Kaggle Competition: Facebook Recruiting IV: Human or Robot?
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Updated
Mar 12, 2016 - OpenEdge ABL
Kaggle Competition: Facebook Recruiting IV: Human or Robot?
Using simple logistic regression to predict credit card fraud transactions
Doctor Patient Drugstore Management blockchain
Analyzing data and provide insights on Financial Fraud Detection using Spark ML.
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Find phishing kits which use your brand/organization's files and image.
Suspicidy aims to detect suspicious web requests
Detection of Accounting Anomalies using Deep Autoencoder Neural Networks - A lab we prepared for NVIDIA's GPU Technology Conference 2018 that will walk you through the detection of accounting anomalies using deep autoencoder neural networks. The majority of the lab content is based on Jupyter Notebook, Python and PyTorch.
using HMM to detect credit card fraudulent transaction
Detection of Accounting Anomalies in the Latent Space using Adversarial Autoencoder Neural Networks - A lab we prepared for the KDD'19 Workshop on Anomaly Detection in Finance that will walk you through the detection of interpretable accounting anomalies using adversarial autoencoder neural networks. The majority of the lab content is based on J…
Detection of fraudulent transactions from IEEE Kaggle Dataset
Modelagem para detecção de fraudes em cartões de crédito
FraudLabs Pro Fraud Prevention plugin that screen the order transaction for online frauds. Fraud Prevention extension for Magento 1.
Fraud prevention tool
Can we predict accurately on the skewed data? What are the sampling techniques that can be used. Which models/techniques can be used in this scenario? Find the answers in this code pattern!
This repository contains the implementation of Self organising maps for fraud detection.
It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Here you will find the easiest method for detection of fraud credit card.
Although digital transactions in India registered a 51% growth in 2018-19, their safety remains a concern. Fraudulent activities have increased severalfold, with around 52,304 cases of credit/debit card fraud reported in FY'19 alone. Due to this steep increase in banking frauds, it is the need of the hour to detect these fraudulent transactions …
Predictive modeling projects developed during the Risk & Fraud Analytics course (Master in Business Analytics & Big Data) at IE HST.
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