Claims Data, Analytics and Decision Science

Introduction

Insurance claims management is increasingly a data-intensive managerial function. Every claim generates information about the policyholder, coverage, event, loss, service process, payment, recovery and possible fraud. When these data are organized and analysed systematically, insurers can improve claim segmentation, forecast severity, identify anomalies, reduce leakage, allocate adjuster capacity and make more consistent decisions.

Claims analytics is not simply the production of dashboards or prediction scores. It is the disciplined use of evidence to support decisions under uncertainty. A useful claims decision system links four elements: a clearly defined business problem, reliable data, an analytically appropriate model and an operational action. A highly accurate model that cannot be explained, integrated into workflow or used fairly may create less value than a simpler and more transparent model.

This chapter develops a practical decision-science framework for claims professionals. It distinguishes descriptive, diagnostic, predictive and prescriptive analytics; explains segmentation, severity forecasting, anomaly detection, leakage analysis and workload prioritization; introduces relevant quantitative measures; and examines governance, ethics and implementation.

Learning Objectives

After studying this chapter, the reader should be able to:

1. Distinguish the four major forms of analytics used in claims management.
2. Design a claims data architecture and assess data quality.
3. Apply segmentation and severity forecasting concepts to claims portfolios.
4. Explain anomaly detection and leakage analysis in operational terms.
5. Evaluate predictive models using statistical, operational and economic metrics.
6. Develop a workload-prioritization framework for adjusters and special-investigation units.
7. Critically assess explainability, fairness, privacy, cybersecurity and human oversight.

1. Claims Data as a Decision Resource

A claim can be viewed as a sequence of events rather than a single record. The sequence may include notification, coverage verification, reserve establishment, inspection, document receipt, liability assessment, negotiation, payment and closure. Data may be structured, such as loss date, claim amount and policy type, or unstructured, such as adjuster notes, medical reports, photographs and correspondence.

Important claims-data domains include:

A central distinction is between a claim-level observation and a transaction-level observation. One claim may contain many payments, invoices or reserve changes. Aggregating transactions incorrectly can double-count exposure or distort severity. The analytical grain—claim, policy, payment, claimant or event—must therefore be stated before analysis begins.

Data quality and lineage

Data quality has several dimensions: completeness, accuracy, consistency, timeliness, validity and uniqueness. For example, a missing loss date is a completeness problem; a future loss date is a validity problem; different spellings of the same repairer are a consistency problem. Data lineage records where a field originated, how it was transformed and who is responsible for it. It is essential when a claim decision is challenged or a model is audited.

A practical data-quality control cycle is:

1. Define critical data elements and acceptable ranges.
2. Profile missing values, duplicates, outliers and coding changes.
3. Reconcile claims, payment and accounting totals.
4. Investigate errors at source rather than merely deleting them.
5. Monitor quality continuously after system or process changes.

Deleting unusual observations without investigation is dangerous. A genuine catastrophic claim, for example, may be an outlier statistically but not an error operationally.

2. Four Forms of Analytics

Descriptive analytics: What happened?

Descriptive analytics summarizes historical experience. Examples include claim frequency by product, average settlement time by region, closure rate by adjuster and paid loss by cause. It uses counts, rates, averages, medians, percentiles, cross-tabulations and visualizations.

The mean claim severity is:

where Cᵢ is the cost of claim i and n is the number of claims included. The formula assumes that each included claim is measured on a comparable basis. If claims are censored, open or differently developed, the mean may be misleading.

The median is often more representative when a small number of large claims create a right-skewed distribution. Percentiles are useful for capacity planning: the 90th percentile indicates a value below which 90% of observations fall.

A portfolio loss ratio is commonly expressed as:

The numerator and denominator must relate to the same period, coverage and basis. Incurred losses may include paid losses plus outstanding case reserves, and definitions should be aligned with the insurer’s accounting and reporting policy.

Diagnostic analytics: Why did it happen?

Diagnostic analytics examines relationships and possible causes. It may compare cycle times by claim type, decompose changes in average severity, trace leakage to process steps or investigate why one supplier has an unusual invoice pattern.

Correlation is not proof of causation. A region with higher severity may have different vehicle mix, weather exposure, repair costs or reporting practices. Diagnostic work should therefore combine quantitative analysis with subject-matter review, process mapping and sampling of actual files.

A useful method is variance decomposition. If total paid loss changes, the analyst can separate the effect of claim volume, claim frequency, average severity, mix and settlement timing. Such decomposition does not automatically establish causal responsibility; it identifies where investigation should concentrate.

Predictive analytics: What is likely to happen?

Predictive analytics estimates an unknown or future outcome. Typical claims outcomes include probability of large loss, expected ultimate cost, likelihood of litigation, probability of fraud referral, time to closure and expected recovery.

For a binary outcome, logistic regression models the log odds of an event:

Here p is the probability of the event, xⱼ are predictor variables, βⱼ are estimated coefficients and β₀ is the intercept. The model assumes a correctly specified relationship between predictors and log odds, independent or appropriately modelled observations, and no severe multicollinearity. The exponential of a coefficient, e^βⱼ, represents the multiplicative change in odds for a one-unit increase in xⱼ, holding other variables constant.

For a continuous cost outcome, a basic regression model is:

where Y is the predicted outcome, ε is an error term and the β coefficients quantify conditional associations. Claims costs often have skewness, heavy tails and many zeros, so ordinary least squares may be unsuitable without transformation or a more appropriate distributional model.

Prescriptive analytics: What should we do?

Prescriptive analytics recommends an action subject to constraints. Examples include selecting the next-best claim task, assigning claims to adjusters, deciding which cases merit field inspection and determining the order of investigation referrals.

A simple prioritization score may combine expected financial benefit and operational cost:

Expected avoidable cost is not the predicted claim cost itself. It is the estimated amount that the proposed action can legitimately prevent, recover or control. A high-value claim may deserve attention for service or regulatory reasons even when its avoidable cost is low. Prescriptive systems must therefore include service, fairness and compliance constraints, not only financial optimization.

3. Segmentation of Claims

Segmentation divides a heterogeneous portfolio into groups with sufficiently similar characteristics for analysis or action. Useful segmentation variables include peril, product, geography, distribution channel, claim complexity, expected severity, litigation status, vulnerability indicators and fraud-risk signals.

A sound segmentation scheme has four properties:

• Mutual clarity: users can determine why a claim belongs to a segment.
• Actionability: different segments lead to different workflows or controls.
• Stability: categories do not change excessively because of random fluctuations.
• Governance: the variables do not create unjustified or discriminatory outcomes.

Segmentation can be rule-based, statistical or hybrid. Rule-based segmentation is transparent: for example, claims above a specified reserve threshold or involving bodily injury may enter a specialist queue. Statistical clustering groups observations according to similarity, but the resulting clusters must be interpreted and validated by claims experts. Clusters that are mathematically distinct but operationally indistinguishable have little value.

Segmentation should not be confused with unfair risk discrimination. Variables such as location may be legitimate for estimating repair cost but problematic if they act as proxies for protected characteristics or socioeconomic disadvantage. Business necessity, legal requirements and impact assessment must be considered.

4. Severity Forecasting and Claims Development

Severity forecasting estimates the eventual cost of a claim or portfolio. It supports initial reserving, escalation, reinsurance planning, cash-flow management and staffing. At claim level, predictors may include early reported facts, coverage, injury indicators, repair estimates, reserve changes, elapsed time and provider information.

The expected ultimate cost can be represented conceptually as:

U is the ultimate cost and X is the information available at the prediction date. The notation emphasizes that forecasts are conditional and should be updated as new evidence arrives.

For open claims, prediction is affected by censoring: the final cost is not yet observed. A model trained only on closed claims may underrepresent long-duration or complex claims. Development methods, survival models, incremental updating and claims-adjuster judgment can address this problem. The model must specify whether it predicts gross incurred cost, paid cost, ultimate cost, net cost after recoveries or some other financial definition.

Forecast evaluation should distinguish calibration from discrimination. A calibrated model produces probabilities or expected values that correspond to observed outcomes in groups. For example, claims assigned an average predicted probability of 0.20 should experience the event at approximately 20% over a sufficiently large and comparable population. A model may rank claims well but systematically overpredict all costs.

Heavy-tailed severity requires particular caution. Mean squared error can be dominated by a few catastrophic claims. Analysts may examine mean absolute error, weighted errors, quantile loss, calibration by severity band and separate catastrophe models. No single metric is sufficient.

5. Anomaly Detection and Fraud Diagnostics

An anomaly is an observation or pattern that differs materially from an expected baseline. An anomaly is not synonymous with fraud. It may result from a data error, an unusual but genuine loss, a new business process or deliberate manipulation. Analytics should therefore generate a risk-based referral, not an automatic finding of dishonesty.

Common anomaly signals include:

• repeated use of the same bank account, address, device or telephone number;
• claims submitted unusually soon after policy inception or change;
• inconsistent dates, locations or descriptions;
• invoices with unusual price, quantity or timing patterns;
• excessive links among claimants, witnesses, repairers or medical providers;
• multiple claims with highly similar narratives or images;
• claim activity that differs substantially from peer-group behaviour.

Methods range from simple rules and peer-group comparisons to clustering, isolation-based methods, graph analysis and natural-language or image similarity. Supervised fraud models require reliable labelled outcomes, but fraud labels are often incomplete: a non-referred claim is not necessarily genuine. This creates selection bias and label noise.

A practical anomaly workflow is:

1. Establish a relevant peer baseline.
2. Generate signals using transparent rules or validated models.
3. Combine signals without double-counting correlated evidence.
4. Refer cases for trained human review.
5. Record investigation outcomes and reasons.
6. Monitor false positives, missed cases and emerging patterns.

Investigation capacity is limited. An alert should be prioritized according to expected value, evidential quality, customer impact and legal requirements. Aggressive referral policies may increase investigation counts while reducing trust and overwhelming investigators.

6. Leakage Analysis

Claims leakage is avoidable cost arising from process failure, inaccurate assessment, weak controls or missed recovery. It may occur through overpayment, duplicate payment, incorrect coverage application, missed deductibles, poor supplier management, inadequate subrogation or delayed intervention.

A leakage-control framework maps the claim lifecycle and identifies preventive, detective and corrective controls. For each process step, management should ask:

• What decision is made?
• What information is required?
• What error can occur?
• What control detects or prevents it?
• Who owns the control?
• What evidence demonstrates that it operated?

Leakage measurement requires a counterfactual or benchmark. If one supplier has higher average invoice cost, the difference may reflect claim mix rather than leakage. Analysts should adjust for relevant drivers such as vehicle type, injury severity, geography and repair complexity.

The expected value of a control can be assessed as:

This is a decision-support expression, not a guarantee. Probability must be estimated from credible evidence, avoidable loss must exclude amounts that would have been paid legitimately, and control cost should include technology, staff time, delay and customer-impact costs.

7. Workload Prioritization and Queue Management

Claims departments manage competing priorities: regulatory deadlines, vulnerable customers, catastrophic events, suspected fraud, high financial exposure and routine service. A purely financial queue can neglect urgency and customer harm.

A robust prioritization policy usually combines:

1. Urgency: statutory, contractual or safety deadline.
2. Customer impact: vulnerability, hardship or interruption of essential services.
3. Complexity: litigation, multiple parties, bodily injury or coverage uncertainty.
4. Financial materiality: expected cost and potential leakage.
5. Risk signal: anomaly, fraud or control concern.
6. Capacity fit: skill, authority and workload of available staff.

These factors can be represented in a governed score, but the score should not replace professional judgment. Queue rules should define override conditions, escalation paths and documentation requirements. Workload models should consider not only the number of claims but also expected handling time and task complexity.

A service-level measure may be expressed as:

The numerator and denominator must use the same population and due-date definition. A high average productivity rate can coexist with poor service if difficult claims are systematically deferred.

8. Model Evaluation: Statistical and Business Metrics

Classification metrics

For a binary decision, outcomes can be arranged in a confusion matrix: true positives, false positives, true negatives and false negatives. Precision is:

Recall, also called sensitivity, is:

Precision answers: among referred cases, how many were positive? Recall answers: among all positive cases, how many were identified? The F1 score is the harmonic mean of precision and recall:

These measures assume clearly defined labels and a selected threshold. They do not measure financial value or fairness by themselves. Accuracy can be misleading when fraud or severe leakage is rare.

The area under the receiver operating characteristic curve, or ROC-AUC, evaluates ranking across thresholds. Precision–recall curves are often more informative for rare events. Calibration curves and the Brier score are useful when the predicted probability itself informs action. For probabilities pᵢ and binary outcomes yᵢ, the Brier score is:

Lower values are better, subject to the same population and label definition.

Regression and forecasting metrics

Mean absolute error is:

where yᵢ is the observed value and ŷᵢ is the prediction. MAE has the same units as the outcome, such as currency, and is relatively interpretable. Root mean squared error is:

RMSE penalizes large errors more strongly than MAE. Percentage metrics can be unstable when actual values are zero or close to zero, a common issue in claims data.

Business metrics

A model should be evaluated against outcomes such as reduced leakage, faster fair settlement, lower handling cost, improved recovery, fewer complaints, appropriate referral yield and better reserve accuracy. A useful evaluation compares the model-assisted process with a defined baseline or control group. Without a baseline, apparent improvement may reflect seasonality, catastrophe mix or changed reporting.

Model validation should include temporal holdout data, because random splitting can allow information from the future to leak into training. Monitoring should examine drift in data, outcome rates, calibration, subgroup performance and operational adoption.

9. Decision Science in Practice

Decision science adds structure to the choice between alternatives. It recognizes that claims decisions involve uncertainty, preferences, constraints and consequences. A decision table can compare actions such as automatic settlement, desk adjustment, field inspection and specialist investigation.

Expected monetary value is often represented as:

P(stateⱼ | information) is the probability of state j given available information, and Payoff is the consequence of the action in that state. The approach assumes that relevant states, probabilities and consequences can be estimated sufficiently for the decision. In practice, non-financial consequences—fairness, customer trust, legal exposure and reputational harm—must also be considered.

Prescriptive optimization may allocate claims to staff subject to capacity constraints. However, optimization can amplify historical inequities if the objective function rewards only speed or cost reduction. Governance should specify hard constraints, human review requirements and acceptable trade-offs.

10. An Illustrative Case: Motor Claims Triage and Leakage Control

Background

An illustrative regional insurer processes 120,000 motor claims annually. Management observes rising repair costs, inconsistent settlement times and a growing number of referrals to a special-investigation unit. The insurer wants to prioritize claims without treating an analytic alert as proof of fraud.

Problem

The existing process routes claims largely by product and region. Simple claims wait behind complex cases, while some high-cost claims are not identified until late in the lifecycle. The organization lacks a common definition of severity, referral yield and leakage.

Analysis

The analytics team first establishes the grain of analysis: one record per claim for triage, with linked transaction records for payments and reserves. It removes duplicate identifiers, standardizes repairer codes and separates paid, incurred and ultimate-cost measures. Closed claims from the previous three years are used for outcome development, while open claims are handled with a development-aware approach.

Descriptive analysis shows that 18% of claims account for 71% of incurred cost. Diagnostic review indicates that high costs are associated with injury indicators, litigation, extended rental use and complex liability. It also identifies duplicate invoice patterns and missed recovery opportunities in selected supplier relationships.

A severity model predicts expected ultimate cost, while a separate anomaly model produces a referral signal. The two outputs are kept distinct: a high predicted cost means that specialist handling may be needed, whereas an anomaly signal indicates that evidence should be reviewed. A rule-based urgency layer protects statutory deadlines and vulnerable customers.

Approach and solution

The insurer creates four operational queues:

The model is tested on a later time period. Evaluation includes MAE for severity, calibration by predicted-cost band, precision and recall for investigation referrals, average cycle time, referral yield, customer complaints and net leakage reduction. A pilot compares model-supported branches with similar branches using the old process.

Outcome

In this illustrative case, the pilot reduces avoidable payment leakage and improves early identification of complex claims. Fast-track claims settle more quickly, while the investigation unit receives fewer but better-supported referrals. The insurer does not permit automatic claim denial from a model score. Reviewers must record evidence, customers retain appeal rights and performance is monitored by relevant customer groups.

Learning points

The principal lesson is that value came from workflow redesign rather than prediction alone. Data definitions, queue ownership, baseline comparison, human review and outcome measurement were as important as model selection. Separating severity, anomaly and urgency prevented a single opaque score from controlling incompatible decisions.

11. Governance, Ethics and Regulation

Claims analytics affects access to payment, timing of assistance and the intensity of investigation. It therefore requires stronger governance than ordinary management reporting.

Key principles include:

• Purpose limitation: collect and use data for legitimate, specified purposes.
• Data minimization: use only information necessary for the decision.
• Transparency: explain material factors in language customers and staff can understand.
• Human accountability: assign decision ownership to named roles.
• Fairness: test for unjustified differences in error rates, service and referral outcomes.
• Security: protect personal, financial, medical and identity data.
• Contestability: provide correction, review and appeal mechanisms.
• Auditability: retain model versions, inputs, decisions, overrides and outcomes.

Privacy law, insurance regulation, consumer-protection rules, record-retention duties and sector-specific requirements vary by jurisdiction. The correct response is not to avoid analytics but to establish a documented legal and governance assessment before deployment.

Explainability has several levels. Global explanations describe how a model generally behaves; local explanations describe why a particular claim received a score. Neither explanation is automatically causal. A variable may be predictive because it is correlated with another factor, and removing a sensitive field does not guarantee fairness if proxy variables remain.

Generative AI and large language models can assist with document summarization, coding classification and retrieval, but they may hallucinate, reproduce bias or expose confidential data. Human validation, access controls, restricted prompts, source citation and prohibition of unsupervised settlement decisions are essential.

12. Implementation and Contemporary Developments

Successful implementation is socio-technical. It requires data engineering, actuarial and statistical expertise, claims knowledge, legal review, information security, change management and frontline participation.

A staged implementation approach is:

1. Define the decision and success measure.
2. Establish data ownership and quality controls.
3. Build a transparent baseline.
4. Develop and validate candidate models.
5. Test operational integration in a limited pilot.
6. Measure financial, service and fairness outcomes.
7. Approve, document and monitor production use.
8. Review performance after process, product or environmental change.

Contemporary claims analytics increasingly uses real-time event streams, graph-based relationship analysis, telematics, geospatial data, computer vision and natural-language processing. These capabilities can improve speed and consistency, but their value depends on data rights, model reliability, explainability and appropriate intervention design.

Challenges and Limitations

Claims data are not neutral observations. They reflect past underwriting, reporting behaviour, staff choices, investigation policies and unequal access to documentation. Historical decisions can therefore encode historical bias. Fraud labels may reflect who was investigated rather than who actually committed fraud. Open claims create censoring and development problems. Catastrophes cause distribution shifts. Supplier and repair-cost inflation can invalidate older models.

Other limitations include concept drift, missing-not-at-random data, adversarial behaviour, feedback loops and overreliance on statistical significance. A model may perform well in development but fail when adjusters change behaviour in response to it. Continuous monitoring and periodic independent validation are necessary.

Best-Practice Checklist

A mature claims analytics function should:

• define the decision, population, time horizon and financial basis;
• preserve analytical grain and data lineage;
• separate prediction from decision policy;
• use temporal validation and a meaningful baseline;
• report calibration, ranking, error and business metrics;
• test outcomes across relevant customer and operational groups;
• keep human review for consequential decisions;
• monitor drift, overrides, complaints and unintended effects;
• document assumptions, limitations and model ownership;
• retire or rebuild models when their purpose or environment changes.

Chapter Summary

Claims analytics converts claims data into evidence for operational and managerial decisions. Descriptive analytics explains what happened; diagnostic analytics explores why; predictive analytics estimates future or unknown outcomes; and prescriptive analytics recommends actions under constraints. Each form has a distinct purpose and should not be confused with the others.

Effective analysis begins with data grain, quality, lineage and clearly defined financial measures. Segmentation supports differentiated handling, while severity forecasting assists reserving, triage and capacity planning. Anomaly detection identifies unusual patterns for review but cannot, by itself, establish fraud. Leakage analysis connects process controls with avoidable financial loss. Workload prioritization must integrate urgency, customer impact, complexity, financial materiality and investigative signals.

Models should be assessed through discrimination, calibration, error and business metrics, using temporal validation and an appropriate baseline. Decision science extends prediction by considering actions, constraints and consequences. Governance is essential because claims analytics can affect payment, investigation and customer rights. Privacy, explainability, fairness, security, human oversight and auditability are integral to professional practice. The central managerial principle is that analytics creates value only when reliable evidence is translated into fair, controlled and measurable action.

Key Terms

Anomaly detection: Identification of observations or patterns that differ materially from an expected baseline.

Calibration: Agreement between predicted probabilities or expected values and observed outcomes.

Claims leakage: Avoidable cost caused by process failure, inaccurate assessment, weak controls or missed recovery.

Confusion matrix: Table classifying predictions as true or false positives and negatives.

Data lineage: Record of a data element’s origin, transformations and ownership.

Descriptive analytics: Analysis summarizing what has occurred historically.

Diagnostic analytics: Analysis investigating relationships and possible reasons for observed outcomes.

Expected monetary value: Probability-weighted value of consequences across possible states.

False negative: A positive case incorrectly classified as negative.

False positive: A negative case incorrectly classified as positive.

Model drift: Decline in model relevance caused by changes in data, behaviour or environment.

Precision: Proportion of predicted positive cases that are actually positive.

Predictive analytics: Estimation of future or unknown outcomes from available information.

Prescriptive analytics: Recommendation of actions subject to objectives and constraints.

Recall: Proportion of actual positive cases correctly identified.

Segmentation: Division of a portfolio into meaningful groups for analysis or differentiated action.

Severity forecasting: Estimation of the cost of an individual claim or portfolio.

Temporal validation: Testing a model on a later period than the training data.

True positive: A positive case correctly identified by the model.

Review Questions

Short Answer Questions

1. Distinguish descriptive, diagnostic, predictive and prescriptive analytics.
2. Why must the analytical grain of claims data be specified?
3. Explain why an anomaly is not necessarily fraud.
4. Define precision and recall in the context of investigation referrals.
5. Identify four dimensions of claims-data quality.

Long Answer Questions

1. Explain how a severity-forecasting model can support claims reserving, triage and workload management. Discuss the problem of open-claim censoring.
2. Develop a leakage-analysis framework covering process mapping, benchmarking, control design and outcome measurement.
3. Compare rule-based segmentation, statistical clustering and hybrid segmentation for claims operations.
4. Discuss the statistical, operational and business metrics that should be used to evaluate a fraud-detection model.
5. Critically evaluate the ethical, regulatory and governance issues involved in automated claims triage.

Analytical / Application Questions

1. An insurer’s investigation model refers 800 claims. Of these, 240 are confirmed positive. A further 160 positive cases were not referred. Calculate precision and recall, and explain the operational trade-off.
2. Design a four-queue claims-prioritization policy for an insurer facing high catastrophe volume, vulnerable customers and limited specialist capacity. State the entry criteria and safeguards.
3. A supplier’s average repair invoice is 15% above the portfolio average. Explain how you would determine whether this indicates leakage rather than a difference in claim mix.

Multiple Choice Questions with Answers

Question 1

Which type of analytics addresses the question “What should the insurer do next?”

A. Descriptive analytics

B. Diagnostic analytics

C. Predictive analytics

D. Prescriptive analytics

Correct Answer: D. Prescriptive analytics

Question 2

A model refers 200 claims for investigation, of which 50 are confirmed positive. What is its precision?

A. 20%

B. 25%

C. 50%

D. 75%

Correct Answer: B. 25%

Question 3

Which statement best describes an anomaly in claims analytics?

A. It is conclusive proof of fraud

B. It is a pattern that differs from an expected baseline

C. It is always caused by incorrect data

D. It is a claim with above-average severity

Correct Answer: B. It is a pattern that differs from an expected baseline

Question 4

Which metric is expressed in the same units as the predicted claims cost?

A. Mean absolute error

B. Precision

C. Recall

D. ROC-AUC

Correct Answer: A. Mean absolute error

Question 5

Why can random train-test splitting be problematic for claims forecasting?

A. It always reduces sample size

B. It prevents calculation of precision

C. It may allow future information to influence model development

D. It eliminates all missing values

Correct Answer: C. It may allow future information to influence model development

Question 6

Which item is most directly associated with claims leakage?

A. Missed subrogation recovery

B. A calibrated probability

C. A model’s temporal holdout

D. A claim’s median severity

Correct Answer: A. Missed subrogation recovery

Question 7

If TP = 80 and FN = 20, recall is:

A. 20%

B. 40%

C. 80%

D. 100%

Correct Answer: C. 80%

Question 8

What is the principal purpose of calibration assessment?

A. To determine whether predicted probabilities correspond to observed event rates

B. To count the number of policyholders

C. To replace human review entirely

D. To measure only processing speed

Correct Answer: A. To determine whether predicted probabilities correspond to observed event rates

Question 9

Which is an appropriate safeguard when analytics supports consequential claims decisions?

A. Automatic denial for every high-risk score

B. Removal of all audit records

C. Human review, documented overrides and appeal mechanisms

D. Use of unverified external data

Correct Answer: C. Human review, documented overrides and appeal mechanisms

Question 10

Why should high predicted severity and anomaly signals generally be kept distinct?

A. They always use identical variables

B. High cost indicates handling complexity, whereas anomaly indicates a need for evidential review

C. Anomaly models cannot use claims data

D. Severity forecasts are never useful operationally

Correct Answer: B. High cost indicates handling complexity, whereas anomaly indicates a need for evidential review