AI Architecture & Predictive Spend Analytics

19-usage-billing-engine incorporates statistical machine learning and predictive forecasting to help customers project end-of-month invoice costs and detect runaway consumption anomalies.

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Spend Anomaly Detection & Forecasting Pipeline

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1. Forecasting Algorithms Used

1. Linear Run-Rate Extrapolation: $$\text{Projected Total} = \frac{\text{Current Month Usage}}{\text{Elapsed Days}} \times \text{Total Month Days}$$
2. Holt-Winters Triple Exponential Smoothing: Accounts for weekly seasonality (lower weekend consumption) and trend acceleration when computing projected billable units.

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2. Generative AI Dispute Assistant Architecture (LLM Copilot)

To reduce manual customer support costs for billing inquiries, an LLM copilot architecture was designed for invoice explanations:

• Prompt Guardrails: System prompts strictly forbid the model from guessing or negotiating invoice amounts. The model can only explain pre-computed mathematical deltas.
• Data Privacy: All customer PII, Tax IDs, and card details are scrubbed via security/audit.py:redact_sensitive_info() before payloads are transmitted to external LLM providers.