#Decision Science

Articles about Decision Science — exploring patterns, best practices, and real-world implementations in production systems.

1 post tagged with decision science. ← Blog home

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Pratik Dhanave · ·4 min read

Scoring, Thresholds, and the Cost of Errors — Where ML Meets the P&L

A fraud model outputs a probability; the business needs a decision; and the line between them — the threshold — is where machine learning stops and economics begins. Set it by gut and you've made your most consequential business decision by vibes. This post builds the machinery that makes the error trade-off explicit: metrics that survive extreme imbalance, calibration that makes scores mean something, and thresholds derived from costs rather than from tradition.

At 0.1% base rates ROC-AUC flatters; use precision at review capacity and recall at FP budget on time-split, segmented data. Calibration makes scores probabilities; thresholds become cost derivations that move with amount and segment, extended into a ladder across approve, friction, review, and decline.

#Fraud Detection#Machine Learning#Decision Science#Risk

All posts on this site are written by Pratik Dhanave, an Agentic AI Architect with 7+ years building production distributed systems, multi-agent AI platforms, and cloud-native infrastructure. About the author → Each article includes working code, architecture diagrams, and references to the specific frameworks and standards discussed. Browse the blog or explore related topics using the tag cloud above.