Approaching the Legal Frontier: A Framework for Developing Legally Aligned Machine Learning Models in Finance

Recently, the integration of machine learning (ML) technologies within financial institutions has accelerated significantly. However, how to translate legal rules and norms into technical operationalizations within ML systems remains uncertain. This uncertainty complicates efforts to ensure these systems meet legal compliance thresholds. Additionally, when multiple legal rules are operationalized simultaneously, tradeoffs arise between different legal objectives, jeopardizing compliance and model performance. To address this challenge, we propose a five-stage interdisciplinary framework that integrates legal and technical analysis during ML system development. The framework is designed to demonstrate compliance across all applicable legal requirements while maintaining high performance. We illustrate the application of our framework through an example case study in the context of anti-money laundering.