Explainability

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Secure governance accelerates financial AI revenue growth

ai, AI (Artificial Intelligence), AI and Us, AI Business Strategy, AI in Action, AI Market Trends, Artificial Intelligence, Compliance, data lineage, Deep Dives, Explainability, Features, Finance, Finance AI, financial ai, governance, Governance, Regulation & Policy, Inside AI, MLOps, Opinion, regulation, Security, strategy, Trust, Bias & Fairness, World of Work

Financial institutions are learning to deploy compliant AI solutions for greater revenue growth and market advantage. For the better part of ten years, financial institutions viewed AI primarily as a mechanism for pure efficiency gains. During that era, quantitative teams programmed systems designed to discover ledger discrepancies or eliminate milliseconds from automated trading execution times. […]

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Deep Learning With Keras To Predict Customer Churn

Artificial Intelligence, Explainability, Tabular Data, TensorFlow/Keras

Using Keras to predict customer churn based on the IBM Watson Telco Customer Churn dataset. We also demonstrate using the lime package to help explain which features drive individual model predictions. In addition, we use three new packages to assist with Machine Learning: recipes for preprocessing, rsample for sampling data and yardstick for model metrics.

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