A curated list of data mining papers about fraud detection.
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Updated
Mar 16, 2024 - Python
A curated list of data mining papers about fraud detection.
Analysis of credit card fraud data
Anomaly Detection Pipeline with Isolation Forest model and Kedro framework
Monotonic Optimal Binning algorithm is a statistical approach to transform continuous variables into optimal and monotonic categorical variables.
Anomaly detection using isolation forest
An attempt to detect fraud in online transaction in deep neural network using pytorch
using HMM to detect credit card fraudulent transaction
An implementation of a distributed machine learning algorithm using Spark able to identify fraud in credit card transactions
Our underwriting python module for underwriting credit card accounts. For enterprise partners wanting to do their own underwriting in-house.
This repository contains an implementation of credit card fault detection using Luhn's algorithm. Luhn's algorithm is a checksum formula used to validate credit card numbers, as well as other identification numbers. The algorithm is based on performing a set of arithmetic operations on the digits of a given number, resulting in a checksum value.
Credit card fraud detection using concepts of self organizing maps.
projects based on machine learning
Successful work completed as Intern at CodSoft in September 2023
Fraud Detection using Autoencoders
Credit Card Fraud Detection
Data-Driven ML Credit Card Fraud Detection
Credit Card Fraud Detection
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