Optimizing supply chain finance with XGBOOST and Merkle Tree Blockchain

This research uses an Ethereum blockchain dataset that contains transactional data, metadata, registry logs, payments and invoices to investigate how Extreme Gradient Boosting (XGBOOST) and Merkle Tree Blockchain can be integrated to optimize Supply Chain Finance (SCF) operations. This will improve SCF processes by guaranteeing data integrity, transparency and security. In order to forecast monetary flows and effectively detect the anomalies, the researchers use a robust method that begins with preprocessing using One-Hot Encoding. After the preprocessing step, the Feature Extraction takes place and is done by Independent Component Analysis (ICA) to identify independent components from the dataset. Then the optimization is done by XGBOOST. Moreover, by comparing the Merkle Tree Blockchain method with the existing Practical Byzantine Fault Tolerance (PBFT), the proposed Merkle Tree Blockchain guarantees safe encoding, decoding and hashing processes while drastically lowering latency and raising throughput that improves the system’s overall efficiency. Furthermore, a robust SCF architecture is supported by network performance monitoring that guarantees scalability, low latency and high throughput. The proposed XGBOOST technique outperforms the current techniques in financial forecasting and fraud detection, reaching 99.96% accuracy, 99.05% precision, 98.61% recall and 99.54% of the F1-score. By enhancing the cash flow, this integration ensures sustainability and operational efficiency by fostering collaboration and trust among the supply chain partners. Thus, this research demonstrates the revolutionary potential of blockchain technology and powerful Machine Learning (ML) in transforming SCF operation by providing a more secure, transparent and effective way to manage financial transactions.

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