The original title is "AI in fintech: use cases that work in production"

Short summary

A practical overview of AI use cases that work in fintech production environments, emphasizing that financial services require higher auditability and explainability than typical AI applications. The article details three mature patterns: LLM-assisted credit decisioning where the LLM extracts data but a deterministic model makes decisions, LLM-based document processing with confidence-thresholded human review, and hybrid fraud detection combining fast ML scoring with LLM analysis of communications. The key architectural principle is assigning LLMs to tasks where variability is acceptable while keeping regulated decisions traceable.

• •AI-assisted credit decisioning separates LLM document extraction from deterministic scoring models for regulatory auditability
• •LLM document processing requires confidence thresholds routing low-confidence extractions to human review to prevent compounding errors
• •Fraud detection combines millisecond ML transaction scoring with LLM-based analysis of customer communications for account takeover detection

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