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.
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.
This paper discusses financial fraud detection in imbalanced dataset using homogeneous and non-homogeneous Poisson processes. The probability of predicting fraud on the financial transaction is derived. Applying our methodology to the financial dataset shows a better predicting power than a baseline approach, especiall…
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.
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.
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.
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.
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 …
This review examines deep learning in financial fraud detection over 5 years.
problem Improving deep learning techniques for financial fraud detection.
method Systematic literature review of 57 studies using performance metrics.
result Deep learning models enhance fraud detection across various financial domains.
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.
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.
TGN outperforms static GNNs in detecting financial fraud.
problem Anomaly detection in dynamic financial networks.
method Temporal Graph Networks (TGN) for capturing edge dynamics.
result TGN significantly outperforms static GNNs in AUC metrics.
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.
Paper proposes a new financial fraud detection system using improved RF and GBM.
problem Detecting financial fraud with high accuracy and efficiency.
method Introduces GBM-SSRF, a novel model combining GBM and SSRF.
result GBM-SSRF model outperforms traditional methods in fraud detection.
With the explosive growth of e-commerce and the booming of e-payment, detecting online transaction fraud in real time has become increasingly important to Fintech business. To tackle this problem, we introduce the TitAnt, a transaction fraud detection system deployed in Ant Financial, one of the largest Fintech compani…
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.
Improved fraud detection in finance with quantum-enhanced federated learning.
problem Challenges in detecting financial fraud with traditional methods.
method Hybrid quantum-enhanced federated learning framework combining quantum LSTM with privacy-preserving techniques.
result Approximately 5% improvement in performance metrics compared to conventional models.
New attacks inflate earnings while reducing fraud scores, potentially millions at stake.
problem Manipulating financial reports to hide distress and gain.
method Maximum Violated Multi-Objective (MVMO) attacks that adapt search direction.
result Inflation of earnings by 100-200% while reducing fraud scores by 15% in 50% of cases.
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.
GNN-CL model improves financial fraud detection using graph neural networks and reinforcement learning.
problem Complex financial fraud detection with high false positive rates.
method Combines GNN, CNN, LSTM, and MLPS to analyze transaction patterns and uses reinforcement learning to filter out noise.
result GNN-CL outperforms existing methods in financial fraud detection, reducing false positives.
SAGE-FIN detects financial fraud using GNNs and Granger causality.
problem Detecting fraud in financial networks with limited labeled data and lack of explainability.
method Semi-supervised GNN approach with Granger causal explanations.
result SAGE-FIN outperforms on real-world financial network dataset with explainable flagged items.
A new GCN model detects cryptocurrency fraud by considering network evolution and balance theory.
problem Detecting fraud in evolving signed cryptocurrency trust networks.
method Motif-aware temporal GCN using balance theory and learnable weights.
result The model outperforms existing methods on bitcoin datasets.
Due to the popularity of the Internet and smart mobile devices, more and more financial transactions and activities have been digitalized. Compared to traditional financial fraud detection strategies using credit-related features, customers are generating a large amount of unstructured behavioral data every second. In …
With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract some features manually to perform prediction. However, in financial se…
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.
AI-driven framework improves enterprise financial audits and risk identification.
problem Manual auditing is inefficient and limited by data complexity and evolving fraud tactics.
method Machine learning algorithms (SVM, RF, KNN) applied to a dataset of audit project counts, violations, and fraud instances.
result Random Forest achieves best performance with F1-score of 0.9012, identifying fraud and compliance anomalies.
Bayesian framework improves financial risk management and compliance.
problem Uncertainty in financial risk forecasting and compliance.
method Integrated Bayesian analytics framework for precise uncertainty quantification.
result Proposed DLM model produces more accurate VaR estimates compared to baseline models.
Paper introduces a new optimization method for imbalanced datasets.
problem Overfitting in imbalanced datasets, especially in financial fraud detection.
method Fractional Weyl Integral optimization algorithm.
result Significantly improved performance in financial fraud detection (40% improvement in PR-AUC).
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. …
EDINET-Bench evaluates LLMs on complex financial tasks using Japanese financial statements.
problem Challenges in evaluating LLMs on financial tasks due to specialized expertise and scarce benchmarks.
method Developed EDINET-Bench, an open-source Japanese financial benchmark for LLMs on tasks like fraud detection and earnings forecasting.
result State-of-the-art LLMs perform only marginally better than logistic regression in financial tasks, highlighting the need for more realistic benchmarks.
Corporate governance struggles to curb fraud in a globalized economy.
problem Lack of effective international regulations against corporate fraud.
method Analyzes historical economic crises and the role of corporate governance.
result Corporate governance is insufficient to prevent large business fraud.
TabNet improves fraud detection in bank transactions.
problem Detecting fraudulent bank transactions using deep learning.
method TabNet, SMOTE, EDA, 5 deep learning architectures, 3-fold cross-validation.
result TabNet achieved 97.39% accuracy and 0.9739 ROC-AUC.
Study improves fraud detection in e-commerce with a stacked model combining CNNs, GNNs, and confidence gating.
problem Detecting credit card fraud in online transactions.
method Stacking approach with attention and confidence-driven layers, using DOWA and IOWA operators.
result The method achieves high accuracy and robust generalization in CCF detection.
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.
Machine learning has automated much of financial fraud detection, notifying firms of, or even blocking, questionable transactions instantly. However, data imbalance starves traditionally trained models of the content necessary to detect fraud. This study examines three separate factors of credit card fraud detection vi…
Survey of determinism issues in financial AI systems.
problem Vulnerabilities in reproducibility of financial AI systems.
method Literature review and first-party experiments on public financial datasets.
result Proposed a layered evaluation framework linking modality-specific metrics to audit readiness.
PRAGMA models financial event sequences for various banking tasks.
problem Handling diverse financial data for multiple applications.
method Pre-training a Transformer model on a large banking event corpus with a self-supervised objective.
result PRAGMA achieves superior performance across multiple financial domains from raw event sequences.
The paper proposes a machine learning framework for detecting DeFi fraud across multiple blockchain chains.
problem Early detection of financial crimes in decentralized finance (DeFi) ecosystems.
method Extracting features from different blockchain chains, employing XGBoost and Neural Network for fraud detection.
result Introduction of novel DeFi-related features significantly improves fraud detection accuracy.
A hybrid ML model detects fraudulent transactions with high accuracy.
problem Detecting and preventing fraudulent credit card transactions.
method Intelligent combination of multiple algorithms with Grid search and IHT-LR.
result Achieves impressive accuracy rates of 99.66% for ENS model.
InfDetect detects e-commerce insurance fraud using graph analysis.
problem Detecting fraudulent claims in e-commerce insurance with multiple parties involved.
method Developed a large-scale fraud detection system InfDetect using graph-based approaches.
result InfDetect successfully detected thousands of fraudulent claims and saved money daily.
Secure federated learning reduces privacy risks with differential privacy and secure multiparty computation.
problem Reverse engineering of private client data from federated learning model parameters.
method Combining differential privacy and secure multiparty computation.
result Improved accuracy of shared models without significant privacy loss.
Semi-supervised GANs with log-signatures improve credit card fraud detection.
problem Detecting fraud in large, complex financial transaction data streams.
method Conditional GANs with Bayesian inference and log-signatures for robust feature encoding.
result Consistent improvements over benchmarks in global and domain-specific metrics.
Fraud detection is essential in financial services, with the potential of greatly reducing criminal activities and saving considerable resources for businesses and customers. We address online fraud detection, which consists of classifying incoming transactions as either legitimate or fraudulent in real-time. Modern fr…
Fraudulent claim detection is one of the greatest challenges the insurance industry faces. Alibaba's return-freight insurance, providing return-shipping postage compensations over product return on the e-commerce platform, receives thousands of potentially fraudulent claims every day. Such deliberate abuse of the insur…
Paper aims to use AI for detecting financial crimes, focusing on money laundering.
problem Financial institutions need better technologies to detect and predict financial crimes.
method Study recent works, develop a novel model for money laundering detection.
result Demonstrates a model for detecting money laundering cases with minimal human intervention.
In general, anomaly detection is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. Financial transactions, customer reviews, social media posts are all characterized by relational information. In these networks, fra…
The changing nature of the relationship between a retail bank and its customers is examined, particularly with respect to new financial concepts, debt and regulation. The traditional image of a bank is portrayed as a physical building a classical Doric portico. This image conveys concepts of service, soundness, strengt…
Paper generates synthetic financial transactions for AML model testing.
problem Insufficient real data for AML model training.
method Agent-based generator calibrated to real transactions.
result Synthetic datasets improve model comparison by providing complete ground truth.