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The relevant literature spans several distinct problems. A model that extracts merchant names, a model that represents transaction histories, and a model that estimates default risk do not have interchangeable results. AMLOK’s comparisons must reproduce the task, evidence access, population, and evaluation conditions before claiming an advantage.

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Primary methodological references

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Financial language is not financial risk validation

BloombergGPT studies financial language-model training. FinBen covers a broad financial task suite. FinanceBench evaluates evidence-based financial question answering. FinQA and TAT-QA test numerical reasoning over financial documents. These resources can evaluate extraction, retrieval, and arithmetic. None replaces a Canadian borrower outcome cohort. For structured prediction, include regularized regression, gradient boosting, FT-Transformer, and a permitted TabPFN release. A strong tabular baseline can be more appropriate than a language model. Architecture selection depends on evidence, latency, sample size, cost, and interpretability.

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Proposed contribution

The proposed contribution is a jointly evaluated Canadian system that connects bilingual transaction meaning to economic-event reconciliation, an evidence-backed temporal graph, and task-specific risk models. Its novelty must be demonstrated by ablations and a fuller prior-art review. It is not established by combining familiar components or naming a new model. The Canadian adaptation must be measured directly. Prism already describes Canadian support, and ATOM establishes relevant Canadian research. We therefore use Canadian financial risk intelligence research, without a first-in-country or state-of-the-art claim.