A UI tool to review potentially fraudulent transactions and perform operations on them

FraudLabs Pro Fraud Prevention plugin that screen the order transaction for online frauds. Fraud Prevention extension for Magento 1.

This repository contains the implementation of Self organising maps for fraud detection.

The central concept involves employing graphs, Java, and Neo4j to identify anomalies in banking transactions.

https://trustswiftly.com - Identity Verifications (PHP SDK)

This project serves as a simplified illustration of the principles underpinning anti-fraud systems within the financial sector. In this endeavor, we focus on a system featuring an enhanced role model, a suite of REST endpoints responsible for user interaction, and an internal transaction validation logic grounded in a set of heuristic rules.

Exploratory Data Analysis on Banking Data

A multi-SQL project to detect potential scam traffic on your VoIP network.

Predictive modeling projects developed during the Risk & Fraud Analytics course (Master in Business Analytics & Big Data) at IE HST.

Utilize autoencoders for anomaly detection and customer credit risk evaluation

Scam-fraud mail analysis and prevention

Simple project created to suggest how Python could be used to assist new fraud investigators in making decisions during complex investigations. It was my first interaction with Python while attending a Cybersecurity master.

Complete Fraud Protection Checklist

Projeto que engloba soluções de Analitycs para empresas do mercado financeiro. Neste projeto envolvemos problemas de Fraud Detection, Churn Detection e Credit Score.

Predict the probability that a customer does not pay back their credit card balance amount in the future based on their monthly customer profile.

Parse a CSV file of Call Data Records (CDRs) and check the SentryPeerHQ API to find a match

Block list of different category's. Please use at your own risk. No warranty

Leveraging Machine Learning to Identify Fraudulent Firms Using Current and Historic Risk Factors

The aim of this project is to use the logistic regression mode as a binary classifier to analyse credit card risk. The recommended model helps to predict the high-risk cases. The accuracy, precision, and recall metrics are used to evaluate this model performance.