Synthetic Data

Synthetic data is artificially generated information designed to reflect the statistical patterns, relationships, and business behaviors found in real data without reproducing actual policyholder, claimant, employee, or partner records.

How insurers can use artificially generated information to accelerate AI development, strengthen testing, improve collaboration, and support responsible innovation.

Synthetic data is artificially generated information designed to reflect the statistical patterns, relationships, and business behaviors found in real data without reproducing actual policyholder, claimant, employee, or partner records. For Property & Casualty insurers, it can support model development, software testing, training, demonstrations, vendor collaboration, privacy-preserving research, and rare-event simulation.

EXECUTIVE PREMISE

Synthetic data creates value when it solves a defined business or development problem, is validated against the intended use, and is governed as a controlled enterprise asset. It complements production data; it does not eliminate the need for representative real-world evidence, professional judgment, privacy controls, or ongoing monitoring.

Insurance organizations depend on large volumes of sensitive and fragmented information to build and test AI systems. Access is frequently constrained by privacy requirements, contractual limitations, security controls, data scarcity, inconsistent quality, and the practical difficulty of assembling representative examples. Synthetic data can reduce these barriers by generating controlled datasets that reflect important business patterns without directly exposing real customer records.

The capability is not inherently safe or accurate. A synthetic dataset may reproduce bias, omit critical edge cases, exaggerate relationships, create unrealistic combinations, or leak information from its source data. For that reason, synthetic data requires the same enterprise discipline applied to other important data and AI assets: approved purpose, accountable ownership, source authorization, lineage, validation, security, privacy assessment, version control, monitoring, and retirement.

This guide provides an executive and implementation reference for P&C insurers. It explains core concepts, generation methods, quality and privacy validation, applications across claims and underwriting, risks, governance, implementation practices, business measurement, and practical case studies. Throughout the guide, insurance professionals retain authority for consequential decisions.

Definitions, boundaries, and executive context

Synthetic data is information created by software, simulation, statistical models, generative models, rules, or combinations of these methods. It is designed to resemble selected characteristics of real data while representing artificial records, events, documents, images, or relationships.

Technically, a generator learns or encodes distributions, correlations, sequences, constraints, and domain rules, then produces new observations sampled from that learned or specified representation. The output may be tabular, textual, visual, temporal, relational, or multimodal.

In P&C insurance, synthetic data may represent claims, policies, insureds, claimants, submissions, exposures, documents, transactions, providers, attorneys, repair facilities, catastrophe events, or networks. The records are not intended to represent actual individuals or claims, even when their structures and patterns resemble production information.

A dataset should be called synthetic only when its artificial origin, intended purpose, generation method, validation status, and limitations are documented. The term should not be used as a blanket assurance that data is private, unbiased, or production-ready.

Business drivers, strategic value, and appropriate expectations

Synthetic data may be unnecessary when approved, representative, well-governed production data is readily available; when the use case depends on precise reconstruction of real claim evidence; when validation cannot establish fidelity; or when the generator would be more complex and costly than controlled access to existing data.

Fully synthetic, partially synthetic, hybrid, simulated, and multimodal data

Every record and value is generated. No row is intended to correspond to a real policy, claim, or person. Fully synthetic datasets can reduce direct exposure of source records, but they may lose important detail or utility if the generator does not preserve complex relationships.

Selected fields, documents, or observations are generated or replaced while other elements remain real. This can preserve operational realism, but the retained data must still be protected and evaluated for re-identification risk.

Real and synthetic records are combined. Hybrid datasets are often useful when rare classes need augmentation or when production examples provide the baseline and synthetic observations broaden coverage. Clear labels and provenance are essential.

Rules, process models, actuarial assumptions, engineering models, catastrophe simulations, and workflow emulators can generate controlled scenarios. These methods are especially useful when physical, contractual, or business constraints are explicit.

Statistical, machine-learning, generative, and foundation models can learn patterns from data and produce new rows, sequences, text, images, or relationships. These methods may capture complexity but require testing for memorization, implausible outputs, and hidden bias.

Generation methods, constraints, and lifecycle

Define the business purpose and permitted use.

Identify authorized source data, rules, and constraints.

Select a generation method appropriate to the data type and risk.

Create candidate synthetic datasets.

Validate statistical, business, privacy, and model utility.

Approve and version the dataset.

Deploy within defined environments and workflows.

Monitor outcomes, changes, and incidents.

Refresh or retire when conditions change.

Insurance generators should enforce logical and regulatory constraints where appropriate. Examples include valid policy periods, consistent dates, permitted coverage combinations, plausible reserve and payment development, geographic relationships, claim-event chronology, and document-to-record consistency.

Synthetic data can intentionally oversample rare but important events. The resulting dataset must clearly distinguish scenario prevalence from real-world prevalence so that model calibration and executive interpretation are not distorted.

Fitness, authorization, representativeness, and lineage

Datasets should preserve distinctions among observed facts, verified facts, inferred values, estimates, labels, and synthetic values. Blurring these categories creates false certainty and weakens explainability.

Reducing exposure without assuming zero risk

Synthetic records can still reveal proprietary underwriting rules, pricing logic, claim strategies, portfolio characteristics, fraud indicators, or operational practices. Governance should protect both personal information and enterprise intellectual property.

Proving utility, realism, privacy, and fitness for purpose

VALIDATION PRINCIPLE

A synthetic dataset is not valid in the abstract. It is valid—or invalid—for a defined use, population, period, workflow, and risk level.

Subject-matter experts should review sampled records, scenarios, and exceptions. Claims leaders may evaluate chronology, medical and litigation development, reserve progression, and document relationships. Underwriters may assess appetite, exposure, classification, and portfolio realism. SIU professionals may review network structures and investigative plausibility.

A common approach is to train on synthetic data and test on a protected real-world holdout set, then compare performance with models trained on real or hybrid data. The evaluation should include calibration, subgroup performance, robustness, explainability, and workflow outcomes—not accuracy alone.

Using synthetic data across the claim lifecycle

Synthetic claim records, narratives, images, and documents can help test intake classification, routing, severity indicators, missing-information detection, and catastrophe surge workflows. Scenarios should include normal, ambiguous, incomplete, and adversarial inputs.

Synthetic development paths can supplement rare high-severity examples, test reserve-change alerts, and evaluate sensitivity to new medical, legal, repair, or exposure information. Calibration must reflect real prevalence and development patterns.

Artificial chronologies may represent attorney involvement, demand activity, venue, treatment escalation, communication patterns, and procedural events. The data should support earlier review—not determinations about legal strategy or claim value without professional assessment.

Synthetic medical narratives and billing sequences can support extraction, chronology, summarization, and escalation testing. Generated content should never be represented as clinical truth or used to replace qualified medical review.

Synthetic scenarios can test early identification of third parties, contractors, defective products, comparative negligence, evidence preservation, and recovery workflows.

Controlled claim simulations allow professionals to practice investigation, escalation, documentation, and management response without exposing real customers. Training outcomes should be reviewed separately from model-development outcomes.

Submission intelligence, appetite, risk assessment, and portfolio simulation

Synthetic submissions can help test document classification, extraction, completeness checks, duplicate detection, normalization, and routing across varied formats and lines of business.

Artificial risks can exercise appetite rules and AI recommendations across industry, geography, construction, occupancy, limits, loss history, and exposure combinations. The test set should include boundary cases and conflicting evidence.

Synthetic narratives and documents can evaluate whether AI summaries remain grounded, distinguish missing from negative information, and surface material contradictions.

Synthetic portfolios can support concentration analysis, stress testing, emerging-risk exploration, and scenario planning. They should not be mistaken for actuarial estimates without appropriate actuarial methods and validation.

Controlled cases can improve consistency in referral preparation, documentation, and escalation while preserving underwriting authority over risk selection, terms, pricing, and adverse decisions.

Synthetic scenarios for detection, prioritization, and investigation

Confirmed fraud examples are relatively scarce, sensitive, and often heterogeneous. Synthetic data can broaden scenario coverage, create controlled networks, and test system behavior against evolving patterns without distributing real investigative records.

A generator creates a set of artificial claims connected through shared providers, addresses, telephone numbers, repair facilities, attorneys, and vehicles. Some links are benign and others form coordinated patterns. Analysts use the scenario to test entity resolution, graph construction, alert thresholds, explanation, and investigator workflow—not to establish that any comparable real-world relationship proves fraud.

Generating and testing unstructured and multimodal insurance information

Insurers can generate artificial forms, letters, notes, estimates, invoices, medical narratives, demand packages, engineering reports, submissions, and policy-related content to test classification, extraction, summarization, retrieval, and workflow routing.

Synthetic prompt-and-document sets can test groundedness, citation accuracy, omission, contradiction handling, refusal, uncertainty, output structure, prompt injection, and data leakage. Evaluation sets should include realistic failures and adversarial examples.

Artificial corpora can support repeatable retrieval benchmarks, permission tests, metadata filtering, deletion tests, and citation checks. Final validation should still use authorized representative enterprise content because synthetic text may not capture the full ambiguity and vocabulary of production documents.

EVIDENCE RULE

A generated answer should remain traceable to approved evidence. Synthetic evaluation content can test the mechanism, but it should not weaken requirements for source access, citations, uncertainty, and human review.

What executives and implementation teams must control

Synthetic data cannot recreate unknown future events with certainty, substitute for real-world evidence in claim or underwriting decisions, or guarantee privacy and fairness. It is a controlled approximation whose value depends on disciplined use.

Ownership, approval, documentation, monitoring, and controlled change

EXECUTIVE PREMISE

Governance should make responsible synthetic-data use repeatable and scalable. It should define who may create, approve, use, change, share, and retire datasets—and what evidence is required at each decision point.

Approve the business purpose, scope, users, and authority boundaries.

Confirm lawful and authorized source-data use.

Define generation, validation, security, privacy, and documentation standards.

Review dataset and model performance before use.

Record approvals, versions, limitations, and permitted environments.

Monitor quality, privacy, adoption, incidents, and business outcomes.

Control refresh, expansion, sharing, and retirement.

Synthetic data may support models and workflows, but consequential insurance decisions remain with authorized professionals. Human review should be defined by decision materiality, uncertainty, customer impact, and applicable requirements.

Platforms, integration, environments, MLOps, and reusable services

Illustrative enterprise implementation

A national P&C insurer used synthetic data to expand claims-AI development while reducing direct exposure of sensitive claim records. The program succeeded because it began with defined claim decisions, embedded business validation, and scaled through evidence-based governance gates.

The insurer operated across personal auto, commercial auto, property, and workers’ compensation. Its AI teams faced slow access to development data, uneven representation of severe and litigated claims, and restrictions on sharing records with vendors and distributed teams.

The team inventoried source systems, documents, decisions, data rights, quality gaps, and existing controls. It selected bodily injury claim triage as the first pilot because the workflow had measurable outcomes, experienced reviewers, and clear escalation points.

The program combined de-identified structured claim features, constrained statistical generation, synthetic narratives, and scenario rules for attorney involvement, treatment escalation, reserve movement, and demand activity. Rare scenarios were deliberately oversampled and labeled so model calibration would not treat them as natural prevalence.

Generated loss descriptions and intake combinations tested routing, missing information, and escalation.

Synthetic medical and legal documents tested classification, extraction, chronology, and summarization.

Artificial claim timelines broadened coverage of changing attorney, venue, treatment, and demand signals.

Synthetic development paths supplemented limited tail examples and supported stress testing.

The insurer reduced data-provisioning delays, improved repeatability of model and integration tests, expanded rare-scenario coverage, and created reusable claims datasets. Production approval still depended on performance against real-world holdout data and human-reviewed pilot outcomes.

Illustrative submission and portfolio implementation

A commercial insurer used synthetic submissions and portfolios to improve underwriting-AI testing without distributing real applicant files. The program focused on evidence organization and appetite support while retaining underwriter authority over selection, terms, and pricing.

Submissions arrived through brokers in inconsistent formats and included applications, schedules, loss runs, engineering reports, financial information, emails, and supplemental forms. Access constraints slowed document-intelligence testing and vendor collaboration.

The team generated coordinated artificial submissions containing structured records and linked documents. Business rules enforced consistent dates, classifications, locations, limits, and loss histories. Scenarios included missing information, conflicting versions, borderline appetite, and concentration changes.

Varied document packages tested classification, extraction, completeness, and routing.

Generated schedules and reports tested tables, terminology, versions, and contradictions.

Artificial risks exercised in-appetite, out-of-appetite, and referral conditions.

Synthetic books supported concentration and emerging-risk exploration.

Underwriters reviewed realism and decision usefulness; data teams measured distributions and consistency; privacy teams assessed leakage; and model teams tested on protected real submissions. The program improved testing coverage and reduced the need to copy sensitive submission files into development and demonstration environments.

Illustrative network and investigator implementation

An insurer expanded fraud-model and SIU workflow testing by creating artificial claim networks with controlled suspicious and benign patterns. The primary value was broader scenario coverage and repeatable investigator training—not automatic fraud determination.

Confirmed fraud cases were limited, highly sensitive, and difficult to share. Existing models performed well on known patterns but had limited testing against coordinated networks and emerging behaviors.

The generator created artificial claims, identities, addresses, devices, providers, attorneys, repair facilities, payments, and vehicles. It inserted both legitimate shared relationships and suspicious clusters so that systems had to distinguish common links from coordinated patterns.

Evaluate features, thresholds, and class imbalance.

Test graph construction, link explanations, and cluster behavior.

Assess referral queues, evidence summaries, and workload balancing.

Practice evidence review, documentation, escalation, and disposition.

SIU experts reviewed plausibility and investigative value. Technical teams measured detection, false positives, network coverage, and explanation quality on synthetic scenarios, then tested final models against authorized real-world holdouts. The program created a reusable test library and improved consistency of workflow validation.

When to invest, pilot, scale, or defer

Define the business or development constraint.

Confirm that synthetic data is an appropriate option.

Assess source authority and readiness.

Define permitted use and risk tier.

Establish generation and validation requirements.

Pilot in a controlled environment.

Scale, correct, or retire based on evidence.

A phased 12–18 month roadmap

IMPLEMENTATION PHILOSOPHY

Begin with a narrow, measurable use case; build reusable governance and technical capabilities; and expand only after utility, privacy, workflow, and operating controls are demonstrated.

Define objectives, sponsorship, use-case portfolio, risk appetite, and funding.

Establish ownership, source authorization, standards, environments, and validation plans.

Generate, validate, and test one focused dataset in a controlled workflow.

Add use cases, data types, partners, and reusable services after evidence gates.

Embed registry, automation, monitoring, and consumption patterns into the operating model.

Refresh, benchmark, audit, manage incidents, and retire obsolete assets.

Measuring business, operational, AI, governance, and financial value

Synthetic-data success should be measured through the chain from dataset quality to development activity, workflow adoption, model behavior, risk control, and business outcome. A realistic dataset that is never used—or a widely used dataset that weakens production performance—is not successful.

Trusted insurance AI through governance, evidence, and human expertise

Charlee.ai views synthetic data as one enabling capability within a broader responsible-AI strategy. Its purpose is to help insurers learn, test, and innovate while protecting sensitive information and preserving professional decision authority.

Models inherit the strengths and weaknesses of their data. Synthetic generation does not remove the need for source quality, lawful use, clear definitions, provenance, validation, and monitoring.

Claims professionals, underwriters, investigators, actuaries, legal teams, and executives provide context that statistical similarity cannot establish. They remain responsible for consequential insurance judgments.

Predictable standards and evidence requirements reduce uncertainty and make it easier to move successful use cases from experiment to production.

Reusable datasets, shared validation services, common controls, and integrated workflows produce more durable value than disconnected pilots.

AI augments human expertise.

Governance enables innovation.

Business outcomes matter most.

Enterprise knowledge creates competitive advantage.

Responsible AI is a long-term commitment.

Executive and technical reference

The policies, roles, controls, evidence, and oversight used to manage AI throughout its lifecycle.

A computational system that transforms inputs into predictions, classifications, generated content, or other outputs.

A defined procedure or set of computational steps.

Modification of real data intended to prevent identification; effectiveness depends on context and attack risk.

Systematic distortion that may affect representation, performance, or outcomes.

Review by qualified domain professionals to determine whether data and scenarios are plausible and useful.

Ongoing observation of data, models, use, incidents, and outcomes.

Accountability and controls for data definition, quality, access, lineage, use, retention, and protection.

Traceable history of data origins, transformations, versions, and movement.

The degree to which data is accurate, complete, consistent, timely, valid, traceable, and fit for purpose.

A formal method that limits how much an individual record can influence a released result.

AI-assisted classification, extraction, organization, retrieval, and summarization of documents.

AI capabilities governed and operated as reusable business assets across an organization.

Information that helps users understand the basis, limitations, and appropriate use of an AI output.

Assessment and management of performance and outcome differences across relevant groups and contexts.

Further training of a pretrained model on task- or domain-specific data.

A large pretrained model that can be adapted to many tasks.

AI that creates new text, images, code, audio, data, or other content.

A design in which qualified people review, approve, correct, or oversee AI-supported work.

A model trained on large text corpora to process and generate language.

Methods that learn patterns from data to perform tasks.

Information describing data, such as source, date, owner, schema, rights, and classification.

Change that causes model inputs, relationships, or performance to differ from validated conditions.

Methods for analyzing and generating human language.

Information that identifies or can reasonably be linked to an individual.

Methods that estimate future or unknown outcomes from current and historical data.

Information generated or used in live business operations.

Structured review and testing to confirm that requirements are met.

A design that retrieves approved evidence and provides it to a generative model.

The degree to which synthetic and reference data share measured distributions and relationships.

Artificially generated information designed to reflect selected characteristics of real data.

A versioned collection of synthetic records or content prepared for an approved purpose.

A controlled system used to evaluate software, models, integrations, or workflows.

Data used to fit or adapt a model.

Clear communication of purpose, data use, methods, limitations, and accountability.

Information not organized in a fixed tabular schema, including documents, notes, images, and audio.

Evidence-based evaluation of fitness, quality, privacy, risk, and performance.

Management of identifiable changes to data, code, models, configuration, and documentation.

Methods and practices that help people understand and evaluate AI behavior.

Learning paths and connected topics

Recommended illustration: Place Synthetic Data at the center with connected hubs for foundations, governance, claims, underwriting, fraud, enterprise AI, and implementation.

Preferred format: Hub-and-spoke map

Production notes: Use as a navigation aid for web and downloadable editions.

A boardroom-ready summary

Synthetic data is artificially generated information that reflects selected characteristics and relationships of real data without reproducing actual customer records. It supports AI development, testing, training, demonstrations, and collaboration. It complements production data; it does not replace it.

Start with the business problem.

Governance enables innovation.

Human expertise remains central.

Validate statistical fidelity, business realism, privacy, and utility.

Measure business outcomes, not technical activity alone.

Strategy and alignment → governance and data readiness → pilot → controlled expansion → enterprise integration → continuous improvement.

Integrating synthetic data with existing enterprise controls

Synthetic data should be incorporated into existing AI, data, privacy, cybersecurity, legal, compliance, enterprise-risk, and audit programs rather than governed through a disconnected parallel structure.

Continuing education for enterprise AI leaders

Authoritative categories for future editions

This guide combines insurance practice, enterprise AI governance, data management, privacy, security, synthetic-data methods, and implementation guidance. Future editions should rely on current, authoritative sources and record versions and dates used for material control or policy decisions.

Recommended illustration: Synthetic Data at the center of AI research, insurance practice, governance, data management, privacy/security, academic research, technology innovation, and the Knowledge Center.

Preferred format: Knowledge ecosystem

Production notes: Keep the graphic evergreen and vendor-neutral.

Synthetic data should be viewed as a strategic enterprise capability that supports responsible innovation. Its greatest value lies not simply in creating artificial records, but in enabling organizations to learn and test more safely, collaborate more effectively, build better AI systems, and deliver measurable outcomes while preserving customer trust and professional accountability.

FINAL EXECUTIVE MESSAGE

The future of insurance AI will belong to organizations that combine technology, governance, trusted data, and human expertise into a unified enterprise strategy. Synthetic data can accelerate that journey when it is governed, validated, and used with discipline.