Fraud analytics using descriptive, predictive, and social network techniques : a guide to data science for fraud detection / / Bart Baesens, Veronique Van Vlasselaer, Wouter Verbeke

• Record Nr.: UNINA9910131489503321
• Autore: Baesens Bart
• Pubbl/distr/stampa: Hoboken, New Jersey : , : Wiley, , 2015; ©2015
• ISBN: 1-119-14683-6; 1-119-14684-4; 1-119-14682-8
• Edizione: [1st edition]
• Descrizione fisica: 1 online resource (402 p.)
• Collana: Wiley and SAS Business Series
• Disciplina: 364.16/3015195
• Soggetti: Fraud - Statistical methods; Fraud - Prevention; Commercial crimes - Prevention
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Includes index.
• Nota di bibliografia: Includes bibliographical references at the end of each chapters and index.
• Nota di contenuto: Cover; Title Page; Copyright; Contents; List of Figures; Foreword; Preface; Acknowledgments; Chapter 1 Fraud: Detection, Prevention, and Analytics!; Introduction; Fraud!; Fraud Detection and Prevention; Big Data for Fraud Detection; Data-Driven Fraud Detection; Fraud-Detection Techniques; Fraud Cycle; The Fraud Analytics Process Model; Fraud Data Scientists; A Fraud Data Scientist Should Have Solid Quantitative Skills; A Fraud Data Scientist Should Be a Good Programmer; A Fraud Data Scientist Should Excel in Communication and Visualization Skills; A Fraud Data Scientist Should Have a Solid Business Understanding A Fraud Data Scientist Should Be Creative; A Scientific Perspective on Fraud; References; Chapter 2 Data Collection, Sampling, and Preprocessing; Introduction; Types of Data Sources; Merging Data Sources; Sampling; Types of Data Elements; Visual Data Exploration and Exploratory Statistical Analysis; Benford's Law; Descriptive Statistics; Missing Values; Outlier Detection and Treatment; Red Flags; ; Standardizing Data; Categorization; Weights of Evidence Coding; Variable Selection; Principal Components Analysis; RIDITs; PRIDIT Analysis Segmentation; References; Chapter 3 Descriptive Analytics for Fraud Detection; Introduction; Graphical Outlier Detection Procedures; Statistical Outlier Detection Procedures; Break-Point Analysis; Peer-Group Analysis; Association Rule Analysis; Clustering; Introduction; Distance Metrics; Hierarchical Clustering; Example of Hierarchical Clustering Procedures; k-Means Clustering; Self-Organizing Maps; Clustering with Constraints; Evaluating and Interpreting Clustering Solutions; One-Class SVMs; References; Chapter 4 Predictive Analytics for Fraud Detection; Introduction; Target Definition Linear Regression; Logistic Regression; Basic Concepts; Logistic Regression Properties; Building a Logistic Regression Scorecard; Variable Selection for Linear and Logistic Regression; Decision Trees; Basic Concepts; Splitting Decision; Stopping Decision; Decision Tree Properties; Regression Trees; Using Decision Trees in Fraud Analytics; Neural Networks; Basic Concepts; Weight Learning; Opening the Neural Network Black Box; Support Vector Machines; Linear Programming; The Linear Separable Case; The Linear Nonseparable Case; The Nonlinear SVM Classifier; SVMs for Regression; Opening the SVM Black Box Ensemble Methods; Bagging; Boosting; Random Forests; Evaluating Ensemble Methods; Multiclass Classification Techniques; Multiclass Logistic Regression; Multiclass Decision Trees; Multiclass Neural Networks; Multiclass Support Vector Machines; Evaluating Predictive Models; Splitting Up the Data Set; Performance Measures for Classification Models; Performance Measures for Regression Models; Other Performance Measures for Predictive Analytical Models; Developing Predictive Models for Skewed Data Sets; Varying the Sample Window; Undersampling and Oversampling; Synthetic Minority Oversampling Technique (SMOTE)
• Sommario/riassunto: Detect fraud earlier to mitigate loss and prevent cascading damage Fraud Analytics Using Descriptive, Predictive, and Social Network Techniques is an authoritative guidebook for setting up a comprehensive fraud detection analytics solution. Early detection is a key factor in mitigating fraud damage, but it involves more specialized techniques than detecting fraud at the more advanced stages. This invaluable guide details both the theory and technical aspects of these techniques, and provides expert insight into streamlining implementation. Coverage includes data gathering, preprocessing, mode

Advanced Data Mining and Applications : 19th International Conference, ADMA 2023, Shenyang, China, August 21-23, 2023, Proceedings, Part I / / edited by Xiaochun Yang [and seven others]

• Record Nr.: UNISA996565871103316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2023]; ©2023
• ISBN: 3-031-46661-6
• Edizione: [First edition.]
• Descrizione fisica: 1 online resource (848 pages)
• Collana: Lecture Notes in Computer Science Series ; ; Volume 14176
• Disciplina: 943.005
• Soggetti: Data mining
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Time Series -- An Adaptive Data-Driven Imputation Model for Incomplete Event Series -- From Time Series to Multi-Modality: Classifying Multivariate Time Series via Both 1D and 2D Representations -- Exploring the Effectiveness of Positional Embedding on Transformer-based Architectures for Multivariate Time Series Classification -- Modeling of Repeated Measures for Time-to-Event Prediction -- A Method for Identifying the Timeliness of Manufacturing Data Based on Weighted Timeliness Graph -- STAD: Multivariate Time Series Anomaly Detection Based on Spatio-temporal Relationship -- Recommendation I -- Refined Node Type Graph Convolutional Network for Recommendation -- Multi-level Noise Filtering and Preference Propagation Enhanced Knowledge Graph Recommendation -- Enhancing Knowledge-aware Recommendation with Contrastive Learning -- Knowledge-Rich Influence Propagation Recommendation Algorithm Based on Graph Attention Networks -- A Novel Variational Autoencoder with Multi-Position Latent Self-Attention and Actor-Critic for Recommendation -- Fair Re-ranking Recommendation Based on Debiased Multi-Graph Representations -- Information Extraction -- FastNER: Speeding Up Inferences for Named Entity Recognition Tasks -- CPMFA: A Character Pair-Based Method for Chinese Nested Named Entity Recognition -- STMC-GCN: A Span Tagging Multi-Channel Graph Convolutional Network for Aspect Sentiment Triplet Extraction -- ; Exploring the Design Space of Unsupervised Blocking with Pre-trained Language Models in Entity Resolution -- Joint Modeling of Local and Global Semantics for Contrastive Entity Disambiguation -- Fine-grained Review Analysis using BERT with Attention: A Categorical and Rating-based Approach -- Emotional Analysis -- Discovery of Emotion Implicit Causes in Products based on Commonsense Reasoning -- Multi-modal Multi-emotion Emotional Support Conversation -- Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations -- Generating Enlightened Suggestions based on Mental State Evolution for Emotional Support Conversation -- Deep One-Class Fine-Tuning for Imbalanced Short Text Classification in Transfer Learning -- EmoKnow: Emotion- and Knowledge-oriented Model for COVID-19 Fake News Detection -- Popular Songs: The Sentiment Surrounding the Conversation -- Market Sentiment Analysis based on Social Media and Trading Volume for Asset Price Movement Prediction -- Data Mining -- Efficient mining of high utility co-location patterns based on a query strategy -- Point-level Label-free Segmentation Framework for 3D Point Cloud Semantic Mining -- CD-BNN: Causal Discovery with Bayesian Neural Network -- A Preference-based Indicator Selection Hyper-heuristic for Optimization Problems -- An Elastic Scalable Grouping for Stateful Operators in Stream Computing Systems -- Incremental natural gradient boosting for probabilistic regression -- Discovering Skyline Periodic Itemset Patterns in Transaction Sequences -- Double-optimized CS-BP Anomaly Prediction for Control Operation Data -- Bridging the Interpretability Gap in Coupled Neural Dynamical Models -- Multidimensional Adaptative kNN Over Tracking Outliers (Makoto) -- Traffic -- MANet: An End-to-End Multiple Attention Network for Extracting Roads around EHV Transmission Lines from High-Resolution Remote Sensing Images -- Deep Reinforcement Learning for Solving the Trip Planning Query -- MDCN: Multi-Scale Dilated Convolutional Enhanced Residual Network for Traffic Sign Detection -- Identifying Critical Congested Roads based on Traffic Flow-Aware Road Network Embedding -- A Cross-Region-based Framework for Supporting Car-Sharing -- Attention-based Spatial-Temporal Graph Convolutional Recurrent Networks for Traffic Forecasting -- Transformer Based Driving Behavior Safety Prediction For New Energy Vehicles -- Graph Convolution Recurrent Denoising Diffusion Model for Multivariate Probabilistic Temporal Forecasting -- A Bottom-Up Sampling Strategy for Reconstructing Geospatial Data from Ultra Sparse Inputs -- Recommendation II -- Feature Representation Enhancing by Context Sensitive Information in CTR Prediction -- ProtoMix: Learnable Data Augmentation on Few-shot Features with Vector Quantization in CTR Prediction -- When Alignment Makes a Difference: A Content-Based Variational Model for Cold-Start CTR Prediction -- Dual-Ganularity Contrastive Learning for Session-based Recommendation -- Efficient Graph Collaborative Filtering with Multi-layer Output-enhanced Contrastive Learning -- Influence Maximization with Tag Revisited: Exploiting the Bi-Submodularity of the Tag-Based Influence Function -- Multi-Interest Aware Graph Convolution Network for Social Recommendation -- Enhancing Multimedia Recommendation through Item-Item Semantic Denoising and Global Preference Awareness -- Resident-based Store Recommendation Model for Community Commercial Planning.c.
• Sommario/riassunto: This book constitutes the refereed proceedings of the 19th International Conference on Advanced Data Mining and Applications, ADMA 2023, held in Shenyang, China, during August 21–23, 2023. The 216 full papers included in this book were carefully reviewed and selected from 503 submissions. They were organized in topical ; sections as follows: Data mining foundations, Grand challenges of data mining, Parallel and distributed data mining algorithms, Mining on data streams, Graph mining and Spatial data mining.