1. Insurance Fraud and Risk Analytics Foundations

Introduction and Context

Insurance exists because individuals and organizations face uncertain events that may create financial loss. By pooling risks, collecting premiums, and paying valid claims, insurers perform an important economic and social function. This function depends on trust: policyholders must provide accurate information, insurers must assess risk fairly, and claims must be settled according to the contract. Fraud weakens each of these relationships. It increases the cost of insurance, diverts resources from legitimate claimants, damages confidence in the industry, and may expose insurers to regulatory and reputational risk.

Insurance fraud detection has traditionally depended on claim handlers, investigators, underwriting controls, and professional judgement. These remain important, but the scale and complexity of modern insurance require more systematic approaches. Digital transactions, online distribution, connected devices, electronic health records, automated claims platforms, social media, and criminal networks generate large volumes of structured and unstructured data. Risk analytics uses this data to identify unusual patterns, estimate the likelihood of fraudulent activity, prioritize investigations, and improve prevention without treating every unusual claim as dishonest.

The subject therefore involves more than identifying false claims. It concerns the management of fraud risk across the entire insurance value chain—from product design and proposal submission to underwriting, policy servicing, claims payment, recovery, and customer complaints. A sound foundation requires clear distinctions among fraud, abuse, error, moral hazard, and adverse selection. It also requires an understanding of who commits fraud, how schemes operate, where vulnerabilities arise, and how analytical methods should support—not replace—human and ethical judgement.

Learning Objectives

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

1. Define insurance fraud, abuse, error, moral hazard, and adverse selection.

2. Distinguish opportunistic, organized, and internal fraud.

3. Explain how fraud arises across major insurance products and stakeholder groups.

4. Map fraud risks across the policy lifecycle.

5. Describe the business case for fraud risk analytics.

6. Apply basic conceptual frameworks to practical insurance situations.

7. Identify limitations, ethical concerns, governance requirements, and emerging trends in fraud analytics.

The Conceptual Boundaries of Insurance Fraud

Fraud

Insurance fraud is a deliberate act, omission, misrepresentation, or concealment intended to obtain an improper insurance benefit or cause an insurer to provide a benefit that is not contractually due. The benefit may be a policy, a lower premium, an inflated claim payment, an unauthorized policy change, or the avoidance of a legitimate obligation.

The central elements are intention, deception or concealment, and an improper benefit. A claimant who accidentally enters the wrong date on a form has not necessarily committed fraud. A claimant who knowingly changes the date to make a loss appear covered may have done so. In practice, intention is often difficult to establish, which is why insurers should distinguish between suspicion, evidence, and legally proven fraud.

Fraud may occur before a policy is issued, during the policy period, at the time of a claim, or after payment. It may be committed by a policyholder, claimant, intermediary, repairer, medical provider, employee, investigator, broker, or organized criminal group. It can also involve collusion among several parties.

Error

Error is an unintentional inaccuracy, omission, calculation mistake, or process failure. Examples include entering an incorrect vehicle model, transposing digits in a bank account number, or submitting duplicate documentation because of a system problem. Errors may produce incorrect underwriting or claims outcomes, but they do not contain the deliberate intent associated with fraud.

The distinction matters operationally. Treating innocent customers as fraudsters can create unfairness, complaints, regulatory exposure, and reputational damage. At the same time, repeated “errors” involving the same customer, provider, or transaction may become indicators requiring further review. Analytics should therefore identify patterns while investigations determine meaning.

Abuse

Abuse refers to conduct that misuses insurance arrangements or exploits contractual, administrative, or professional processes, often without satisfying the strict legal elements of fraud. Examples include unnecessary medical services, excessive repair charges, unreasonable billing practices, or repeated use of benefits in a manner inconsistent with their intended purpose.

Abuse may be technically permissible in an isolated transaction but harmful when systematic. In health insurance, a provider may bill for unnecessary procedures; in motor insurance, a repairer may recommend replacement rather than economical repair. Abuse is often addressed through contract controls, provider management, utilization review, pricing rules, and professional discipline, as well as through investigation.

Moral Hazard

Moral hazard arises when a person changes behaviour because insurance reduces the financial consequences of risk. It is not itself fraud. A fully insured driver may take less care than an uninsured driver; a property owner may invest less in loss prevention after obtaining extensive cover; or a claimant may be less concerned about the cost of treatment when an insurer pays the bill.

Moral hazard may be ex ante, occurring before a loss, or ex post, occurring after a loss. Ex ante moral hazard concerns reduced preventive effort. Ex post moral hazard may involve exaggerating a genuine loss, seeking unnecessary treatment, or failing to mitigate damage. Deliberate exaggeration can become fraud, but moral hazard as an economic concept includes a broader range of behavioural responses.

Insurers manage moral hazard through deductibles, co-payments, exclusions, policy conditions, warranties, inspection, claims controls, risk-based pricing, and monitoring. These mechanisms must be designed carefully so that they discourage opportunism without preventing legitimate use of insurance.

Adverse Selection

Adverse selection occurs when individuals with higher-than-average risk are more likely to purchase insurance or seek greater coverage, while lower-risk individuals participate less. It results from information asymmetry: applicants may know more about their risk than the insurer.

For example, a person aware of a serious medical condition may seek unusually high health cover without disclosing the condition. A property owner in a flood-prone location may be more motivated to purchase extensive flood insurance than an owner in a low-risk area. Adverse selection is not necessarily fraudulent; it may arise from rational decisions based on private information. Fraud occurs when the applicant deliberately conceals or misrepresents relevant information.

Underwriting, risk classification, waiting periods, deductibles, policy limits, data verification, and regulatory rules concerning disclosure are tools for managing adverse selection. Analytics can reveal risk concentrations and inconsistencies, but it must respect privacy and avoid unjustified discrimination.

Forms and Actors of Insurance Fraud

Opportunistic Fraud

Opportunistic fraud is usually committed by individuals or small groups who exploit an opportunity created by a genuine event or a perceived weakness in controls. A policyholder may add pre-existing damage to a legitimate loss, exaggerate the value of stolen property, or report a minor collision as a more serious accident. Opportunistic fraud is often episodic rather than highly organized.

Its apparent simplicity does not make it insignificant. Because such behaviour is widespread and may involve modest amounts, aggregate losses can be substantial. Opportunistic fraud may also be difficult to detect when the claim otherwise appears normal.

Organized Fraud

Organized fraud involves planned, repeated, and coordinated activity by networks of claimants, professionals, intermediaries, or criminal groups. Examples include staged vehicle collisions, networks of medical providers submitting coordinated false bills, fabricated theft rings, and the use of multiple identities or policies to obtain benefits.

Organized schemes often display relationships among people, addresses, vehicles, telephone numbers, bank accounts, providers, claims, and prior incidents. They may move across jurisdictions or insurers to avoid detection. Network analysis, cross-claim linkage, external intelligence, and specialized investigation are particularly relevant to this form of fraud.

Internal Fraud

Internal fraud is committed by employees, contractors, agents, brokers, or other persons trusted with access to insurance systems and information. It may involve manipulating claim reserves, approving fictitious claims, diverting premium payments, altering policy records, suppressing adverse information, or colluding with external parties.

Internal fraud is especially damaging because insiders understand control weaknesses and may be able to conceal evidence. Strong segregation of duties, access controls, audit trails, rotation of responsibilities, whistle-blower channels, conflict-of-interest declarations, and independent review are necessary safeguards.

These categories can overlap. An employee may collaborate with an organized external network, while an opportunistic policyholder may be assisted by a dishonest repairer. Classification should therefore describe the nature of the scheme rather than assign a single permanent label to a person.

Fraud Across Products, Stakeholders, and the Policy Lifecycle

Fraud risk differs by insurance product because the insured object, information available, claims process, and payment structure differ.

Life and Health Insurance

In life insurance, risks may arise through identity misrepresentation, concealment of medical information, fraudulent death claims, policy ownership manipulation, beneficiary fraud, and suspicious changes shortly before a claim. Health insurance may involve phantom patients, unnecessary treatment, duplicate billing, upcoding, unbundling, forged prescriptions, identity sharing, and provider-patient collusion.

The stakeholders include applicants, policyholders, beneficiaries, medical providers, hospitals, employers, agents, administrators, and claims professionals. The sensitivity of health information requires strict confidentiality and controlled access. Analytical systems should not assume that high medical utilization is fraudulent; legitimate illness, chronic conditions, and unequal access to care can produce unusual patterns.

Motor Insurance

Motor insurance is exposed to staged accidents, inflated repair estimates, false theft reports, identity misrepresentation, pre-existing damage, duplicate claims, phantom passengers, vehicle substitution, and deliberate collisions. Repairers, towing operators, medical providers, legal representatives, claimants, and sometimes employees may participate.

Vehicle identification numbers, accident locations, repair invoices, photographs, telematics, police records, prior claims, and relationships among participants can assist analysis. However, telematics and location data raise consent, privacy, and data security concerns.

Property and Commercial Insurance

Property fraud can involve arson, inflated contents claims, fabricated inventories, misrepresentation of occupancy, concealed maintenance problems, and claims for damage that existed before the insured event. Commercial fraud may involve inflated business interruption losses, fictitious payroll, false invoices, manipulation of stock records, or misstatement of revenue.

These claims often require documentary review, site inspection, accounting analysis, engineering expertise, and assessment of causation. A large loss is not necessarily fraudulent; high-value claims simply require proportionate controls and specialist review.

Travel, Agriculture, and Specialty Insurance

Travel insurance may experience fabricated cancellations, altered medical documents, repeated baggage claims, and claims for pre-existing conditions. Agricultural insurance may be vulnerable to false reporting of crop loss, manipulated acreage, duplicate coverage, or collusion involving assessors. Marine, aviation, cyber, and professional liability products have distinct risks involving valuation, causation, contractual responsibility, and technical evidence.

Lifecycle Mapping

A useful way to understand fraud is to map it across the policy lifecycle:

1. Product design and distribution: unclear wording, excessive complexity, unsuitable products, commission pressure, and weak intermediary oversight can create opportunities for misconduct.

2. Proposal and underwriting: applicants may misrepresent identity, occupation, residence, health, prior losses, vehicle use, assets, or business activity.

3. Policy issuance and servicing: fraud may involve unauthorized discounts, policy backdating, alteration of beneficiaries, payment diversion, or manipulation of coverage dates.

4. Loss occurrence and notification: an insured event may be staged, deliberately caused, falsely dated, or reported late to conceal exclusions or lack of coverage.

5. Claims assessment: documents may be forged, losses exaggerated, duplicate claims submitted, or experts influenced.

6. Settlement and payment: payments may be directed to unauthorized accounts, made to fictitious vendors, or duplicated through system manipulation.

7. Recovery, litigation, and closure: salvage, subrogation, legal costs, and recoveries may be manipulated, while suspicious relationships may be missed if claims data are not retained and linked.

This lifecycle view discourages the narrow assumption that fraud is only a claims problem. Prevention at proposal, distribution, payment, and vendor-management stages may be less costly than discovering fraud after funds have been paid.

Why Risk Analytics Matters

Fraud risk analytics is the structured use of data, statistical methods, rules, machine learning, network techniques, and expert judgement to identify, assess, prevent, investigate, and learn from fraud risk. Its purpose is not merely to generate a fraud score. It supports decisions about which transactions require verification, which providers merit review, where controls should be strengthened, and how resources should be allocated.

The business case has several dimensions. First, fraud creates direct financial leakage through improper claims, premium avoidance, payment diversion, investigation costs, and legal expenses. Second, fraud increases premiums or reduces the funds available for legitimate claims. Third, fraud damages operational efficiency when employees repeatedly review low-risk cases or respond to avoidable complaints. Fourth, fraud may expose an insurer to regulatory sanctions, money-laundering concerns, data breaches, and reputational loss. Finally, effective analytics can improve legitimate customer service by allowing low-risk claims to be processed more quickly.

A mature business case should not be based only on the amount denied. It should consider prevented loss, recovered amounts, investigation costs, false-positive costs, customer impact, control improvement, and the value of deterrence. A model that identifies many suspicious claims but delays thousands of legitimate settlements may not create value.

A Practical Framework for Fraud Risk Analytics

A useful framework consists of five connected activities.

Risk identification

The insurer identifies fraud scenarios by product, process, stakeholder, geography, channel, and control point. Workshops, historical investigations, audit findings, complaints, law-enforcement information, and industry intelligence can contribute. A fraud-risk register may record the scenario, opportunity, potential impact, indicators, existing controls, control owner, and residual risk.

Data preparation and linkage

Relevant data may include proposal forms, policy records, claims, payments, documents, call records, provider information, geospatial data, identity records, and investigation outcomes. Data quality is critical. Missing values, inconsistent names, duplicate identities, changing addresses, and incompatible coding systems can create misleading signals. Entity resolution attempts to determine whether different records refer to the same person, vehicle, organization, or account.

Detection and prioritization

Rules identify known patterns, such as a claim reported immediately after policy inception or multiple claims paid to the same bank account. Statistical models estimate relationships between features and outcomes. Anomaly detection searches for unusual observations without requiring extensive labelled fraud data. Network analytics examines connections among entities. Text and image analytics may identify repeated wording, altered documents, or similarities among photographs, subject to legal and technical safeguards.

No single method is universally superior. Rules are transparent but may be evaded. Supervised models can be accurate where reliable historical labels exist but may reproduce past investigative bias. Unsupervised methods can identify novel patterns but often produce many false positives. A layered approach generally combines automated screening with human review.

Investigation and decision

Analytical alerts are leads, not proof. Investigators examine documents, contact relevant parties, inspect physical evidence, compare timelines, and assess explanations. Decisions should be recorded with reasons and evidence. The outcome may be payment, request for clarification, referral for specialist investigation, partial settlement, denial, recovery action, or referral to an authority, depending on the facts and applicable law.

Learning and feedback

Investigation outcomes should be fed back into the risk system. Models require monitoring for drift, changes in fraud tactics, changes in product design, and changes in customer behaviour. Performance should be assessed using precision, recall, investigation yield, prevented loss, cycle time, customer complaints, and fairness indicators—not merely the number of alerts.

Applied Illustrative Case: The Linked Motor Claims Network

Background

An illustrative insurer, Meridian General, observed increasing motor claims costs in three regional offices. Individual claims did not appear extraordinary. Most involved low-speed rear-end collisions, modest bodily injury claims, and repairs by common workshops.

Situation and problem

Traditional claim rules identified some late notifications and repeated repair invoices, but investigators lacked a clear basis for linking the cases. Management needed to determine whether the increase reflected genuine inflation, changes in repair prices, or coordinated fraud.

Analysis

The insurer consolidated policy, claim, vehicle, driver, repairer, medical provider, address, telephone, bank-account, and accident-location data. Entity-resolution procedures identified variations in names and addresses. A network analysis then revealed that several apparently unrelated claims shared repairers, witnesses, telephone numbers, and medical providers. A time-location review showed clusters of accidents occurring near the same junctions and involving vehicles that appeared repeatedly in different combinations.

The indicators did not establish fraud by themselves. Some shared providers served a legitimate regional market, and accident concentration could reflect road design. Investigators therefore selected a sample for detailed review rather than denying all linked claims.

Approach and outcome

Investigators compared damage patterns, vehicle photographs, police reports, medical records, repair estimates, and statements. Several claims contained inconsistent accident timelines and damage incompatible with the reported collisions. The insurer referred the strongest cases to the appropriate authorities, suspended payments only where contractually and legally justified, and reviewed the conduct of associated vendors. It also introduced stronger vendor due diligence, image capture standards, cross-claim alerts, and management review of linked entities.

The important outcome was not simply the identification of suspicious claims. The insurer improved its understanding of network risk while protecting legitimate claimants from indiscriminate action. The case demonstrates why graph relationships, chronology, physical evidence, and professional judgement should be combined.

Learning points

The case illustrates four principles: isolated claims may conceal network relationships; analytical indicators are not proof; investigation resources should be prioritized; and prevention must include vendors and processes, not only claimants.

Limitations, Risks, and Common Mistakes

Fraud analytics is vulnerable to poor data, weak labels, concept drift, biased historical decisions, fragmented systems, and excessive dependence on a single score. A model trained on previously investigated cases may learn who was investigated rather than who was fraudulent. If certain communities have historically received more scrutiny, automated systems may reproduce that disparity.

False positives impose real costs. They delay valid claims, frustrate customers, increase complaint volumes, and may cause vulnerable policyholders to abandon legitimate entitlements. False negatives allow fraud to continue. The correct balance depends on the product, loss severity, legal requirements, customer vulnerability, and available investigative capacity.

Common mistakes include defining fraud solely as claims fraud, using unverified external data, ignoring internal fraud, failing to document decisions, treating correlation as causation, allowing vendors to operate without oversight, and measuring success only by claim denials. Another mistake is automating adverse decisions without meaningful human review or an opportunity for explanation.

Governance, Ethics, and Professional Responsibility

Fraud controls must be lawful, proportionate, explainable, and consistent with the insurer’s duties to policyholders and other stakeholders. Data protection principles generally require a legitimate purpose, data minimization, security, retention controls, and appropriate transparency. Sensitive personal data—especially health, biometric, financial, and location data—requires heightened safeguards.

Governance should define accountability for data quality, model development, validation, deployment, monitoring, investigation, and adverse decisions. Independent model validation should test performance, stability, explainability, bias, and security. Access to investigative information should follow the principle of least privilege, and audit trails should record who accessed or changed material data.

Professional investigators and analysts should maintain confidentiality, avoid conflicts of interest, preserve evidence, distinguish fact from inference, and provide fair opportunities for clarification. Suspicion must not become accusation. Collaboration with regulators, law enforcement, industry bodies, and other insurers should comply with applicable law and information-sharing standards.

Technology and Emerging Developments

Digital claims platforms increasingly use automated document extraction, identity verification, image analysis, geospatial intelligence, graph databases, and machine-learning models. Generative artificial intelligence may assist with summarizing files, comparing statements, or identifying document inconsistencies, but generated outputs require verification. Deepfakes, synthetic identities, manipulated images, and automated social-engineering attacks are emerging threats.

The future of fraud analytics is likely to involve real-time risk assessment, privacy-preserving data collaboration, explainable artificial intelligence, continuous authentication, and stronger integration of underwriting, claims, payment, and compliance systems. However, technological sophistication cannot compensate for unclear policy wording, weak governance, poor data quality, or inadequate investigative capability. The most effective operating model combines technology with trained professionals, sound controls, and fair customer treatment.

Best-Practice Principles

A sound fraud-risk programme should:

• treat fraud as an enterprise risk rather than only a claims issue;
• maintain a product- and lifecycle-based fraud-risk assessment;
• combine prevention, detection, investigation, recovery, and deterrence;
• use multiple analytical methods and retain human oversight;
• validate data, models, rules, and investigative conclusions;
• monitor false positives, customer outcomes, fairness, and model drift;
• strengthen employee, intermediary, and vendor controls;
• document decisions and preserve evidence;
• protect personal information and restrict access;
• update controls as products, technologies, and fraud schemes change.

Chapter Summary

Insurance fraud is deliberate deception or concealment intended to obtain an improper insurance benefit. It must be distinguished from error, abuse, moral hazard, and adverse selection, although these concepts may interact. Fraud can be opportunistic, organized, or internal, and may involve policyholders, providers, intermediaries, employees, or criminal networks.

Fraud risk exists throughout the policy lifecycle, including distribution, underwriting, servicing, loss notification, claims assessment, payment, recovery, and closure. Product characteristics shape the relevant schemes: health insurance may face unnecessary treatment and false billing, motor insurance may face staged accidents and inflated repairs, and property or commercial insurance may face fabricated losses and manipulated financial records.

Risk analytics supports identification, linkage, prioritization, investigation, prevention, and organizational learning. Effective analytics combines data quality, rules, statistical and machine-learning methods, network analysis, expert judgement, and governance. Alerts are not proof, and models must be monitored for error, bias, drift, privacy risks, and customer impact. The business case includes reduced leakage, better resource allocation, faster legitimate settlements, stronger compliance, and improved trust. Ethical and professional practice requires proportionality, explainability, confidentiality, human review, and fair treatment.

Key Terms

Fraud: Deliberate deception, concealment, or misrepresentation intended to obtain an improper insurance benefit.

Abuse: Misuse or exploitation of insurance arrangements that may not meet the strict legal definition of fraud.

Error: An unintentional inaccuracy, omission, or process mistake.

Moral hazard: Behavioural change caused by reduced financial consequences after insurance is obtained.

Adverse selection: The tendency for higher-risk persons to seek insurance or greater coverage because of information asymmetry.

Opportunistic fraud: Fraud committed by individuals or small groups by exploiting an opportunity, often around a genuine loss.

Organized fraud: Planned, repeated, and coordinated fraudulent activity involving networks or criminal groups.

Internal fraud: Fraud committed by employees, contractors, agents, brokers, or other trusted insiders.

Policy lifecycle: The stages from product design and proposal through underwriting, servicing, claims, payment, recovery, and closure.

Fraud-risk register: A structured record of fraud scenarios, indicators, controls, owners, impacts, and residual risks.

Risk analytics: The use of data and analytical methods to assess, predict, prioritize, and manage risk.

Entity resolution: The process of determining whether records with different identifiers refer to the same entity.

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

Network analysis: Examination of relationships among people, organizations, assets, transactions, and events.

False positive: A legitimate transaction incorrectly flagged as suspicious.

False negative: Fraudulent activity that is not detected by a control or model.

Model drift: Decline in model relevance or performance as behaviour, data, or conditions change.

Ex ante moral hazard: Reduced preventive care or risk-reduction effort before a loss occurs.

Ex post moral hazard: Behaviour after a loss, such as exaggeration or inadequate mitigation, influenced by insurance coverage.

Fraud leakage: Financial value lost through undetected or improperly paid fraudulent activity.

Review Questions

Short Answer Questions

1. What are the essential elements of insurance fraud?

2. How does abuse differ from fraud?

3. Explain the distinction between moral hazard and adverse selection.

4. What is internal fraud, and why is it particularly damaging to an insurer?

5. Why should analytical alerts not be treated as proof of fraud?

Long Answer Questions

1. Explain how fraud can arise at each stage of the insurance policy lifecycle.

2. Compare opportunistic, organized, and internal fraud, using suitable examples.

3. Discuss the business case for using risk analytics in insurance fraud management.

4. Explain the roles of rules, statistical models, anomaly detection, network analysis, and human investigation.

5. Discuss the ethical, governance, and data-protection requirements of fraud analytics.

Analytical / Application Questions

1. A claimant reports a genuine motor accident but includes damage that existed before the accident. Is this error, abuse, moral hazard, or fraud? Explain the reasoning and identify evidence an investigator should seek.

2. Several health claims involve different patients but the same bank account, provider address, telephone number, and unusual billing pattern. Design an initial analytical and investigative response while protecting legitimate claimants.

3. An insurer’s fraud model produces many alerts in one geographic community and significantly delays claims there. Analyze possible causes, risks, performance measures, and governance actions before the model is continued or withdrawn.

22. Multiple Choice Questions with Answers

Question 1

Which feature most clearly distinguishes insurance fraud from an ordinary error?

A. The transaction produces a financial loss

B. The event occurs during the claims process

C. There is deliberate deception or concealment for improper benefit

D. The insurer requires additional documentation

Correct Answer: C. There is deliberate deception or concealment for improper benefit

Question 2

A policyholder exaggerates the value of property stolen in an otherwise genuine theft. This is best classified as:

A. A data-quality error only

B. Opportunistic fraud

C. Adverse selection

D. Internal fraud

Correct Answer: B. Opportunistic fraud

Question 3

Which situation best illustrates adverse selection rather than fraud?

A. An applicant knowingly hides a diagnosed illness

B. A claimant submits an altered medical invoice

C. A high-risk person is more motivated to purchase extensive cover because of private risk information

D. An employee approves a fictitious claim

Correct Answer: C. A high-risk person is more motivated to purchase extensive cover because of private risk information

Question 4

Why can network analysis be especially useful for organized insurance fraud?

A. It automatically proves criminal intent

B. It examines relationships among people, providers, assets, and claims

C. It eliminates the need for investigators

D. It guarantees that all linked claims are fraudulent

Correct Answer: B. It examines relationships among people, providers, assets, and claims

Question 5

Which activity occurs primarily during the underwriting stage of the policy lifecycle?

A. Diverting a settlement to an unauthorized bank account

B. Manipulating salvage recovery after payment

C. Misrepresenting occupation, residence, or prior losses

D. Submitting duplicate repair invoices after an accident

Correct Answer: C. Misrepresenting occupation, residence, or prior losses

Question 6

What is a major risk of training a fraud model only on previously investigated cases?

A. The model cannot process numerical data

B. The model may learn historical investigation bias rather than fraud itself

C. The model will always produce too few alerts

D. The model cannot identify internal fraud

Correct Answer: B. The model may learn historical investigation bias rather than fraud itself

Question 7

Which measure is most appropriate for evaluating a fraud analytics programme?

A. The number of claims denied, regardless of legitimacy

B. The number of automated alerts alone

C. A combination of prevented loss, investigation yield, false positives, customer impact, and cycle time

D. The number of policyholders investigated in one month

Correct Answer: C. A combination of prevented loss, investigation yield, false positives, customer impact, and cycle time

Question 8

An employee changes policy records and approves payments to an external accomplice. This is an example of:

A. Internal fraud with possible collusion

B. Adverse selection only

C. Ex ante moral hazard only

D. Random sampling error

Correct Answer: A. Internal fraud with possible collusion

Question 9

Why should a high-risk analytical alert not automatically lead to claim denial?

A. Analytics has no role in insurance decisions

B. An alert is an investigative lead and may represent a legitimate unusual claim

C. All claims must be paid immediately

D. Fraud can only be established by customer admission

Correct Answer: B. An alert is an investigative lead and may represent a legitimate unusual claim

Question 10

Which control most directly addresses unauthorized employee manipulation of claim payments?

A. Increasing advertising expenditure

B. Removing all policy deductibles

C. Segregation of duties, access controls, and audit trails

D. Automatically rejecting claims from new customers

Correct Answer: C. Segregation of duties, access controls, and audit trails