There are 3 repositories under fraudulent-transactions topic.
A data science project to predict whether a transaction is a fraud 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.
System to tell apart the transaction was from the real user who owns the credit card or the transaction was from the stolen credit card.
Project for the Big Data Computing course at the University of "La Sapienza" in Master in Computer Science A.A. 2021/2022
This notebook tries to make fraud/not fraud predictions on a transactions dataset with highly imbalanced data.
This is my final project for my internship at EISystems Technologies. I have used two ML algorithms and tried my hands-on. Also, the final report is included.
This project aims to detect fraudulent transactions in the Ethereum blockchain using machine learning algorithms. Fraud detection is crucial for maintaining the integrity and security of the blockchain, and can help prevent financial losses due to fraudulent activity.
Focused on advancing credit card fraud detection, this project employs machine learning algorithms, including neural networks and decision trees, to enhance fraud prevention in the banking sector. It serves as the final project for a Data Science course at the University of Ottawa in 2023.
Detection of fraudulent transactions from IEEE Kaggle Dataset
This project uses predictive modeling techniques to identify fraudulent Credit Card transactions on data obtained from European credit card holders made in September 2013.
In this repo I have used SQL to analyze historical credit card transactions and consumption patterns in order to identify possible fraudulent transactions.
[Project Repository] Predicting fraudulent transactions.
💳 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.
Detect fraudulent transactions and accounts in a vast dataset
🔍 Predict fraudulent transactions with a pre-trained Random Forest Classifier model via Streamlit app.
Catering to Blocker Fraud Company's expansion in Brazil, this data science and machine learning project focuses on detecting fraudulent financial transactions. Leveraging advanced analytics, it delivers insights into transaction legitimacy, aiding revenue generation and minimizing losses
Building an online payment fraud detection system using machine learning algorithms. It utilizes three primary classification algorithms - Logistic Regression, Decision Tree, and Random Forest - to analyze and classify transactions as either legitimate or fraudulent.
Code to detect credit card fraud detecton
Credit Card Transactions Fraud Detection using Deep Learning.
Fraud transaction detection using Machine Learning algorithms on highly imbalanced dataset
The objective of my experiment is to analyze the performance of Random Forest, Naive Bayes, Logistic Regression, and K-nearest neighbors machine learning models for evaluation of the utility of synthesized data for fraud detection.
Develop a model for predicting fraudulent transactions for a financial company and use insights from the model to develop an actionable plan.
An attempt to detect credit card fraud with imbalanced dataset using various models and identify the costs for using each model - over the span of 2 weeks for my 3rd Project with IOD
https://trustswiftly.com - Identity Verifications (PHP SDK)
A UI tool to review potentially fraudulent transactions and perform operations on them
In this project we are going to develop a Supervised ML model for predicting a fraudulent transaction
Using SQL queries, I crafted complex algorithms to detect suspicious behavior, uncovering hidden connections and irregularities that may indicate fraudulent transactions
Machine Learning based Fraudulent Transaction Detection
Credit card detection fraud using fastai.
Data preprocessing and classification for the detection of fraudulent transactions
Transaction Fraud Detection project using machine learning techniques (March, 2024)
SnapML library's Decision Tree classifier and SVM was used to train a model on a real dataset to identify fraudulent credit card transactions. The Decision Tree model resulted in ROC-AUC score = 0.92 and the SVM yielded ROC-AUC score = 0.93 and hinge loss = 0.15. Multi-threaded CPU was implemented to reduce model training time.
A model for predicting fraudulent transactions for a financial company and use insights from the model to develop an actionable plan. Data for the case is available in CSV format having 6362620 rows and 10 columns.
System to tell apart the transaction was from the real user who owns the credit card or the transaction was from the stolen credit card