Use Cases

Insurance

Claims Severity and Fraud Risk Intelligence

Model6 can support two separately governed predictive tracks. A claims severity model estimates an insurer-defined cost, complexity, litigation, or specialist-review outcome. A fraud-risk model identifies patterns associated with an insurer-defined investigation or referral outcome.

Each model requires its own labels, training data, validation, threshold, reason indicators, reviewers, and downstream actions. Scores can prioritize professional review, but they do not determine fraud, coverage, liability, reserves, settlement, payment, or claim outcome.

Why This Use Case Matters

Early claim signals can improve specialist allocation

Information available at intake and early handling may indicate elevated complexity, potential severity, litigation exposure, or investigation need before the file develops further.

Static rules miss interacting and changing patterns

Loss, policy, claimant, provider, repair, payment, transaction, and behavioral signals can interact in ways that simple thresholds do not capture consistently.

Fraud-risk prioritization requires strong safeguards

A high score is not proof of fraud. False positives can create customer harm, complaint risk, wasted investigation capacity, and unfair treatment without controlled review and monitoring.

Severity and Fraud Risk Are Distinct Questions With Different Review Paths

See how Bay6 AI helps insurers identify claims that may require earlier specialist, complex-claims, or SIU review using governed predictive signals.

Severity may mean ultimate cost, a severity band, injury complexity, litigation, catastrophe exposure, or specialist need. Fraud risk may mean SIU referral, confirmed misrepresentation, organized activity, provider concerns, or another governed outcome. These definitions cannot be blended into one undifferentiated score.

Early claim files contain incomplete information while complexity and investigation needs continue to develop.
Claim, policy, payment, provider, repair, customer, and transaction data are fragmented across systems.
Historical severity and fraud labels may be inconsistent, delayed, sparse, or shaped by prior investigation practices.
Severe and confirmed fraud outcomes are rare relative to overall claim volume.
Sensitive or proxy variables can create unfair treatment, regulatory exposure, or reputational harm.
Leaders need evidence connecting scores, reasons, referrals, professional decisions, overrides, outcomes, and model versions.

Bay6 designs severity and fraud-risk models as separate workstreams. Model6 uses the insurer’s approved data and outcome definition to build, validate, score, and monitor each model independently. Thresholds and reason indicators are aligned to specialist capacity, customer impact, and the permitted review process.

Agent6 can route validated signals into complex-claim, specialist, SIU, quality, or evidence-collection workflows. Connect6 or PolicyBuddy can support approved communication and information collection. Forge6 supports data readiness, risk design, fairness review, workflow mapping, and governance.

Operating principle: Severity scores prioritize professional claim review. Fraud-risk scores prioritize fair investigation review. Neither score is a final insurance or fraud decision.

How the Claims Severity and Fraud-Risk Workflow Works

The workflow keeps the two model tracks separate from outcome definition through monitoring and downstream action.

Define separate outcomes

Set the severity outcome, fraud-risk outcome, eligible claims, observation points, time horizons, exclusions, and permitted uses independently.

Prepare approved data

Inventory, join, time-align, document, and review claim, policy, payment, provider, repair, transaction, investigation, and outcome data.

Build separate models

Develop severity and fraud-risk models using features and methods appropriate to each approved business question.

Validate performance and fairness

Test calibration, lift, error, precision, recall, false positives, false negatives, leakage, stability, proxy risk, and segment performance.

Deliver scores and reasons

Provide distinct probabilities, values or bands, priority categories, and approved reason indicators through batch or API scoring.

Route and monitor review

Send claims to the correct specialist or SIU path and track referrals, decisions, overrides, customer impact, drift, and model versions.

What Model6 Helps Claims and SIU Teams Prioritize

The two tracks remain operationally separate even when they use some of the same claim data.

claims-severity-fraud-risk-intelligence-capability

Claims severity model

Estimate a defined cost, severity band, complexity, litigation, catastrophe, or specialist-review outcome within the approved scope.

Fraud-risk model

Identify patterns associated with an elevated probability of the insurer-defined investigation outcome without declaring fraud.

Batch and real-time scoring

Score eligible claims at intake, early handling, review, or another approved point through scheduled batches or APIs.

Reason and priority indicators

Provide approved factors that help reviewers understand why a claim received a higher or lower review priority.

Threshold and capacity design

Align severity and fraud-risk thresholds separately with specialist capacity, investigation cost, customer impact, and error tradeoffs.

Model monitoring and traceability

Track calibration, error, precision, recall, lift, drift, segment performance, referrals, decisions, overrides, outcomes, and versions.

What Insurers Can Improve

Measure each model separately against its professional review path, customer impact, and business objective.

Earlier identification of potentially severe or complex claims
Specialist and SIU workload prioritization
Severity calibration, error, and band accuracy
Fraud-risk precision, recall, referral yield, and false-positive impact
Professional acceptance, override, disposition, and review efficiency
Fairness, complaints, drift, retraining, and end-to-end traceability

Outcome focus: A governed claims intelligence workflow directs limited specialist capacity toward the right review while preserving professional judgment and customer safeguards.

Pilot Measurement Areas

Choose one model track first. Define the claim type, outcome, observation point, population, reviewer group, threshold, workflow, and safeguards.

Measurement Area	What to Track
Model performance	Calibration, lift, expected-value error, band accuracy, precision, recall, false positives, false negatives, and uncertainty.
Operational usefulness	Specialist or SIU acceptance, review yield, queue time, workload distribution, completion, disposition, and cycle time.
Threshold and capacity fit	Referral volume, capacity use, marginal yield, investigation cost, customer impact, and alternative threshold performance.
Reason and reviewer quality	Reason usefulness, professional agreement, overrides, override rationale, additional evidence, and decision consistency.
Fairness and customer impact	Performance by approved segments, proxy analysis, adverse impact, additional verification, delays, complaints, and remediation.
Model health and governance	Data quality, feature availability, drift, label change, version comparisons, approvals, incidents, and audit records.

Frequently Asked Questions

Have questions? Browse our FAQs to learn more about how Bay6 AI works and how it supports your organization’s goals.

What is claims severity and fraud risk intelligence?

It uses separately governed predictive models to estimate a defined severity outcome and identify patterns associated with elevated investigation risk for professional review.

Are severity and fraud risk one combined model?

No. They require different outcomes, labels, data assumptions, validation, thresholds, reviewers, actions, and monitoring. They should remain separate model tracks.

Does a high fraud-risk score mean fraud occurred?

No. It indicates patterns associated with the insurer-defined referral or investigation outcome. It should never be treated as proof or as the sole basis for adverse claim action.

Can a severity model set reserves or claim value?

No. It can estimate an approved severity outcome or priority. Reserves, valuation, coverage, liability, settlement, and payment remain with authorized claims and actuarial professionals.

What should an initial pilot include?

Start with one model track, defined claim type, outcome, observation point, historical period, population, reviewer group, threshold, workflow, fairness review, and governance plan.

Responsible AI and Claims Oversight

Model6 supports prioritization. Fraud findings, coverage, liability, reserves, settlement, payment, and claim authority remain with authorized insurer professionals.

Define severity and fraud-risk outcomes separately with clear populations, time horizons, observation points, exclusions, and permitted uses.
Use relevant, permitted, documented data and review label quality, selective-investigation bias, sensitive attributes, proxies, and adverse impact.
Validate performance by model, claim type, product, segment, and threshold before deployment.
Present fraud-risk outputs as review signals, never as proof of fraud or the sole basis for adverse action.
Require claims, SIU, actuarial, legal, compliance, or other authorized review before material customer or claim action.
Monitor scores, reasons, referrals, decisions, overrides, customer impact, drift, incidents, and complaints by model version.

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Review one severity or fraud-risk use case and determine whether the outcome, data, labels, review capacity, thresholds, customer safeguards, and governance support a Model6 pilot.