AI Explainability in Finance: Why and How

In the finance sector, AI is revolutionizing tasks such as credit scoring, fraud detection, chat box, algorithmic trading and risk management. However, the complexity of AI models necessitates explainability to ensure transparency, compliance, and customer trust. Without clear insights into AI decisions, financial institutions risk regulatory penalties and eroded customer confidence. Achieving explainability cannot only be an after-though with post-hoc explanation techniques like SHAP and LIME. While these methods clarify complex AI outputs, ensuring stakeholders understand and trust the decision-making process, they are not enough. A robust framework is required to secure appropriate transparency and explainability. It starts on day zero and needs to include planning, documentation, monitoring and modelling choices as well as the use of readily available validations further enhance transparency and ultimately Join us to explore how leading financial institutions can successfully implement AI explainability, improving compliance, risk management, and customer trust. Learn best practices and practical techniques to make AI-driven decisions clear and accountable. Key Takeaways - Regulatory Compliance: Meet requirements and avoid penalties. - Risk Management: Identify and mitigate AI-related risks. - Customer Trust: Enhance confidence with transparent AI decisions. - Model Development and Validation: Facilitate thorough audits and validations. - Best Practices: Monitor continuously, train staff, and foster transparency.