 2023, v. 17, n. 2, p. 428, doi. 10.3778/j.issn.1673-9418.2104090
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Modeling Insurance Fraud Detection Using Ensemble Combining Classification.
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- International Journal of Computer Information Systems & Industrial Management Applications, 2016, v. 8, p. 257
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Design Principles for Robust Fraud Detection: The Case of Stock Market Manipulations.
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- Journal of the Association for Information Systems, 2021, v. 22, n. 1, p. 156, doi. 10.17705/1jais.00657
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The role of automated controls and streamlined compliance in managing risks in digital finance.
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- International Journal of Financial Engineering, 2024, v. 11, n. 3, p. 1, doi. 10.1142/S2424786324430047
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Fast Bayesian optimization of Needle-in-a-Haystack problems using zooming memory-based initialization (ZoMBI).
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- NPJ Computational Materials, 2023, v. 9, n. 1, p. 1, doi. 10.1038/s41524-023-01048-x
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Peningkatan Akurasi Pembobotan Attribute Importance Weights pada Deteksi Fraud.
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- Techno.com, 2020, v. 19, n. 4, p. 331, doi. 10.33633/tc.v19i4.3934
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Forensic Accounting in the Fraud Auditing Case.
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- European Journal of Applied Economics, 2016, v. 13, n. 2, p. 45, doi. 10.5937/ejae13-10509
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Internal Controls and the Detection of Management Fraud.
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- Journal of Accounting Research (Wiley-Blackwell), 1999, v. 37, n. 1, p. 101, doi. 10.2307/2491398
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Outlier detection for set-valued data based on rough set theory and granular computing.
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- International Journal of General Systems, 2023, v. 52, n. 4, p. 385, doi. 10.1080/03081079.2022.2132491
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A Hybrid Deep Learning Approach with Generative Adversarial Network for Credit Card Fraud Detection.
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- Technologies (2227-7080), 2024, v. 12, n. 10, p. 186, doi. 10.3390/technologies12100186
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Bi-Partitioned Feature-Weighted K -Means Clustering for Detecting Insurance Fraud Claim Patterns.
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- Mathematics (2227-7390), 2025, v. 13, n. 3, p. 434, doi. 10.3390/math13030434
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Research on Small-Sample Credit Card Fraud Identification Based on Temporal Attention-Boundary-Enhanced Prototype Network.
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- Mathematics (2227-7390), 2024, v. 12, n. 24, p. 3894, doi. 10.3390/math12243894
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A Bellman–Ford Algorithm for the Path-Length-Weighted Distance in Graphs.
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- Mathematics (2227-7390), 2024, v. 12, n. 16, p. 2590, doi. 10.3390/math12162590
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CCFD: Efficient Credit Card Fraud Detection Using Meta-Heuristic Techniques and Machine Learning Algorithms.
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- Mathematics (2227-7390), 2024, v. 12, n. 14, p. 2250, doi. 10.3390/math12142250
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An Intelligent Financial Fraud Detection Support System Based on Three-Level Relationship Penetration.
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- Mathematics (2227-7390), 2024, v. 12, n. 14, p. 2195, doi. 10.3390/math12142195
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Novel Machine Learning Based Credit Card Fraud Detection Systems.
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- Mathematics (2227-7390), 2024, v. 12, n. 12, p. 1869, doi. 10.3390/math12121869
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RaKShA : A Trusted Explainable LSTM Model to Classify Fraud Patterns on Credit Card Transactions.
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- Mathematics (2227-7390), 2023, v. 11, n. 8, p. 1901, doi. 10.3390/math11081901
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High-Cardinality Categorical Attributes and Credit Card Fraud Detection.
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- Mathematics (2227-7390), 2022, v. 10, n. 20, p. 3808, doi. 10.3390/math10203808
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Tuning Machine Learning Models Using a Group Search Firefly Algorithm for Credit Card Fraud Detection.
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- Mathematics (2227-7390), 2022, v. 10, n. 13, p. 2272, doi. 10.3390/math10132272
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Adopting Nonlinear Activated Beetle Antennae Search Algorithm for Fraud Detection of Public Trading Companies: A Computational Finance Approach.
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- Mathematics (2227-7390), 2022, v. 10, n. 13, p. 2160, doi. 10.3390/math10132160
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A Novel Time-Series Transformation and Machine-Learning- Based Method for NTL Fraud Detection in Utility Companies.
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- Mathematics (2227-7390), 2022, v. 10, n. 11, p. 1878, doi. 10.3390/math10111878
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Credit Card Fraud Detection Using a New Hybrid Machine Learning Architecture.
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- Mathematics (2227-7390), 2022, v. 10, n. 9, p. 1480, doi. 10.3390/math10091480
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Credit Card Fraud Detection with Autoencoder and Probabilistic Random Forest.
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- Mathematics (2227-7390), 2021, v. 9, n. 21, p. 2683, doi. 10.3390/math9212683
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