Fraudulent websites detector built with nonparametric models

Built LDA and QDA models on variables obtained from Principal Component Analysis (PCA) and Kolmogorov-Smirnov (KS) and tuned by leave-one-out cross-validation (LOOCV) to predict fraudulent online advertising click traffic

Script to predict fraud clicks in ads.

Fraud analysis and detection.

HarvardX Data Science Certificate: Choose Your Own Project

Human or Robot? Predict if an online bid is made by a machine or a human.

h2o is a ML library that can be used with R and Python. Here are some R examples for supervised and unsupervised methods.

Big Data Analytics course work at Harper College: Proof of concept - Fraud Detection

The repository contains implementations of different univariate outlier detection algorithms

A comprehensive machine learning approach to fraud detection combining unsupervised clustering, supervised classification, and financial market analysis to identify fraudulent transactions in real-time banking data.

A Wald statistic for group-level cheating detection

The aim of this project is to build a classifier that can detect credit card fraudulent transactions. I used a variety of machine learning algorithms like Decision Trees, Logistic Regression, Artificial Neural Networks and finally, Gradient Boosting Classifier

The Wirecard scandal is considered one of the largest financial scandals of the decade, which caused losses of several billion euros. This analysis examines the digit structure of Wirecard's financial figures in the period from 2005 to 2019 by analyzing the conformity with the expected frequency distributions according to Benford's law. The resu…

This is an UPDATED deep statistical data analysis of the 2020 Presidential Race at the federal, state, and local county level. Benford's Law analysis was conducted at each level to detect for ballot fraud and manipulation.

Outlier Detection Using Cluster Analysis

Credit card fraud detection using machine learning techniques

Exploratory data analysis of a car insurance claims dataset to uncover trends, anomalies, and insights related to fraudulent claims. Built for a student project.

Detecting fraudulent car insurance claims using classification models and machine-learning techniques with resampling methods to handle imbalanced data. Built for a student project.

The methods and results of the publication "Potential COVID-19 test fraud detection: Findings from a pilot study comparing conventional and statistical approaches" are described in more detail in this appendix. The R-syntax for the calculation is provided, as well as a pseudo data set with which the syntax can also be tested.

A program based on semi-supervised learning to detect CerditCard Fraud Detection on Very Unbalanced database