This notebook explores fraud detection using various machine learning techniques.

Repository to share the development of three machine learning tasks as part of the Cognorise Internship Data Science program

A series of interactive lab notebooks we prepared for the ACA course on "Internal Audit Knowledge Elements". The content of the series is based on Python, IPython Notebook, and PyTorch.

Credit Card Fraud Detection using ML: IEEE style paper + Jupyter Notebook

Project for the Big Data Computing course at the University of "La Sapienza" in Master in Computer Science A.A. 2021/2022

This repo has a notebook that I worked on for making a fraud detection model. The dataset was Highly imbalanced, so i used random undersampling to balance the data.

This notebook tries to make fraud/not fraud predictions on a transactions dataset with highly imbalanced data.

The notebook on the main topic of interpretable machine learning is a descriptive and instructive analysis of a car data set from a public source.

IEEE Fraud Detection with XGBoost and CatBoost

A notebook about credit card fraud detection treated as anomaly detection via multivariative normal distribution. The dataset is highly imbalanced (0.17 % of positive class labels). Dataset source: https://www.kaggle.com/mlg-ulb/creditcardfraud

In this notebook we take a look at a relevant project that is frequently encountered by insurers: Fraud Detection. For this purpose we use a car data set from a public source and will show the necessary steps to establish an automated fraud detection.

Survey notebook to compare traditional ML and deep learning techniques for fraud detection

This repository contains some of my machine learning notebooks I created on Kaggle

IEEE-CIS Fraud Detection Kaggle Competition notebooks

This is a notebook for fraud detection for a kaggle challenge.

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 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.