Abstract
The essence-vendored borrowed platforms have emerged as a transformational force in the financial sector, providing an option for traditional banking systems by enabling colleagues to colleagues. However, the challenge of assessing credit in these decentralized environments, however, is complex, as the lack of centralized data makes traditional credit scoring methods less effective. This research searches for the integration of Artificial Intelligence (AI) in decentralized borrowings, especially its role in increasing credit scoring and risk analysis. By taking advantage of machine learning algorithms and advanced data analytics, AI has the ability to provide more accurate, inclusive and dynamic assessment of credit risk of borrowers. The study enters various AI techniques-as future-centered analytics, natural language processing, and blockchain-based data integration-who are re-shaping the way of assessing credit to lenders, reducing risk and ensuring more confidence and transparency in decentralized ecosystems. Through this exploration, research aims to highlight the ability to bridge the difference between traditional and decentralized finance for AI, which provides insight into the future of lending into a decentralized world. Keywords-AI, blockchain, credit scoring, decentralized finance, decentralized loans, machine learning, peer-to-peer lending, predictive analytics, risk analysis, transparency
Citations
IRE Journals:
Olusegun Adebayo , Nicholas Mensah
"Decentralized Lending: The Role of AI in Credit Scoring and Risk Analysis" Iconic Research And Engineering Journals Volume 6 Issue 9 2023 Page 442-447
IEEE:
Olusegun Adebayo , Nicholas Mensah
"Decentralized Lending: The Role of AI in Credit Scoring and Risk Analysis" Iconic Research And Engineering Journals, 6(9)
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