From Black Box to Mission Critical: Implementing Advanced AI Explainability and Alignment in FSIs

In highly regulated industries like Financial Services (FSIs), deploying AI models requires more than just performance. Stakeholders demand stringent criteria for acceptance, including explainability, alignment with business processes, and robust risk management. This talk delves into the challenges of moving AI models from 'black box' to 'mission critical' in FSIs. It discusses the critical factors for model acceptance, such as accurate and stable explanations, observability, monitoring, auditability, and regulatory compliance. The presentation highlights the proprietary AryaXAI platform, focusing on its advanced explainability techniques (like DLBackTrace), similar case explanations, and observation-based explanations. A case study in fraud monitoring demonstrates how these features improve recall rates, reduce investigations, and capture more fraud. The talk also addresses the crucial aspect of model alignment, discussing strategies like synthetic alignment and retraining to combat model drift and ensure performance stability. Ultimately, it emphasizes that explainability and alignment are not just acceptance criteria but powerful product features that drive ROI and scalability for AI solutions in financial services.