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.
Model predicts missing boarding stops in smart card data.
Paper predicts in-situ metro passenger density using smart card data.
Investigates the cost-effectiveness of security features in smart card chips.
TripDecoder recovers metro routes and travel times from smart card data.
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…
Semi-supervised GANs with log-signatures improve credit card fraud detection.
Study evaluates SHAP for credit card default model consistency.
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…
Historically, games of all kinds have often been the subject of study in scientific works of Computer Science, including the field of machine learning. By using machine learning techniques and applying them to a game with defined rules or a structured dataset, it's possible to learn and improve on the already existing …
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…
Study optimizes classifiers for credit card mail campaigns and default prediction.
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-…
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…
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…
Expert system predicts credit card charge-offs using macroeconomic indicators.
CARD detects treatment responders with machine learning and adjustment.
The study reveals fundamental limits of fraud detection in card payment networks.
The paper proposes a method to detect credit card fraud using sparse Gaussian approximations.
Study evaluates AD methods for fraud detection in online credit card payments.
Research examines motivations and factors influencing retailers' payment method choices.
FinTech uses data science and AI to transform finance.
CaT-GNN improves credit card fraud detection by integrating causal reasoning into GNNs.
Wrist movements can reveal digits, posing security risks.
Future buildings will offer new convenience, comfort, and efficiency possibilities to their residents. Changes will occur to the way people live as technology involves into people's lives and information processing is fully integrated into their daily living activities and objects. The future expectation of smart build…
SMART is an open source web application designed to help data scientists and research teams efficiently build labeled training data sets for supervised machine learning tasks. SMART provides users with an intuitive interface for creating labeled data sets, supports active learning to help reduce the required amount of …
Public special events, like sports games, concerts and festivals are well known to create disruptions in transportation systems, often catching the operators by surprise. Although these are usually planned well in advance, their impact is difficult to predict, even when organisers and transportation operators coordinat…
Paper assesses the market value of sharing privacy-protected smart meter data.
CARD models predict the distribution of continuous or categorical responses.
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…
BreachRadar detects points-of-compromise in bank transactions to prevent fraud.
This paper proposes a joint energy and data market to handle uncertainty in energy procurement.
Paper addresses class imbalance in disk SMART dataset using GANs and genetic algorithms.
Study develops a new model for predicting individual mobility based on activity patterns.
This paper summarizes AI methods for detecting credit card fraud.
Differences in data size per class, also known as imbalanced data distribution, have become a common problem affecting data quality. Big Data scenarios pose a new challenge to traditional imbalanced classification algorithms, since they are not prepared to work with such amount of data. Split data strategies and lack o…
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…
Deep learning enhances smart fish farming through automated feature extraction.
We introduce a new virtual environment for simulating a card game known as "Big 2". This is a four-player game of imperfect information with a relatively complicated action space (being allowed to play 1,2,3,4 or 5 card combinations from an initial starting hand of 13 cards). As such it poses a challenge for many curre…
DAMVI algorithm improves imbalanced binary classification by adjusting weights of examples and classifiers.
CARDS improves decoding efficiency and alignment quality for LLMs.
Study Figgie card game strategies using agent-based simulation.
A recurring problem faced when training neural networks is that there is typically not enough data to maximize the generalization capability of deep neural networks(DNN). There are many techniques to address this, including data augmentation, dropout, and transfer learning. In this paper, we introduce an additional met…
Proposes a probabilistic framework for smart contract risk quantification.
Smart Close-out Netting aims to automate close-out netting processes.
Smart reply systems have been developed for various messaging platforms. In this paper, we introduce Uber's smart reply system: one-click-chat (OCC), which is a key enhanced feature on top of the Uber in-app chat system. It enables driver-partners to quickly respond to rider messages using smart replies. The smart repl…
Paper proposes auction method for smart derivatives to avoid disputes.