A new era of convenience and efficiency in financial transactions has emerged with the combination of financial systems and the Internet of Things (IoT). This innovation has raised earlier unseen security issues that require frame fixes. This study explores approaches to increase the security of financial transactions provided by the IoT using artificial intelligence (AI). The likelihood of increased complexity and expense in establishing and maintaining strong security measures, as well as the ongoing difficulty of supporting quickly evolving cyber threats, are the problems associated with utilizing AI to safeguard IoT-enabled financial transactions. To evaluate financial transactions based on an IoT system, the power suggests a scalable K-nearest neighbor (SFI-SKNN) method inspired by the scarlet fox. Collection of data that is essential to the financial industry for financial analysis, fraud detection, and trend identification. Utilize Z-score normalization for preprocessing. Financial operation data is subjected to feature extraction using the term frequency-inverse document frequency (TF-IDF). Using popular performance measures including precision, recall, accuracy, and F1-score, the performance of the proposed model is compared to current machine learning methods. This study shows that risks can be identified and mitigated with remarkable success, which reduces fraud incidents significantly while maintaining transactional utility and user experience. This innovative approach ensures security and simplicity, offering a storage foundation for the resurrecting financial technology sector.