Journal of Business Research, Journal Year: 2023, Volume and Issue: 170, P. 114327 - 114327
Published: Oct. 11, 2023
Language: Английский
Journal of Business Research, Journal Year: 2023, Volume and Issue: 170, P. 114327 - 114327
Published: Oct. 11, 2023
Language: Английский
Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 374, P. 124052 - 124052
Published: Jan. 8, 2025
Language: Английский
Citations
1Journal of Accounting Literature, Journal Year: 2025, Volume and Issue: unknown
Published: Jan. 29, 2025
Language: Английский
Citations
1Journal of Business Ethics, Journal Year: 2025, Volume and Issue: unknown
Published: Feb. 14, 2025
Language: Английский
Citations
1International Journal of Data Science and Analytics, Journal Year: 2023, Volume and Issue: unknown
Published: July 1, 2023
Abstract Artificial intelligence methods, based on machine learning models, are rapidly changing financial services, and credit lending in particular, complementing traditional bank with platform lending. While technologies improve user experience possibly lower costs, they may increase risks and, the model that derive from inaccurate rating assessments. In this paper, we will show how to reduce such risks, using a S.A.F.E. statistical model, which improves: Sustainability, taking environmental, social governance factors into account; Accuracy, building maximises predictive accuracy; Fairness, merging ESG scores different data providers, improving their representativeness; Explainability, clarifying relative contribution of each score accuracy.
Language: Английский
Citations
22Journal of Business Research, Journal Year: 2023, Volume and Issue: 170, P. 114327 - 114327
Published: Oct. 11, 2023
Language: Английский
Citations
21