Dynamic Feature Engineering for Adaptive Fraud Detection
Ajay Kumar Sharma, Shamneesh Sharma, Arun Malik, Rajeev Sobti, Anang Suryana · 2025
In today’s digital economy, electronic payments are essential to supporting financial transactions. However, the danger of fraud also rises with company complexity and volume. This study uses machine learning and advanced analytics to investigate fraud detection in electronic payments. Using business tools like accounts, account types, and balance sheets, we spot patterns and trends connected to illicit activities. To detect and identify fraud, our study uses pre-existing data, machine learning algorithms, and infrastructure. The author has assessed the performance of several models, such as logistic regression, random forests, and k-nearest neighbor models, using criteria like accuracy, precision, and recall. To determine the most important characteristics for fraud detection, the author also conducts a significance analysis and examines the model’s interpretability. According to the current study’s findings, financial institutions and payment systems will be able to identify fraud more efficiently and gain an improved knowledge of the traits of commercial fraud.