AI explainability in finance: auditable models, GenAI, and agents

Financial institutions face increasing pressure from regulators to explain AI-driven decisions, highlighting a significant gap in compliance capabilities. This challenge is exacerbated by models and AI systems that lack transparency, making it difficult to document decision logic, respond to compliance queries, or explain outcomes to clients. To address these challenges, institutions must develop infrastructure for explainable AI, guided by regulations like the EU AI Act and frameworks such as SHAP and LIME. A structured, five-step framework can help implement enterprise-wide explainability, ensuring that AI decisions are transparent, auditable, and meet regulatory standards. This framework includes inventorying AI systems, mapping techniques to models, building audit logging infrastructure, validating explanations with stakeholders, and monitoring for compliance. Explainable AI is crucial in financial services to prevent regulatory fines, mitigate algorithmic bias, and maintain reputational trust, as institutions must now prove both the reasoning behind decisions and the processes that support them. Dataiku's platform facilitates this by integrating governance controls and explainability tools, supporting compliance efforts across traditional models, GenAI, and agent deployments.

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