Apr 2, 2025 · Online Payment Fraud Detection using Machine Learning in Python. Here we will try to solve this issue with the help of machine learning in Python. The dataset we will be using have these columns - The libraries used are :

Through advanced feature engineering and modelling techniques, such as graph networks, autoencoders and clustering, machine learning can help detect fraudulent events as anomalies in a standard customer purchasing journey.

Jan 29, 2023 · In order to examine past transaction information and extract consumer behavioral patterns, our main goal in this study, a novel fraud detection algorithm for streaming transaction data is built and created.

We define three models to address these issues: a risk model to forecast fraud risk while taking counter measures into account; machine learning-based fraud detection; and economic optimization of machine learning outcomes. Real data is used to test the models. The world is heading quickly toward a cashless society.

Nov 10, 2023 · Online fraud refers to frauds when product bought online is of cheap quality or the money from one account is robbed. In this paper, we are going to discuss various types of online-based frauds and their detection using machine learning.

Dec 5, 2022 · By combining traditional ML with other approaches, such as explainable AI and deep learning, we can unlock even greater capabilities and build robust, interpretable fraud detection systems...

May 13, 2025 · These provide a much truer picture of a fraud detection model’s effectiveness. Key Challenges. While powerful, deep learning for fraud detection isn’t without its obstacles: The “Black Box” Problem & Explainable AI (XAI): Deep learning models can be incredibly complex, making it hard to understand why they made a specific decision. This ...

Drawing on a comprehensive review of existing literature and case studies, this paper explores the underlying mechanisms of online fraud and identifies key vulnerabilities in current payment systems.

Apr 24, 2025 · Real-World Use Cases of Machine Learning in Fraud Detection. Organizations that prioritize AI-driven fraud detection gain a significant advantage by spotting suspicious behavior in a broad range of digital interactions. From conventional credit card scams to advanced account takeovers, machine learning systems offer the flexibility and speed ...

5 days ago · Fraud detection remains a critical task in high-stakes domains such as finance and e-commerce, where undetected fraudulent transactions can lead to significant economic losses. In this study, we systematically compare the performance of four supervised learning models - Logistic Regression, Random Forest, Light Gradient Boosting Machine (LightGBM), and a Gated Recurrent Unit (GRU) network - on ...