Credit card fraud detection is a critical challenge in the financial sector, demanding sophisticated approaches to accurately identify fraudulent transactions. This research proposes an innovative methodology combining Neural Networks (NN) and Synthet ic Minority Over-sampling Technique (SMOTE) to enhance the detection performance. The study addresses the inherent imbalance in credit card transaction data, focusing on technical advancements for robust and precise fraud detection. Results demonstrat e that the integration of NN and SMOTE exhibits superior precision, recall, and F1-score compared to traditional models, highlighting its potential as an advanced solution for handling imbalanced datasets in credit card fraud detection scenarios. This rese arch contributes to the ongoing efforts to develop effective and efficient mechanisms for safeguarding financial transactions from fraudulent activities.

Devising procedures for downstream task-oriented generative model selections is an unresolved problem of practical importance. Existing studies focused on the utility of a single family of generative models. They provided limited insights on how synthetic data practitioners select the best family generative models for synthetic training tasks given a specific combination of machine learning model class and performance metric. In this paper, we approach the downstream task-oriented generative model selections problem in the case of training fraud detection models and investigate the best practice given different combinations of model interpretability and model performance constraints. Our investigation supports that, while both Neural Network(NN)-based and Bayesian Network(BN)-based generative models are both good to complete synthetic training task under loose model interpretability constrain, the BN-based generative models is better than NN-based when synthetic training fraud detection model under strict model interpretability constrain. Our results provides practical guidance for machine learning practitioner who is interested in replacing their training dataset from real to synthetic, and shed lights on more general downstream task-oriented generative model selection problems.

Exploring generative model training for synthetic tabular data, specifically in sequential contexts such as credit card transaction data, presents significant challenges. This paper addresses these challenges, focusing on attaining both high fidelity to actual data and optimal utility for machine learning tasks. We introduce five pre-processing schemas to enhance the training of the Conditional Probabilistic Auto-Regressive Model (CPAR), demonstrating incremental improvements in the synthetic data's fidelity and utility. Upon achieving satisfactory fidelity levels, our attention shifts to training fraud detection models tailored for time-series data, evaluating the utility of the synthetic data. Our findings offer valuable insights and practical guidelines for synthetic data practitioners in the finance sector, transitioning from real to synthetic datasets for training purposes, and illuminating broader methodologies for synthesizing credit card transaction time series.

Traditionally, the detection of fraudulent insurance claims relies on business rules and expert judgement which makes it a time-consuming and expensive process (\'Oskarsd\'ottir et al., 2022). Consequently, researchers have been examining ways to develop efficient and accurate analytic strategies to flag suspicious claims. Feeding learning methods with features engineered from the social network of parties involved in a claim is a particularly promising strategy (see for example Van Vlasselaer et al. (2016); Tumminello et al. (2023)). When developing a fraud detection model, however, we are confronted with several challenges. The uncommon nature of fraud, for example, creates a high class imbalance which complicates the development of well performing analytic classification models. In addition, only a small number of claims are investigated and get a label, which results in a large corpus of unlabeled data. Yet another challenge is the lack of publicly available data. This hinders not only the development of new methods, but also the validation of existing techniques. We therefore design a simulation machine that is engineered to create synthetic data with a network structure and available covariates similar to the real life insurance fraud data set analyzed in \'Oskarsd\'ottir et al. (2022). Further, the user has control over several data-generating mechanisms. We can specify the total number of policyholders and parties, the desired level of imbalance and the (effect size of the) features in the fraud generating model. As such, the simulation engine enables researchers and practitioners to examine several methodological challenges as well as to test their (development strategy of) insurance fraud detection models in a range of different settings. Moreover, large synthetic data sets can be generated to evaluate the predictive performance of (advanced) machine learning techniques.

We're a fraud detection company using advanced machine learning and network analysis technology to solve big problems. Our goal is to make online transactions safer and help our clients feel confident serving their customers. And we have fun in the meantime! We are a friendly bunch and pride ourselves in having a strong culture and adhering to our values of empathy, ambition, unity and integrity. We really value work/life balance and we embrace a flat hierarchy structure company-wide.

Accuracy: Of all the classes, how many you predicted right. Accuracy is simply the fraction of the total sample that is correctly identified. Precision: Out of all the classes we have predicted as positive, how many are actually positive. Precision is very useful when you have a model that starts some kind of business workflow (e.g. So, you want your model to be as correct as possible when it says 1 and don't care too much when it predicts 0. That's why we see only the second column of the confusion matrix, which is related to a prediction equal to 1. Precision is very used in marketing campaigns, because a marketing automation campaign is supposed to start an activity on a user when it predicts that they will respond successfully.

The meager supply and high salaries of data scientists have led to a decision among many companies totally in keeping with artificial intelligence ― to automate whatever is possible. Case in point is machine learning. A Forrester study found that automated machine learning (AutoML) has been adopted by 61% of data and analytics decision makers in companies using AI, with another 25% of companies saying they'll do so in the next year. Automated machine learning (AutoML) automates repetitive and manual machine learning tasks. That's no small thing, especially when data scientists and data analysts now spend a majority of their time cleaning, sourcing, and preparing data.

When credit card giant American Express began offering bank accounts for the first time last year, it had a foundation of fraud detection to bring to an entirely new product arena. That meant in some cases, the company could port over AI and machine-learning models used to spot phony identities or dodgy transactions for its credit card products to its consumer and business checking accounts. But it's been a process, and now, AmEx plans to invest in bringing additional AI techniques used to protect against credit card fraud to its banking products. "We have models which run to detect whether it's you or whether somebody else is logging into your account. Very straightforwardly, we transferred it to the banking product," said Abhinav Jain, vice president for Global Fraud Decision Science at AmEx, who is responsible for the company's fraud detection models.

Fraud is one of the major issues we come up majorly in banks, life insurance, health insurance, and many others. These major frauds are dependent on the person who is trying to sell you the fake product or service, if you are matured enough to decide what is wrong then you will never get into any fraud transactions. But one such fraud that has been increasing a lot these days is fraud in making payments. In this article, I will take you through a solution to fraud detection with machine learning. The dataset that I will use for this task can be easily downloaded from here.

The traditional method of training AI models involves setting up servers where models are trained on data, often through the use of a cloud-based computing platform. However, over the past few years an alternative form of model creation has arisen, called federated learning. Federated learning brings machine learning models to the data source, rather than bringing the data to the model. Federated learning links together multiple computational devices into a decentralized system that allows the individual devices that collect data to assist in training the model. In a federated learning system, the various devices that are part of the learning network each have a copy of the model on the device.