arXiv:2606.25808v1 Announce Type: cross Abstract: We propose a predict-optimize-explain framework that uses gradient-based sample generation to interpret various portfolio models by identifying macroeconomic conditions that induce specified portfolio outcomes. Unlike traditional feature-importance methods, this approach directly probes decision pipelines (predictive models coupled with portfolio optimization) by constructing economically meaningful what-if questions. We focus on four such questions: under what macroeconomic conditions a predict-then-optimize pipeline closes or reverses its ret
The increasing complexity and opacity of AI-driven financial models necessitate new methods for interpretability, particularly in high-stakes areas like portfolio optimization, driven by regulatory and risk management demands.
This framework offers a critical tool for understanding and validating AI decisions in finance, enabling better-informed investment strategies and mitigating risks associated with black-box models.
The ability to generate macroeconomic conditions that explain specific portfolio outcomes fundamentally changes how financial AI models can be audited, debugged, and integrated into decision-making processes.
- · Financial institutions
- · Quantitative analysts
- · AI ethics and auditing firms
- · Regulators
- · Opaque AI model providers
- · Compliance departments reliant on traditional methods
Increased trust and adoption of AI in complex financial decision-making.
Regulatory bodies may mandate explainability frameworks for AI used in critical financial applications.
The development of a new 'explanation as a service' industry for AI systems across various sectors.
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Read at arXiv cs.LG