ASC-DEMONET A NOVEL ARIMA AND BLOCKCHAIN-BASED SMART CONTRACT MODEL FOR POST-DEMONETIZATION BANKING ANALYSIS

Abstract

The ASC-Demonet model is a hybrid architecture that integrates ARIMA time-series forecasting with blockchain technology to enhance management and security in post-demonetization banking transactions. This model provides better prediction and deals with real-time fraud detection to make digital financial transactions safe and transparent. The performance of the ASC-Demonet model was evaluated using key performance metrics that show a significant improvement over traditional banking systems. The proposed model achieved a forecasting accuracy with a Mean Absolute Error Percentage (MAPE) of 4.3%, significantly better than the MAPE of 8.2% obtained using traditional models. On the ASC-Demonet platform, anomaly detection jumps to nearly 12% against conventional methods, which detects around only 3%. In addition, regarding establishing genuine fraud cases, the model's 95% actual positive detection rate underlines its superiority over traditional methods with a 75% actual positive detection rate. Besides, the ASC-Demonet model incorporates a blockchain-based decentralized ledger for transaction security and tamper-proof records, ensuring real-time compliance monitoring. The statistics analysis showcases how the model is adaptable to the changing patterns of digital payment and can thus be utilized to detect anomalies critical to post-demonetization banking problems. Therefore, this study provides irrefutable evidence that the model can facilitate financial inclusion, enhance security, and build public confidence in digital banking systems. Future work will ensure deeper integration of AI and machine learning technologies for refining anomaly detection and fraud prevention and having region-specific strategies to bridge digital infrastructure gaps in rural areas.