Fraud Detection and Prevention in Flutter E-Commerce Applications Using Machine Learning
Darsh Karena
, Murtaza Tinwala , Khyati Joshi , Piyush Kashiyani
Flutter, E-Commerce, Fraud Detection, Machine Learning, XGBoost, Mobile Security, Real-time Detection
This study presents a machine learning framework for real-time fraud detection in Flutter-based e-commerce applications. The exponential growth of mobile commerce has increased vulnerability to sophisticated fraud techniques. We demonstrate that XGBoost classifiers [1] achieve 98.5% accuracy in detecting fraudulent transactions with minimal latency (~120ms), making this approach viable for cross-platform mobile applications. Our hybrid architecture balances on-device and server-side processing to optimize performance while maintaining security. The framework effectively identifies patterns such as device spoofing, geolocation inconsistencies, and abnormal transaction behaviors while preserving user experience. This research contributes to mobile security and e-commerce analytics by addressing the unique constraints of Flutter environments [7].
"Fraud Detection and Prevention in Flutter E-Commerce Applications Using Machine Learning", TIJER - TIJER - INTERNATIONAL RESEARCH JOURNAL (www.TIJER.org), ISSN:2349-9249, Vol.12, Issue 4, page no.a174-a177, April-2025, Available :https://tijer.org/TIJER/papers/TIJER2504027.pdf
Volume 12
Issue 4,
April-2025
Pages : a174-a177
Paper Reg. ID: TIJER_157315
Published Paper Id: TIJER2504027
Downloads: 000292
Research Area: Science and Technology
Country: Porbandar, Gujarat, India
ISSN: 2349-9249 | IMPACT FACTOR: 8.57 Calculated By Google Scholar | ESTD YEAR: 2014
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.57 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: TIJER (IJ Publication) Janvi Wave
