Abstract
This chapter examines the use of machine learning (ML) models in credit scoring and risk assessment, comparing their efficiency and accuracy to traditional methods. The discussion highlights ML's potential to enhance credit evaluations, particularly in areas like peer-to-peer lending and non-traditional financial products. The analysis emphasizes the operational benefits of adopting ML in credit processes.
| Original language | English |
|---|---|
| Title of host publication | Innovation in Banking and Financial Intermediaries |
| Publisher | Routledge |
| Pages | 189-215 |
| Number of pages | 27 |
| ISBN (Print) | 9781032887968 |
| Publication status | Published - 2025 |
Keywords
- machine learing
- credit scroring
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