Analyze Finance Anomalies with LLM Grounding

Generates grounded financial analysis and insight discovery from Alteryx workflow outputs by uploading AI-ready datasets into large language models and requesting citations to specific file sources and columns. This replaces manual review of financial anomalies, variance drivers, and root causes, which requires navigating multiple output files and reconciling inconsistent findings across reports.

1. Run Alteryx workflows 02 through 06 in sequence to generate output datasets.
2. Upload Data_Dictionary.txt first, then the five AI-ready CSV files (ai_closure_quality_tracking_feed.csv, ai_execution_hit_rate_scoreboard.csv, ai_high_impact_hotspot_seed.csv, ai_kpi_gap_median_opportunity.csv, ai_priority_variance_trace_manifest.csv).
3. Request analysis using prompt patterns from the LLM Data Usage Playbook: ask for summaries, prioritization, anomalies, and next actions.
4. Require the model to cite the exact source file and column names for each claim, then validate conclusions against the original workflow outputs before sharing. Output: a grounded financial analysis report with source-cited findings ready for stakeholder review.

Every conclusion traces back to specific data files and columns, eliminating unsourced recommendations in financial analysis.

• Alteryx: required to run workflows 02 through 06 in order to generate the output datasets before LLM analysis begins. Workflows can be found in the starter kit here: https://www.alteryx.com/starter-kit/finance-report-insight-deep-dive
• Data_Dictionary.txt: upload first; provides schema context so the LLM understands the structure and meaning of all uploaded datasets.
• Five AI-ready output files: upload in this order before supporting detail files: ai_closure_quality_tracking_feed.csv, ai_execution_hit_rate_scoreboard.csv, ai_high_impact_hotspot_seed.csv, ai_kpi_gap_median_opportunity.csv, ai_priority_variance_trace_manifest.csv.
• Prompt template: use patterns from LLM_Data_Usage_Playbook.html (ask for summaries ranked by impact, anomaly detection, missing evidence, and next actions; require source citations for each claim).

Requires Alteryx to pre-process data; the workflow does not work with raw financial data imported directly into the LLM. Model responses are treated as analyst support, not final evidence, and must be validated against source workflow outputs before external sharing.