AI Model for Detecting Fraudulent Banking Transactions
A recent study published on arXiv (2608.07471) investigates how artificial intelligence can detect bank fraud, especially given the rise in online banking and charity scams during the COVID-19 pandemic. This research emphasizes machine learning algorithms for scrutinizing online banking transactions, introducing innovative models for spotting fraudulent activities and preprocessing banking data. The paper outlines techniques to enhance detection precision, such as managing imbalanced datasets, transforming features, and engineering features. The suggested model, which utilizes an artificial neural network, significantly boosts the effectiveness of fraud detection.
Key facts
- Study on arXiv:2608.07471
- Focus on AI for bank fraud recognition
- Increased fraud due to COVID-19 and online banking
- Use of machine learning algorithms
- Development of models for identifying fraudulent transactions
- Techniques for preprocessing bank data
- Methods for handling imbalanced datasets, feature transformation, and feature engineering
- Proposed model based on artificial neural network
Entities
Institutions
- arXiv