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 AD methods for fraud detection in online credit card payments.
Research examines motivations and factors influencing retailers' payment method choices.
This paper builds a machine learning model to predict credit defaults for unsecured lending.
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…
The study reveals fundamental limits of fraud detection in card payment networks.
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…
The paper assesses fairness in AI for financial services, using statistical methods.
The aim of this paper is to get an overview of the online buyer profile, and also some key aspects in the way the online shopping is conducted. In this project we conducted a quantitative research, consisting of a questionnaire based survey. For data processing and interpretation we used SPSS statistical software and E…
Adaptive Stress Testing detects financial fraud by simulating potential failures.
Study evaluates SHAP for credit card default model consistency.
Stablecoins offer efficient settlement but externalize costs and risks.
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…
Standard economic theory, starting with Adam Smith's invisible hand, holds that those who trade for their own selfish motives of maximizing their private preferences may contribute more to the public wealth than those who claim altruistic motives. Under restrictive conditions, this has been shown to result from a self-…
Study uses synthetic data to estimate credit risk for underbanked consumers in Istanbul.
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 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.
This paper summarizes AI methods for detecting credit card fraud.
CaT-GNN improves credit card fraud detection by integrating causal reasoning into GNNs.
Interleaved RNNs detect fraud without costly features.
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.
Study improves fraud detection in e-commerce with a stacked model combining CNNs, GNNs, and confidence gating.
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.
In order to scale transaction rates for deployment across the global web, many cryptocurrencies have deployed so-called "Layer-2" networks of private payment channels. An idealized payment network behaves like a Credit Network, a model for transactions across a network of bilateral trust relationships. Credit Networks …
Distributionally Robust Optimization (DRO) has been shown to provide a flexible framework for decision making under uncertainty and statistical estimation. For example, recent works in DRO have shown that popular statistical estimators can be interpreted as the solutions of suitable formulated data-driven DRO problems.…
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.
Deep learning improves credit risk assessment without new data.
AI framework predicts invoice dilution in supply chain finance.
This paper takes a deep learning approach to understand consumer credit risk when e-commerce platforms issue unsecured credit to finance customers' purchase. The "NeuCredit" model can capture both serial dependences in multi-dimensional time series data when event frequencies in each dimension differ. It also captures …
DAMVI algorithm improves imbalanced binary classification by adjusting weights of examples and classifiers.
Improved hardness results for clearing payments in financial networks with CDSs.
An adversarial detector identifies anomalous sequences in sequential data.
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…
Analyzes how financial network dependencies can lead to multiple equilibrium outcomes and optimal bailout strategies.
Paper introduces balanced payment systems to improve liquidity and risk management.
Credit networks represent a way of modeling trust between entities in a network. Nodes in the network print their own currency and trust each other for a certain amount of each other's currency. This allows the network to serve as a decentralized payment infrastructure---arbitrary payments can be routed through the net…
IA-BMA adapts model weights to inputs for better predictions.
We study dynamic hedging of counterparty risk for a portfolio of credit derivatives. Our empirically driven credit model consists of interacting default intensities which ramp up and then decay after the occurrence of credit events. Using the Galtchouk-Kunita-Watanabe decomposition of the counterparty risk price paymen…
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.
The performance of classification algorithms with a massive and highly imbalanced data stream depends upon efficient balancing strategy. Some techniques of balancing strategy have been applied in the past with Batch data to resolve the class imbalance problem. This paper proposes a new incremental data balancing framew…