Popular GitHub Repositories for Fraudulent

Successfully developed a machine learning model which can predict whether an online review is fraudulent or not. The main idea used to detect the fake nature of reviews is that the review should be computer generated through unfair means. If the review is created manually, then it is considered legal and original.

The aim of this dissertation is to assess the effectiveness of LLMs such as FinBERT and GPT-2 in detecting fraudulent activities in financial reports and statements. This repo provides the code for implementing LLMs, traditional machine learning and deep learning models on the labelled dataset

3D passive face liveness detection, face anti-spoofing, face fraudulent check, face liveness check, face spoof detection, face fraud detection and face analysis on Android.

Utilize the deep learning library Keras to classify transactions as fraudulent(1) or non-fraudulent(0).

Detecting Fraudulent Blockchain Accounts on Ethereum with Supervised Machine Learning

Using R and machine learning to build a classifier that can detect credit card fraudulent transactions.

The project for 3D passive face liveness detection, face anti-spoofing, face fraudulent check, face liveness check and face fraud detection on iOS

In the banking industry, detecting credit card fraud using machine learning is not just a trend; it is a necessity for banks, as they need to put proactive monitoring and fraud prevention mechanisms in place. Machine learning helps these institutions reduce time-consuming manual reviews, costly chargebacks and fees, and denial of legitimate transactions. Suppose you are part of the analytics team working on a fraud detection model and its cost-benefit analysis. You need to develop a machine learning model to detect fraudulent transactions based on the historical transactional data of customers with a pool of merchants.

? Creates a new gym environment for credit-card anomaly detection using Deep Q-Networks (DQN) and leverages Open AI's Gym toolkit to allocate appropriate awards to the RL agent.

Fraud Detection Dynamics is a machine learning system that uses advanced algorithms to identify fraudulent transactions in real-time

Utilizing AutoXGB for Credit Card Financial Fraud Detection

The aim of this project is to predict fraudulent credit card transactions with the help of different machine learning models.

A web app providing the probability that a credit card transaction was a fraudulent one.

???? Introducing SecureCreditAI: An advanced AI-based system designed to detect credit card fraud and ensure financial security. Our cutting-edge algorithms are meticulously crafted to identify fraudulent patterns and unusual activities within credit cards and financial systems. ???

Build machine learning models to improve the efficacy of fraudulent transaction alerts. The data comes from Vesta's real-world e-commerce transactions and contains a wide range of features from device type to product features.

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

To build a classification methodology to determine whether a customer is placing a fraudulent insurance claim.

Different models to detect if a claim is fraudulent or not

? Creates a new gym environment for credit-card anomaly detection using Deep Q-Networks (DQN) and leverages Open AI's Gym toolkit to allocate appropriate awards to the RL agent.

Detecting fraudulent app usage data with STL and confidence intervals and enhancing ARIMA forecasting with STL