In high-stakes settings, Shapley explanations increase analyst confidence but do not improve decision accuracy, and standard metrics fail to predict human utility.
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FinLangNet applies dual-granularity prompting in a sequential model to heterogeneous financial data, reporting 6.3 pp KS improvement and 9.9% bad debt reduction in real-world deployment.
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Rethinking XAI Evaluation: A Human-Centered Audit of Shapley Benchmarks in High-Stakes Settings
In high-stakes settings, Shapley explanations increase analyst confidence but do not improve decision accuracy, and standard metrics fail to predict human utility.
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A Unified Framework for Modeling Heterogeneous Financial Data via Dual-Granularity Prompting
FinLangNet applies dual-granularity prompting in a sequential model to heterogeneous financial data, reporting 6.3 pp KS improvement and 9.9% bad debt reduction in real-world deployment.