Study builds labeled shill bidding dataset for auction fraud detection.
problem Difficulty in detecting shill bidding in auctions.
method Hierarchical clustering CURE for systematic labeling of fraud data.
result CURE approach effectively labels shill bidding instances with multidimensional features.
In the last three decades, we have seen a significant increase in trading goods and services through online auctions. However, this business created an attractive environment for malicious moneymakers who can commit different types of fraud activities, such as Shill Bidding (SB). The latter is predominant across many a…
Exact solution found for k-price auctions, introducing a new fair auction type.
problem Finding fair solutions for auction mechanisms.
method Exact analytical solution for k-price auctions and introduction of a new auction type.
result New auction type provides fair solutions.
Optimizes bidding strategy for Maker Protocol auctions.
problem Minimizing costs in Maker Protocol auctions.
method Developed and optimized a bidding function using historical data.
result Most auctions end at higher prices than optimal recommendations.
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.
Equity auctions show linear price impact up to a large volume, then non-linear.
problem Understanding price impact in equity auctions.
method High-quality data analysis of Paris stock exchange auctions.
result Price impact is linear up to a large volume, then becomes non-linear.
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.
The paper tackles auction market design flaws by randomizing closing times and optimizing transaction fees.
problem Strategic traders exploit accumulated information to delay their orders, distorting auction efficiency.
method Randomizing auction closing times and designing optimal transaction fees policies.
result Policies encourage strategic traders to send orders earlier, improving auction market efficiency.
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.
The study compares uniform-price and discriminatory auctions in terms of learning difficulty.
problem Comparing the learning difficulty of uniform-price and discriminatory multi-unit auctions.
method Characterization of learning difficulty through regret minimization in both full-information and bandit feedback settings.
result Regret scales similarly for both auction formats under full-information, but uniform-price auctions can achieve faster learning rates.
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.
We first investigate the evolution of opening and closing auctions volumes of US equities along the years. We then report dynamical properties of pre-auction periods: the indicative match price is strongly mean-reverting because the imbalance is; the final auction price reacts to a single auction order placement or can…
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.
AHEAD improves financial market efficiency through ad-hoc auctions.
problem Improving financial market efficiency and reducing transaction costs.
method Introducing a new matching design (AHEAD) for electronic markets where participants can trade at a fixed price and trigger auctions when unsatisfied.
result A Nash equilibrium is achieved in the market, and ad-hoc auctions are more relevant and efficient than periodic auctions and continuous limit order books.
Study on heavy tails in closing auction returns, explaining imbalance through limit order submission.
problem Understanding heavy tails in closing auction return distributions.
method Used the stochastic call auction model of Derksen et al. (2020a) to derive and verify a relation between tail exponents.
result Large closing price fluctuations are not caused by large market orders, but by imbalance in limit orders.
Study proposes a machine learning method for bid shading in first-price auctions.
problem Maintaining strategy equilibrium in first-price auctions.
method Machine learning approach to model optimal bid shading.
result Demonstrates superiority and robustness of new approach across various metrics.
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.
A new approach to fraud detection minimizes human intervention.
problem Minimizing the number of non-fraudulent operations requiring human verification.
method Computer-assisted fraud detection using a meta-algorithm to minimize oracle calls.
result A simple meta-algorithm provides competitive results in minimizing non-fraudulent operations.
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.
Study on efficiency of Dutch auctions on blockchains considering various parameters.
problem Efficiency and fairness in Dutch auctions on blockchains.
method Modeling Dutch auctions with Poisson process and geometric Brownian motion, computing expected losses and time-to-fill.
result Tradeoff between speed and quality in Dutch auctions, useful for setting parameters.
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.
Develops auction theory for real-life applications with positive valuations.
problem Real-life auction settings with positive valuations and interdependent bidders.
method Approximations using log-normal distribution, positive symmetric discrete distribution, and interdependent valuations.
result New auction theory results applicable to finance and procurement.
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.
New method for causal inference in RTB advertising auctions.
problem Measuring the effectiveness of online advertising in RTB systems.
method Adapted Thompson sampling algorithm for causal inference.
result The method outperforms existing methods in estimating advertising effects.
Optimal bidding strategy for multi-platform ad auctions under budget constraints.
problem Optimizing ad placements for budget-constrained advertisers across multiple platforms.
method Developed an optimal bidding strategy for non-incentive-compatible auctions with budget constraints.
result Maximized total utility across auctions while satisfying budget constraints in expectation.
Paper addresses selection bias in online advertising auctions.
problem Selection bias affects auction truthfulness and advertiser profits.
method Theoretical analysis combined with multi-task learning.
result Selection bias can be significantly reduced using multi-task learning.
MiFID II impacts European stock liquidity and price formation.
problem Impact of MiFID II on European stock liquidity and price formation.
method Analyzed effects of MiFID II on European stock markets, focusing on intraday and closing auction liquidity and tick size changes.
result Closing auction volumes increased and price formation became more efficient after MiFID II.
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.
New algorithm optimizes auction prices in real-time.
problem Maximizing revenue in online auctions with high frequency data.
method First real-time algorithm for online learning of monopoly prices.
result Achieves constant time and memory complexity for updates.
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.
Study adapts liquidity model to equity auctions, revealing accelerated event rates and reduced price impact.
problem Understanding and predicting price dynamics in equity auctions.
method Adapted latent/revealed order book framework to equity auctions, measuring order submissions, cancellations, and diffusion rates.
result Equity auctions exhibit accelerated event rates leading to reduced price impact and decreased volatility.
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.
Proposes a framework for modeling RTB auctions using point processes.
problem Modeling and optimizing repeated auctions in the RTB ecosystem.
method Develops a stochastic framework using point processes to model and optimize RTB auctions.
result The proposed framework can be approximated to a Poisson point process, enabling the use of established properties.
The paper examines how builders in Ethereum auctions can defect and replicate winning MEV opportunities, affecting searchers' bidding strategies.
problem Commitment problem in Ethereum auctions where builders can defect and replicate winning MEV opportunities.
method Modeling and analysis of searchers' bidding strategies and the resulting equilibrium, using libMEV dataset.
result The equilibrium is piecewise, with the cost of imperfect commitment depending on replicability and competition. There is sharp heterogeneity across MEV types.
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 …
Paper proposes auction method for smart derivatives to avoid disputes.
problem Disputes over derivative liquidation processes in smart contracts.
method Defines an auction type resolution for smart derivatives.
result Proposes a beneficial method for smart derivatives participants.
Deep learning model predicts online fraud using customer behavior data.
problem Predicting online financial fraud from customer behavior data.
method Recurrent Neural Network (RNN) integrated with Markov Transition Field (MTF).
result The proposed model significantly improves fraud prediction compared to traditional methods.
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.
We consider a class of auctions (Lowest Unique Bid Auctions) that have achieved a considerable success on the Internet. Bids are made in cents (of euro) and every bidder can bid as many numbers as she wants. The lowest unique bid wins the auction. Every bid has a fixed cost, and once a participant makes a bid, she gets…
Optimal auction duration affects price formation in markets.
problem Improving price formation in auction markets.
method Derived the optimal auction duration and analyzed its impact on price formation.
result Optimal auction durations are from 2 to 10 minutes, improving price formation.
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.