A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
BreachRadar detects points-of-compromise in bank transactions to prevent fraud.
This paper improves fraud prevention rule sets in fintech by generating diverse rules and finding Pareto-optimal subsets.
This paper examines anomalies and frauds in blockchain networks and proposes detection techniques.
Relational Graph Neural Networks improve fraud detection in Super-Apps.
DBDT uses deep boosting decision trees for fraud detection.
Fraud detection is extremely critical for e-commerce business. It is the intent of the companies to detect and prevent fraud as early as possible. Existing fraud detection methods try to identify unexpected dense subgraphs and treat related nodes as suspicious. Spectral relaxation-based methods solve the problem effici…
Detects organized fraudsters in insurance claims with high precision.
Many online platforms have deployed anti-fraud systems to detect and prevent fraudulent activities. However, there is usually a gap between the time that a user commits a fraudulent action and the time that the user is suspended by the platform. How to detect fraudsters in time is a challenging problem. Most of the exi…
New method improves false-/true-positive-rate estimation in fraud detection with noisy labels.
A hybrid ML model detects fraudulent transactions with high accuracy.
Paper introduces a new optimization method for imbalanced datasets.
Often the challenge associated with tasks like fraud and spam detection is the lack of all likely patterns needed to train suitable supervised learning models. This problem accentuates when the fraudulent patterns are not only scarce, they also change over time. Change in fraudulent pattern is because fraudsters contin…
We develop a model for contagion in reinsurance networks by which primary insurers' losses are spread through the network. Our model handles general reinsurance contracts, such as typical excess of loss contracts. We show that simpler models existing in the literature--namely proportional reinsurance--greatly underesti…
Model detects insurance fraud using social network analysis.
Paper introduces a fraud detection dataset benchmark.
New taxonomy reveals different detection limits for various types of fraud.
Online retail, eCommerce, frequently falls victim to fraud conducted by malicious customers (fraudsters) who obtain goods or services through deception. Fraud coordinated by groups of professional fraudsters that place several fraudulent orders to maximize their gain is referred to as organized fraud. Existing approach…
Graph Neural Networks improve financial fraud detection.
New method detects corporate fraud in noisy financial networks.
New algorithm improves fraud detection by analyzing financial account relationships.
Novelty detection is the unsupervised problem of identifying anomalies in test data which significantly differ from the training set. Novelty detection is one of the classic challenges in Machine Learning and a core component of several research areas such as fraud detection, intrusion detection, medical diagnosis, dat…
TimeTrail detects financial fraud patterns through temporal correlation analysis.
Quantum Support Vector Classifier outperforms other QML models in finance fraud detection.
This paper develops a dynamic internal fraud model for operational losses in retail banking. It considers public operational losses arising from internal fraud in retail banking within a group of international banks. Additionally, the model takes into account internal factors such as the ethical quality of workers and …
ARIMA model detects credit card fraud in unbalanced datasets.
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 …
The credit cards' fraud transactions detection is the important problem in machine learning field. To detect the credit cards's fraud transactions help reduce the significant loss of the credit cards' holders and the banks. To detect the credit cards' fraud transactions, data scientists normally employ the unsupervised…
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. …
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…
CaT-GNN improves credit card fraud detection by integrating causal reasoning into GNNs.
The identification of anomalies in temporal data is a core component of numerous research areas such as intrusion detection, fault prevention, genomics and fraud detection. This article provides an experimental comparison of the novelty detection problem applied to discrete sequences. The objective of this study is to …
Payment card fraud causes multibillion dollar losses for banks and merchants worldwide, often fueling complex criminal activities. To address this, many real-time fraud detection systems use tree-based models, demanding complex feature engineering systems to efficiently enrich transactions with historical data while co…
Study uses stacked generalization to improve fraud detection algorithms.
SemiGNN detects financial fraud using social relations and multi-view data.
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…
Unsupervised model detects healthcare fraud from patient visit data.
Deep semi-supervised anomaly detection improves fraud detection in financial markets.
Study evaluates AD methods for fraud detection in online credit card payments.
FraudTransformer detects payment fraud by preserving event order and time gaps.
A new method detects financial fraud using graph transformers.
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…
Adaptive Stress Testing detects financial fraud by simulating potential failures.
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 …
robROSE tackles imbalanced fraud data by creating synthetic samples and detecting outliers.
This paper summarizes AI methods for detecting credit card fraud.
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…
QFNN-FFD uses quantum computing and FL for secure financial fraud detection.