The Future of AI in Banking

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Presented by

Stephen Moody, GM, causaLens

About this talk

This talk covers: - Problems with ML – what got us here won’t get us there - Regulatory convergence and the way forward - Measures of Explainability, Trust, Fairness and Bias - Causal AI as a 10X accelerator in Financial Services - Examples of Causal AI in Fraud Detection, Credit Risk and Stress Testing Time stamps: 0:33 - What do we do? 3:33 - Challenges with current ML approaches 6:55 - Intro to Causal AI 10:42 - Example: Causal AI for Fraud/Crime Prevention 20:32 - Example: Causal AI for Credit Risk 26:06 - Example: Causal AI for Stress Testing 33:54 - Summary and closing remarks
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causaLens is the pioneer of Causal AI—a giant leap in machine intelligence. Today’s machine learning algorithms extract correlations from data and predict outcomes based on patterns in past data. Correlations are useful for making predictions, but they’re of little use for decisions. Causal AI goes beyond predictions by understanding the actual causes behind an outcome and quantifying the impact of different interventions. It is the only form of machine intelligence that can answer “Why?”. causaLens builds Causal AI-powered products that empower all users to make superior decisions and drive business value. Leading organisations across a wide range of industries trust causaLens with their most important decisions.