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Analyzing Machine Learning Models for Credit Scoring with Explainable AI and Optimizing Investment Decisions. (arXiv:2209.09362v1 [cs.LG])
stat.ML updates on arXiv.org arxiv.org
This paper examines two different yet related questions related to
explainable AI (XAI) practices. Machine learning (ML) is increasingly important
in financial services, such as pre-approval, credit underwriting, investments,
and various front-end and back-end activities. Machine Learning can
automatically detect non-linearities and interactions in training data,
facilitating faster and more accurate credit decisions. However, machine
learning models are opaque and hard to explain, which are critical elements
needed for establishing a reliable technology. The study compares various
machine learning models, …
arxiv credit decisions explainable ai investment machine machine learning machine learning models scoring