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Fraud Detection
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Fraud Detection is a vital topic that applies to many industries including the financial sectors, banking, government agencies, insurance, and law enforcement, and more. Fraud endeavors have detected a radical rise in current years, creating this topic more critical than ever. Despite struggles on the part of the troubled organizations, hundreds of millions of dollars are wasted to fraud each year. Because nearly a few samples confirm fraud in a vast community, locating these can be complex. Data mining and statistics help to predict and immediately distinguish fraud and take immediate action to minimize costs.
Source: Applying support vector data description for fraud detection
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/boxinz17/data-market-via-adaptive-sampling
공식 코드https://github.com/campanulabells/huge-gad
공식 코드A Label-Free Heterophily-Guided Approach for Unsupervised Graph Fraud Detection
https://github.com/cchao0116/EasyDGL
공식 코드EasyDGL: Encode, Train and Interpret for Continuous-time Dynamic Graph Learning
https://github.com/compnet/pang
공식 코드Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement
https://github.com/cvjena/libmaxdiv
공식 코드Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection
https://github.com/davidnugent2425/he-cc-fraud-detection
공식 코드Privacy-Preserving Credit Card Fraud Detection using Homomorphic Encryption
https://github.com/dcszhang/dynamic_feature
공식 코드https://github.com/deeksha-sethi03/nl-imbalanced-learning
공식 코드Neurochaos Feature Transformation and Classification for Imbalanced Learning
https://github.com/derposoft/crypto_pump_and_dump_with_deep_learning
공식 코드https://github.com/dhivyaeswaran/hols
공식 코드https://github.com/dmlc/dgl
공식 코드DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs
https://github.com/donghao51/nng-mix
공식 코드NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
https://github.com/ds3lab/dyhgn
공식 코드Modelling graph dynamics in fraud detection with "Attention"
https://github.com/eBay/xFraud
공식 코드https://github.com/elopezfune/urysohn-s-binary-classifier
공식 코드Leveraging the Urysohn Lemma of Topology for an Enhanced Binary Classifier
https://github.com/facebookresearch/taser-tgnn
공식 코드TASER: Temporal Adaptive Sampling for Fast and Accurate Dynamic Graph Representation Learning
https://github.com/fdjingliu/dmad
공식 코드https://github.com/featurespace/foundation-model-paper
공식 코드Towards a Foundation Purchasing Model: Pretrained Generative Autoregression on Transaction Sequences
https://github.com/feedzai/bank-account-fraud
공식 코드Turning the Tables: Biased, Imbalanced, Dynamic Tabular Datasets for ML Evaluation
https://github.com/feedzai/deccaf
공식 코드Cost-Sensitive Learning to Defer to Multiple Experts with Workload Constraints
https://github.com/feedzai/fair-automl
공식 코드https://github.com/feedzai/fifar-dataset
공식 코드https://github.com/feedzai/research-arms
공식 코드https://github.com/feedzai/timeshap
공식 코드TimeSHAP: Explaining Recurrent Models through Sequence Perturbations
https://github.com/fingertap/fird.cython
공식 코드https://github.com/finint/antifraud
공식 코드Semi-supervised Credit Card Fraud Detection via Attribute-Driven Graph Representation
https://github.com/flopska/mmdew-change-detector
공식 코드Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
https://github.com/foobar777x/ja-gnn-exp
공식 코드Financial Fraud Detection using Jump-Attentive Graph Neural Networks
https://github.com/freshmanxb/dbdt
공식 코드Efficient Fraud Detection Using Deep Boosting Decision Trees
https://github.com/gisec-team/meta-ifd
공식 코드Enhancing Ethereum Fraud Detection via Generative and Contrastive Self-supervision
https://github.com/git-disl/bert4eth
공식 코드BERT4ETH: A Pre-trained Transformer for Ethereum Fraud Detection
https://github.com/git-disl/ellipticplusplus
공식 코드https://github.com/goyalanil/DAMVI
공식 코드Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data
https://github.com/graph-com/gad-nr
공식 코드GAD-NR: Graph Anomaly Detection via Neighborhood Reconstruction
https://github.com/ibpa/kglm
공식 코드KGLM: Integrating Knowledge Graph Structure in Language Models for Link Prediction
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