Storacha Network
AI in finance #95
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How do you see the role of explainable AI (XAI) evolving in finance, especially with increasing regulatory pressure around model transparency in credit scoring, fraud detection, and trading algorithms? |
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Explainability in AI is moving from a ‘nice-to-have’ to a compliance-driven necessity in finance. Regulators want transparency on model decisions — especially in credit scoring, loan approvals, and fraud detection. Techniques like SHAP, LIME, and interpretable models are becoming standard. But beyond compliance, explainable AI builds user trust — which is critical in financial products where decisions impact real money. The future is hybrid — leveraging complex models for accuracy but wrapping them with interpretable layers for stakeholders. |
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Explainability in AI is moving from a ‘nice-to-have’ to a compliance-driven necessity in finance. Regulators want transparency on model decisions — especially in credit scoring, loan approvals, and fraud detection. Techniques like SHAP, LIME, and interpretable models are becoming standard. But beyond compliance, explainable AI builds user trust — which is critical in financial products where decisions impact real money. The future is hybrid — leveraging complex models for accuracy but wrapping them with interpretable layers for stakeholders.