Securing Blockchain Loyalty Programs with Artificial Intelligence– Driven Fraud Detection

Blockchain technology offers enhanced security and transparency, making it a more reliable foundation for loyalty programs compared to traditional relational databases. Its decentralized nature ensures tamper-resistant records, which is crucial for fraud detection in financial systems. Recent reports reveal a financial loss of $1 billion annually due to fraudulent activities in loyalty platforms, and the blockchain’s transparency and immutability make it an ideal solution for minimizing this issue. This paper presents an innovative approach that combines blockchain technology with artificial intelligence (AI)-driven fraud detection models to enhance the security of loyalty platforms. The approach includes real-time detection and rejection of fraudulent transactions prior to being committed to the immutable blockchain ledger, utilizing a meticulously created synthetic dataset to train a variety of machine learning models. A total of 100 000 user wallets, campaigns, and market items were generated to develop effective detection models, simulating real-world transaction patterns. Afterwards, various fraud scenarios, including new user abuse, employee fraud, and account theft, were injected into the Ethereum network with randomness to mimic the complexity of real-world situations. Using Blockscout explorer for data extraction, multiple models, including Random Forest, XGBoost, and a 3-layer neural network, were trained. The artificial neural network achieved the highest performance, with a receiver operating characteristic area under the curve score of 0.9942. The method allows for real-time fraud detection and seamless retraining as real user data accumulates, demonstrating its scalability and potential to prevent fraud in blockchain-based systems. By incorporating blockchain technology with AI-driven fraud detection methods, this innovative solution improves the security of loyalty platforms.