Study shows it's hard to detect when a decision-maker fakes fairness using a specific sampling technique.
problem Detecting when decision-makers fake fairness using auditing tools.
method Developed a stealthily biased sampling algorithm to construct deceptive benchmark datasets.
result The constructed deceptive datasets are difficult to detect, making it hard to avoid fake fairness.
Algorithm solves job acceptance problem with random arrivals and values.
problem Decision-making under random job arrivals and values with limited acceptance.
method Proposes Non-Parametric Sequential Allocation (NPSA) algorithm.
result Expected reward converges to optimality as sample size increases.
Model detects insurance fraud using social network analysis.
problem Fraudulent insurance claims by exaggeration or intentional damage.
method Network construction linking claims and parties, BiRank algorithm for fraud score computation, feature extraction from network and claims, supervised model building.
result Network features improve fraud detection performance.
New method detects organized eCommerce fraud by clustering orders.
problem Detecting coordinated fraud by groups of fraudsters.
method Scalable categorical clustering using agglomerative clustering and sampling.
result Groups 35-45% of fraudulent orders together, detects 26.2% of fraud with low false alarms.
Paper introduces a fraud detection dataset benchmark.
problem Unique challenges in fraud detection datasets.
method Compilation of publicly available fraud datasets.
result Demonstrates applications of the Fraud Dataset Benchmark.
Model predicts internal fraud in retail banking is cyclical and influenced by corruption.
problem Predicting and mitigating internal fraud losses in retail banking.
method Developed a dynamic model considering internal factors and macroeconomic indicators.
result Internal fraud losses are pro-cyclical and positively affected by corruption perceptions.
New taxonomy reveals different detection limits for various types of fraud.
problem Existing fraud detection treats all fraud as the same, ignoring its diverse forms.
method Introduced an observation-mechanism taxonomy with five fraud classes.
result Separate estimation by fraud class outperforms pooled estimation.
Interleaved RNNs detect fraud without costly features.
problem Real-time fraud detection in payment cards.
method Use interleaved sequence RNNs for fraud detection.
result Interleaved RNNs outperform state-of-the-art models in fraud detection.
Paper improves fraud detection in imbalanced financial data.
problem Detecting fraud in imbalanced financial datasets.
method Uses time-varying Poisson processes for fraud prediction.
result Method outperforms baseline in imbalanced data.
Graph Neural Networks improve financial fraud detection.
problem Complex financial transactions pose challenges in fraud detection.
method Unified framework of GNN methodologies applied to financial fraud detection.
result GNNs excel at capturing complex relational patterns in financial networks.
This paper tackles credit card fraud detection using graph-based learning methods.
problem Detecting credit card fraud to reduce financial losses.
method Graph p-Laplacian based semi-supervised learning combined with undersampling techniques.
result Graph p-Laplacian semi-supervised learning outperforms current methods.
New method detects corporate fraud in noisy financial networks.
problem Detecting corporate fraud in rich yet noisy financial networks.
method Knowledge-enhanced GCN with Robust Two-stage Learning (KeGCN_R)
result KeGCN_R outperforms baselines in fraud detection effectiveness and robustness.
Study examines fraud detection methods for credit cards with limited data.
problem Data imbalance in credit card fraud detection.
method Assesses different sampling methods and machine learning algorithms.
result Monte Carlo analysis shows random undersampling outperforms SMOTE in fraud cost reduction.
BreachRadar detects points-of-compromise in bank transactions to prevent fraud.
problem Detecting and preventing bank transaction fraud caused by data breaches.
method A distributed alternating algorithm that assigns probabilities to different locations being compromised.
result BreachRadar achieves over 90% precision and recall in detecting compromised cards.
New algorithm improves fraud detection by analyzing financial account relationships.
problem High false positive rates and missed detections in conventional fraud detection systems.
method Personalized PageRank (PPR) algorithm to capture social dynamics of fraud.
result Integrating PPR enhances fraud detection model's predictive power.
TitAnt detects online transaction fraud in milliseconds.
problem Real-time detection of online transaction fraud in e-commerce.
method Feature extraction, real-time prediction, deployment in Ant Financial.
result Efficient real-time fraud detection in milliseconds.
TimeTrail detects financial fraud patterns through temporal correlation analysis.
problem Detecting and explaining complex financial fraud patterns.
method Temporal data enrichment, dynamic correlation analysis, interpretable pattern visualization.
result TimeTrail outperforms conventional methods in accuracy and interpretability.
Quantum Support Vector Classifier outperforms other QML models in finance fraud detection.
problem Detecting financial fraud using Quantum Machine Learning.
method Comparative study of four QML models: Quantum Support Vector Classifier, Variational Quantum Classifier, Estimator QNN, and Sampler QNN.
result Quantum Support Vector Classifier achieved the highest F1 scores (0.98) for fraud and non-fraud classes.
EnsemFDet detects fraud by solving subproblems on small graphs, scaling up e-commerce fraud detection.
problem Detecting and preventing fraud in e-commerce, especially in large-scale bipartite graphs.
method EnsemFDet uses an ensemble approach to decompose the problem into smaller subproblems, solving them in parallel.
result EnsemFDet is up to 100x faster than state-of-the-art methods while maintaining high accuracy.
DBDT uses deep boosting decision trees for fraud detection.
problem Fraud detection in imbalanced data.
method Gradient boosting with neural networks (SDT), AUC maximization.
result DBDT significantly improves fraud detection performance.
ARIMA model detects credit card fraud in unbalanced datasets.
problem Unsupervised credit card fraud detection in unbalanced datasets.
method ARIMA model applied to customer spending patterns for anomaly detection.
result ARIMA model outperforms benchmark anomaly detection methods.
Accounting fraud is a global concern representing a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Several tricks can be used to commit accounting fraud, hence the need for non-static regulatory interventions that take …
Enhances fraud detection with multiple HMM perspectives.
problem Detecting credit card fraud from sequential transactions.
method Modeling credit card transactions from three perspectives (card-holder, terminal, amount/time) using HMMs and combining likelihoods as features.
result 15% increase in precision-recall AUC compared to state-of-the-art methods.
This paper examines anomalies and frauds in blockchain networks and proposes detection techniques.
problem Anomalies and frauds undermine blockchain networks' integrity and security.
method Statistical and machine learning methods, game-theoretic solutions, digital forensics, reputation-based systems, and risk assessment techniques.
result Practical applications and insights for enhancing blockchain network security.
Although shill bidding is a common auction fraud, it is however very tough to detect. Due to the unavailability and lack of training data, in this study, we build a high-quality labeled shill bidding dataset based on recently collected auctions from eBay. Labeling shill biding instances with multidimensional features i…
The automatic detection of frauds in banking transactions has been recently studied as a way to help the analysts finding fraudulent operations. Due to the availability of a human feedback, this task has been studied in the framework of active learning: the fraud predictor is allowed to sequentially call on an oracle. …
CaT-GNN improves credit card fraud detection by integrating causal reasoning into GNNs.
problem Credit card fraud detection overlooks causal structure of transactions.
method CaT-GNN combines causal invariant learning and temporal graph neural networks.
result CaT-GNN outperforms existing methods on various datasets.
Credit card fraud detection is a very challenging problem because of the specific nature of transaction data and the labeling process. The transaction data is peculiar because they are obtained in a streaming fashion, they are strongly imbalanced and prone to non-stationarity. The labeling is the outcome of an active l…
Study uses stacked generalization to improve fraud detection algorithms.
problem Improving performance of algorithms in imbalanced fraud data sets.
method Two-step process combining machine learning methods and cross-validation.
result Improved performance metrics on resampled fraud data sets.
SemiGNN detects financial fraud using social relations and multi-view data.
problem Detecting fraud in financial services with limited labeled data and complex interactions.
method Semi-supervised graph attentive network with hierarchical attention mechanism.
result SemiGNN achieves better accuracy on fraud detection tasks compared to state-of-the-art methods.
Unsupervised model detects healthcare fraud from patient visit data.
problem Detecting fraudulent healthcare bills from patient visit data.
method Uses LSTM and seq2seq models for anomaly detection, normalizes scores with EDF.
result Improves anomaly detection for high class imbalance problems.
Deep semi-supervised anomaly detection improves fraud detection in financial markets.
problem Detecting fraud in high-frequency financial data with limited labeled examples.
method Evaluation of Deep Semi-Supervised Anomaly Detection (Deep SAD) on proprietary limit order book data.
result Deep SAD significantly improves fraud detection accuracy with minimal labeled data.
Study evaluates AD methods for fraud detection in online credit card payments.
problem Fraud detection in online credit card payments using anomaly detection methods.
method Assessed several recent anomaly detection methods and compared them with standard supervised learning methods.
result LightGBM outperforms other methods but is more sensitive to distribution shifts.
FraudTransformer detects payment fraud by preserving event order and time gaps.
problem Detecting payment fraud in real-world banking streams with irregular time gaps.
method Augments a GPT-style architecture with a dedicated time encoder and a learned positional encoder.
result FraudTransformer outperforms classical and transformer baselines, achieving highest AUROC and PRAUC on held-out test set.
A new method detects financial fraud using graph transformers.
problem Detecting fraudulent transactions in financial data.
method Spatial-Temporal-Aware Graph Transformer (STA-GT) integrating GNNs and transformers.
result STA-GT outperforms general GNN models on financial fraud detection.
Adaptive Stress Testing detects financial fraud by simulating potential failures.
problem Detecting and mitigating vulnerabilities in financial systems.
method Developed a simplified model using historical data and reinforcement learning.
result Identified the most likely path to system failure and improved fraud detection.
Fraud detection is a difficult problem that can benefit from predictive modeling. However, the verification of a prediction is challenging; for a single insurance policy, the model only provides a prediction score. We present a case study where we reflect on different instance-level model explanation techniques to aid …
Paper quantifies dataset shift for credit card fraud detection.
problem Change in purchase behavior over time affects fraud detection accuracy.
method Measures day-to-day dataset shift using classification efficiency and clustering.
result Improves credit card fraud detection by incorporating dataset shift knowledge.
A CBR approach helps fraud analysts trust machine learning predictions.
problem Understanding the trustworthiness of machine learning predictions for fraud analysts.
method Case-based reasoning (CBR) approach to visualize similar previous instances and their local post-hoc explanations.
result Empirically, the visualization of similar previous instances is useful and easy to use for fraud analysts.
robROSE tackles imbalanced fraud data by creating synthetic samples and detecting outliers.
problem Detecting fraud in imbalanced data sets where fraud is a minority class.
method Combines oversampling techniques with robust statistics to handle anomalies.
result robROSE enhances fraud detection while ignoring anomalies.
This paper summarizes AI methods for detecting credit card fraud.
problem Detecting credit card fraud from millions of transactions.
method Rule-based and AI approaches, addressing imbalanced datasets, real-time scenarios, and feature engineering.
result Summarizes state-of-the-art AI methods for fraud detection.
QFNN-FFD uses quantum computing and FL for secure financial fraud detection.
problem Financial fraud detection in the financial sector.
method Quantum Federated Neural Network (QFNN-FFD) combining QML and FL.
result Achieves precision rates above 95% and robustness against noise.
Relational Graph Neural Networks improve fraud detection in Super-Apps.
problem Fraud detection in Super-Apps using alternative data.
method Relational Graph Convolutional Networks applied to heterogeneous graphs.
result Added value in fraud detection when considering alternative data and interactions.
The paper proposes a method to detect credit card fraud using sparse Gaussian approximations.
problem Detecting credit card fraud in financial institutions.
method Sparse Gaussian classification method with pseudo or inducing inputs and different kernels and inducing points.
result The RBF kernel with a higher number of inducing points achieved the best accuracy.
DILP improves fraud detection explainability without significant performance boost.
problem Improving fraud detection explainability in machine learning.
method Differentiable Inductive Logic Programming (DILP) for fraud detection with data curation.
result DILP provides comparable results to traditional methods but lacks significant advantage.
EmDT generates synthetic fraud data to improve detection accuracy.
problem Imbalanced datasets in fraud detection lead to poor performance on rare fraudulent transactions.
method EmDT uses UMAP clustering to identify fraudulent patterns and a Transformer denoising network to generate synthetic data.
result EmDT significantly improves classification performance compared to existing methods.
A new approach models credit card transactions using HMMs to detect fraud.
problem Detecting credit card fraud using isolated event analysis.
method Model sequences from three perspectives using HMMs and combine likelihoods as features.
result Improved fraud detection effectiveness compared to state-of-the-art methods.
Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams.
problem Financial fraud detection in digital banking requires reasoning over multiple heterogeneous event streams.
method Multi-Stream Fraud Transformer (MSFT) architecture that encodes each event stream with independent Transformer encoders and fuses their representations through configurable mechanisms.
result Sequence models significantly outperform gradient-boosted trees operating on aggregated features.