Fusion System for Blockchain Asset Securitization Risk Control Using Adaptive Deep Learning-Based Framework

Abstract

Feature engineering methods, which entail identifying and extracting useful features from big datasets, can be used to enhance the precision of asset securitization. It might be difficult to securitize assets that produce multiple receivables, such as consumer or company debt. In order to overcome these difficulties, companies might think about adopting a fusion system that integrates feature engineering with distributed ledger technologies such as blockchain. Businesses can benefit from implementing a fusion system like the Deep learning-based Adaptive Online Intelligent Framework (DLAOIF) since it allows for better decision-making, less wasted time and money, and less chance of fraud. Financial asset tracking on a blockchain can help investors keep a closer eye on asset performance and related risks, while also decreasing their reliance on credit rating agencies. Blockchain's high data security standards and elimination of regulatory bottlenecks in the securitization process also make it a useful tool for easing the burden of due diligence.