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.
Study evaluates SHAP for credit card default model consistency.
Study optimizes classifiers for credit card mail campaigns and default prediction.
ARIMA model detects credit card fraud in unbalanced datasets.
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…
We present a model of credit card profitability, assuming that the card-holder always pays the full outstanding balance. The motivation for the model is to calculate an optimal credit limit, which requires an expression for the expected outstanding balance. We derive its Laplace transform, assuming that purchases are m…
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this article, we model a sequence of credit card transactions from three different perspectiv…
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However purchase behaviour and fraudster strategies may change over time. This phenomenon is named dataset shift or concept drift in the domain of fraud detection. In this paper, we present a method to quantify…
Expert system predicts credit card charge-offs using macroeconomic indicators.
Semi-supervised GANs with log-signatures improve credit card fraud detection.
Machine learning and data mining techniques have been used extensively in order to detect credit card frauds. However, most studies consider credit card transactions as isolated events and not as a sequence of transactions. In this framework, we model a sequence of credit card transactions from three different perspect…
This paper summarizes AI methods for detecting credit card fraud.
Study evaluates AD methods for fraud detection in online credit card payments.
CaT-GNN improves credit card fraud detection by integrating causal reasoning into GNNs.
Assessment of risk levels for existing credit accounts is important to the implementation of bank policies and offering financial products. This paper uses cluster analysis of behaviour of credit card accounts to help assess credit risk level. Account behaviour is modelled parametrically and we then implement the behav…
The paper proposes a method to detect credit card fraud using sparse Gaussian approximations.
This paper builds a machine learning model to predict credit defaults for unsecured lending.
In this study, we employ Generative Adversarial Networks as an oversampling method to generate artificial data to assist with the classification of credit card fraudulent transactions. GANs is a generative model based on the idea of game theory, in which a generator G and a discriminator D are trying to outsmart each o…
NetDP predicts loan defaults using network data, addressing cold-start issues.
Paper proposes an intelligent credit limit management system using causal inference.
Credit risk modelling is an integral part of the global financial system. While there has been great attention paid to neural network models for credit default prediction, such models often lack the required interpretation mechanisms and measures of the uncertainty around their predictions. This work develops and compa…
Enhances credit card limit adjustments by considering treatment uncertainty and prediction criteria.
DAMVI algorithm improves imbalanced binary classification by adjusting weights of examples and classifiers.
Money flow models are essential tools to understand different economical phenomena, like saving propensities and wealth distributions. In spite of their importance, most of them are based on synthetic transaction networks with simple topologies, e.g. random or scale-free ones, as the characterisation of real networks i…
Adaptive Stress Testing detects financial fraud by simulating potential failures.
IA-BMA adapts model weights to inputs for better predictions.
Study improves fraud detection in e-commerce with a stacked model combining CNNs, GNNs, and confidence gating.
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…
A hybrid ML model detects fraudulent transactions with high accuracy.
Paper tackles transparency and auditability of machine learning in credit scoring.
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…
Research examines motivations and factors influencing retailers' payment method choices.
Study uses RL to optimize credit card limits, achieving better results than traditional methods.
Simple tabular event prediction model outperforms existing methods.
BreachRadar detects points-of-compromise in bank transactions to prevent fraud.
Machine learning improves joint default assessment by capturing non-linear dependencies.
Secure federated learning reduces privacy risks with differential privacy and secure multiparty computation.
This study surveys methods for detecting outliers in spatial data.
Deep learning improves credit risk assessment without new data.
Credit scoring is without a doubt one of the oldest applications of analytics. In recent years, a multitude of sophisticated classification techniques have been developed to improve the statistical performance of credit scoring models. Instead of focusing on the techniques themselves, this paper leverages alternative d…
We propose a general model explanation system (MES) for "explaining" the output of black box classifiers. This paper describes extensions to Turner (2015), which is referred to frequently in the text. We use the motivating example of a classifier trained to detect fraud in a credit card transaction history. The key asp…
This study compares logistic regression and XGBoost for predicting credit risk.
Auto-encoders are among the most popular neural network architecture for dimension reduction. They are composed of two parts: the encoder which maps the model distribution to a latent manifold and the decoder which maps the latent manifold to a reconstructed distribution. However, auto-encoders are known to provoke cha…
Artificial Intelligence (AI) is an important driving force for the development and transformation of the financial industry. However, with the fast-evolving AI technology and application, unintentional bias, insufficient model validation, immature contingency plan and other underestimated threats may expose the company…
Study uses synthetic data to estimate credit risk for underbanked consumers in Istanbul.
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…
Imbalanced data classification problem has always been a popular topic in the field of machine learning research. In order to balance the samples between majority and minority class. Oversampling algorithm is used to synthesize new minority class samples, but it could bring in noise. Pointing to the noise problems, thi…
We consider the sequential anomaly detection problem in the one-class setting when only the anomalous sequences are available and propose an adversarial sequential detector by solving a minimax problem to find an optimal detector against the worst-case sequences from a generator. The generator captures the dependence i…
Study explores fairness in financial deep learning through multi-scale trust quantification.