Day 1 - Workshops 28 May
Explainable AI for better adoption of Machine Learning techniques in Risk Mitigation
Businesses across domains are looking for sharper tools and techniques to identify and predict risk, and develop data driven strategies to mitigate that risk, which be in the form of financial default, customer attrition, fraud, employee attrition, etc. New age machine learning techniques have provided businesses tools to sharpen their focus on predicting risk, though the adoption of these techniques to drive business impact at scale, is slowed by the need for explainability, especially in regulated industry domains like banking, insurance, healthcare, etc.
This hands-on session will focus on a step-by-step approach to build a risk framework, by applying multiple machine learning techniques and the application of XAI to explain the factors driving risk at a local level. The session will be delivered via Analyttica TreasureHunt® LEAPS (leaps.analyttica.com), one can register to ATH LEAPS using their email ID and follow and execute the approach as part of the session.