Unit 07 · Chapter 3 · 10 min read

Risk models, calibration, and delayed outcomes

Evaluate predictions against the decision they will support.

A model reports 99 percent accuracy. Fraud occurs in only 1 percent of the population. A model that predicts no fraud at all can claim the same number. The interesting result is hidden in the errors.

Choose a target that matches the action

A model target defines what the system learns to predict. Confirmed unauthorized use, a dispute, a default, and a suspicious-activity disposition are different labels. Choose the target from the intended decision and available evidence.

Specify the prediction horizon, population, exclusions, and label maturity. A payment-fraud model should not silently use merchant service disputes as equivalent fraud labels. A credit model should distinguish a technical payment failure from inability to repay. Write a target contract before selecting the algorithm. Otherwise, a sophisticated model can optimize the wrong outcome very efficiently.

Choose a target that matches the action Choose a target that matches the action — the flow Follow the sequence. Assess evidence and maturity. Action Define the decision to support Target Specify the outcome and horizon Labels Assess evidence and maturity — Follow the sequence. Assess evidence and maturity. Chapter sources · Open image

Choose a target that matches the action Choose a target that matches the action — the distinction These concepts answer different questions. Read each definition in the context of the section. Proxy label Available outcome related to the concern Intended target Specific event the decision should predict — These concepts answer different questions. Read each definition in the context of the section. Chapter sources · Open image

Choose a target that matches the action Target contract Fictional teaching record. Different cause. Target contract Illustrative data; not a real customer record or a prescribed policy. Outcome confirmed unauthorized payment Defined label Horizon specified maturity window Comparable observation Excluded technical duplicates Different cause Different bad outcomes need different controls — Fictional teaching record. Different cause. Chapter sources · Open image

Choose a target that matches the action Choose a target that matches the action — control and failure modes Different bad outcomes need different controls. The branches show why alternative designs fail. Control design Align target definition with the intended action. Different bad outcomes need different controls. Failure mode 1 Combine every negative event into one label. The objective becomes confused. avoid Failure mode 2 Select the algorithm before defining success. It may optimize the wrong thing. avoid Failure mode 3 Ignore immature outcomes. Recent cases can be mislabeled as safe. avoid — Different bad outcomes need different controls. The branches show why alternative designs fail. Chapter sources · Open image

Read the confusion matrix

Precision measures the share of positive predictions that are true positives. Recall measures the share of actual positives detected. The false-positive rate uses actual negatives as its denominator. These ratios answer different questions.

In a fictional 10,000-payment cohort, 100 are confirmed fraud. A control flags 200 payments, including 80 fraud and 120 legitimate payments. Precision is 40 percent and recall is 80 percent. The false-positive rate is 120 divided by 9,900, about 1.21 percent. Count-based metrics do not describe value-weighted loss or customer harm by themselves.

A confusion matrix is meaningful only after the positive class and evaluation population are defined. If positive means confirmed fraud, a true positive is a flagged event later established as fraud under the chosen label process. A false positive is a flagged event that meets the defined negative outcome. Unresolved cases should not silently become negatives merely because a review has not finished.

Precision and recall emphasize different errors. Precision describes the proportion of flagged events that are positive; recall describes the proportion of positives that were flagged. Neither alone states the dollar loss, the review workload, or the effect on legitimate customers. Pair model metrics with action-level measures and inspect how the results change across transaction values and relevant populations.

Read the confusion matrix Read the confusion matrix — the flow Follow the sequence. Use the correct denominator. Predictions Identify flagged and unflagged records Outcomes Compare with mature evidence Metrics Use the correct denominator — Follow the sequence. Use the correct denominator. Chapter sources · Open image

Read the confusion matrix Read the confusion matrix — the distinction These concepts answer different questions. Read each definition in the context of the section. Precision 80 true positives out of 200 flags Recall 80 detected fraud out of 100 actual fraud — These concepts answer different questions. Read each definition in the context of the section. Chapter sources · Open image

Read the confusion matrix Confusion-matrix example Fictional teaching record. Fraud missed. Confusion-matrix example Illustrative data; not a real customer record or a prescribed policy. True positives 80 Fraud correctly flagged False positives 120 Legitimate payments flagged False negatives 20 Fraud missed One accuracy number can hide important errors — Fictional teaching record. Fraud missed. Chapter sources · Open image

Read the confusion matrix Read the confusion matrix — control and failure modes One accuracy number can hide important errors. The branches show why alternative designs fail. Control design Report precision recall and the relevant costs. One accuracy number can hide important errors. Failure mode 1 Call 99 percent accuracy sufficient. Rare events can make trivial models look good. avoid Failure mode 2 Divide false positives by all flags for FPR. That is not the false-positive-rate denominator. avoid Failure mode 3 Ignore payment values. Counts and financial loss differ. avoid — One accuracy number can hide important errors. The branches show why alternative designs fail. Chapter sources · Open image

Calibrate probabilities before using dollars

A calibrated probability estimate should correspond to observed outcome frequency in comparable groups, within sampling uncertainty. Ranking quality and calibration differ. A model can sort risky payments well while overstating their absolute probability.

Use held-out data appropriate to the deployment population and examine reliability across score bands and segments. If payments assigned 10 percent risk develop about 2 percent observed fraud, the score may be miscalibrated or the evaluation population may differ. Expected-loss decisions need probability meaning, not just a useful ranking. Calibration itself can drift as behavior and selection change.

Calibration compares predicted probabilities with observed frequencies in an appropriate evaluation set. Among sufficiently many events predicted near 2%, the observed positive rate should be near 2% if the probabilities are well calibrated for that setting. This does not mean any particular event will produce 2% of a loss. It also does not guarantee calibration after the traffic or policy changes. A model can rank risky events well and still produce probabilities that are unsuitable for expected-dollar calculations without further work.

Calibrate probabilities before using dollars Calibrate probabilities before using dollars — the flow Follow the sequence. Use suitable probabilities in loss estimates. Rank Order records by estimated risk Calibrate Compare probabilities with observed frequencies Apply Use suitable probabilities in loss estimates — Follow the sequence. Use suitable probabilities in loss estimates. Chapter sources · Open image

Calibrate probabilities before using dollars Calibrate probabilities before using dollars — the distinction These concepts answer different questions. Read each definition in the context of the section. Ranking Higher scores tend to have more events Calibration Stated probabilities match observed frequencies — These concepts answer different questions. Read each definition in the context of the section. Chapter sources · Open image

Calibrate probabilities before using dollars Calibration check Fictional teaching record. Do not price loss from the raw score. Calibration check Illustrative data; not a real customer record or a prescribed policy. Predicted band 10 percent Model estimate Observed rate 2 percent Held-out cohort result Action investigate calibration and mix Do not price loss from the raw score A ranking score is not automatically a probability — Fictional teaching record. Do not price loss from the raw score. Chapter sources · Open image

Calibrate probabilities before using dollars Calibrate probabilities before using dollars — control and failure modes A ranking score is not automatically a probability. The branches show why alternative designs fail. Control design Validate probability meaning before expected-loss use. A ranking score is not automatically a probability. Failure mode 1 Multiply any score by dollars. Its scale may have no probability interpretation. avoid Failure mode 2 Check only overall calibration. Segments can differ. avoid Failure mode 3 Calibrate on the same fitted data only. Evaluation can be overly optimistic. avoid — A ranking score is not automatically a probability. The branches show why alternative designs fail. Chapter sources · Open image

Split data by time and entity

Random splits can leak related records across training and evaluation. The same account, device cluster, or merchant can appear in both sets, making generalization look easier than it is. Temporal splits better reflect predicting later activity, but they also need entity and label-availability checks.

Define the evaluation question: future activity from known customers, new customers, new merchants, or another population. Choose splits that test it. Keep model selection separate from final evaluation. Repeatedly tuning against a holdout turns it into development data even if the filename still says test.

Split data by time and entity Split data by time and entity — the flow Follow the sequence. Protect final evaluation from repeated tuning. Question Define the deployment population Split Respect time and relevant entity links Hold out Protect final evaluation from repeated tuning — Follow the sequence. Protect final evaluation from repeated tuning. Chapter sources · Open image

Split data by time and entity — the distinction

Random record split: May share related entities across sets
Deployment-like split: Tests the intended future use

These concepts answer different questions. Read each definition in the context of the section. Chapter sources · Open image

Split data by time and entity Split audit Fictional teaching record. Rebuild the evaluation. Split audit Illustrative data; not a real customer record or a prescribed policy. Account appears in both sets Potential dependency Feature post-outcome record Temporal leakage Fix review split and cutoff Rebuild the evaluation Record randomness alone is not enough — Fictional teaching record. Rebuild the evaluation. Chapter sources · Open image

Split data by time and entity Split data by time and entity — control and failure modes Record randomness alone is not enough. The branches show why alternative designs fail. Control design Design splits for the actual generalization claim. Record randomness alone is not enough. Failure mode 1 Assume a random seed prevents leakage. It only makes the split repeatable. avoid Failure mode 2 Tune repeatedly on the final holdout. It stops being independent evidence. avoid Failure mode 3 Ignore delayed labels. Future outcomes can leak into training. avoid — Record randomness alone is not enough. The branches show why alternative designs fail. Chapter sources · Open image

Monitor the model as part of a system

Track input quality, score distribution, calibration, ranking, latency, coverage, and mature outcomes. Data drift means inputs changed; performance drift means outcome quality changed. One can occur without the other.

Define action thresholds with owners and context. A score-distribution shift may come from a new merchant mix rather than a model defect. A stable score distribution can still hide deteriorating calibration. Monitor the decision policy and human overrides too. Customers experience the complete system, not an isolated model artifact.

Monitor the model as part of a system Monitor the model as part of a system — the flow Follow the sequence. Investigate cause before changing the model. Observe inputs Check quality and distribution Observe outcomes Measure mature predictive performance Respond Investigate cause before changing the model — Follow the sequence. Investigate cause before changing the model. Chapter sources · Open image

Monitor the model as part of a system Monitor the model as part of a system — the distinction These concepts answer different questions. Read each definition in the context of the section. Data drift Input population changed Performance drift Prediction quality changed — These concepts answer different questions. Read each definition in the context of the section. Chapter sources · Open image

Monitor the model as part of a system Monitoring example Fictional teaching record. Stable scores do not prove stable risk. Monitoring example Illustrative data; not a real customer record or a prescribed policy. Scores stable Distribution unchanged Observed loss higher Outcome change Investigation calibration and policy mix Stable scores do not prove stable risk The model is one part of the customer decision — Fictional teaching record. Stable scores do not prove stable risk. Chapter sources · Open image

Monitor the model as part of a system Monitor the model as part of a system — control and failure modes The model is one part of the customer decision. The branches show why alternative designs fail. Control design Monitor inputs outcomes and policy together. The model is one part of the customer decision. Failure mode 1 Retrain automatically after any drift. Some changes have other causes. avoid Failure mode 2 Wait only for final losses. Leading health checks can reveal defects. avoid Failure mode 3 Ignore manual overrides. They change realized outcomes. avoid — The model is one part of the customer decision. The branches show why alternative designs fail. Chapter sources · Open image

Chapter connections

This chapter builds on Decision engines, rules, and reliable execution. Continue with Experiments, causal effects, and risk tradeoffs to follow the next part of the system. Use the glossary for terminology and risk mathematics for formulas and worked calculations.

Sources

Reviewed 2026-09-17