10. Fraud, Financial Crime and Claims Investigation

Introduction and Context

Insurance depends on a basic social and commercial promise: the policyholder pays a premium, and the insurer provides financial protection when a specified uncertain event occurs. Fraud attacks that promise by introducing deliberate deception into underwriting, policy servicing, claims, or payment. It increases loss costs, distorts pricing, disadvantages honest customers, weakens confidence in insurance institutions, and may expose insurers to regulatory, criminal, and reputational consequences.

Fraud is not limited to exaggerated motor claims or fabricated losses. It may occur when an applicant conceals a material fact, an intermediary manipulates a proposal, a repairer submits false invoices, a claimant stages an accident, an employee diverts settlement funds, or a criminal network uses insurance transactions to launder money. Financial crime is a wider category that includes money laundering, terrorist financing, bribery, corruption, sanctions evasion, identity theft, cyber-enabled crime, and the misuse of insurance products for unlawful purposes.

Claims investigation therefore requires more than suspicion. It is a disciplined process of identifying risk indicators, preserving evidence, testing competing explanations, interviewing relevant persons, applying analytics, and making a fair and legally defensible decision. The investigator must protect the insurer from illegitimate payments while ensuring that genuine claims are not delayed or rejected merely because they resemble fraudulent cases.

This chapter examines fraud across the insurance value chain. It links fraud detection with underwriting discipline, claims governance, anti-money-laundering controls, forensic methods, technology, professional ethics, and lawful referral procedures.

Learning Objectives

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

1. Distinguish insurance fraud from error, negligence, breach of policy condition, and ordinary claims disagreement.

2. Explain common forms of underwriting fraud, application misrepresentation, staged loss, inflated claims, and organized fraud.

3. Apply red-flag models and risk-based investigation methods.

4. Plan investigative interviews and manage documentary, digital, physical, and testimonial evidence.

5. Use basic forensic, analytical, and technological techniques in claims investigation.

6. Explain the relevance of anti-money-laundering and counter-financing-of-terrorism controls to insurers.

7. Evaluate ethical, privacy, legal, and governance issues in fraud decisions.

8. Recommend proportionate actions, including referral to internal governance bodies, regulators, or law-enforcement agencies.

The Nature and Scope of Insurance Fraud

Insurance fraud is a deliberate act, omission, or scheme intended to obtain an improper insurance benefit or to cause an insurer to suffer an improper loss. The essential elements are usually deception, materiality, intent, and an attempted or actual financial benefit. A clerical error is not automatically fraud. A claimant who misunderstands a policy may be wrong, but not necessarily dishonest. A fraudulent case normally requires evidence that the person knowingly presented false information, concealed a material fact, fabricated an event, or collaborated in an unlawful arrangement.

Fraud may be classified by the stage at which it occurs. Application fraud involves false statements or omissions during proposal and underwriting. Policy-service fraud may involve false changes of address, beneficiary manipulation, or identity substitution. Claims fraud concerns the event, circumstances, loss amount, ownership, documentation, or payment destination. Internal fraud is committed by employees, agents, assessors, or other persons with access to insurance systems. Organized fraud involves coordinated activity by multiple participants, often across several claims or insurers.

Another useful distinction is between hard fraud and soft fraud. Hard fraud involves inventing or deliberately causing a loss, such as staging a collision or submitting a claim for property that never existed. Soft fraud involves exaggerating a genuine loss, overstating the value of damage, or adding unrelated items to a valid claim. The distinction is analytically useful, although both forms can produce serious legal and ethical consequences.

Fraud risk is also influenced by the insurance product. Motor insurance may face staged accidents, phantom passengers, vehicle identity substitution, inflated repair invoices, and theft rings. Property insurance may face arson, inflated inventories, fraudulent invoices, and concealment of pre-existing damage. Health insurance may be exposed to unnecessary treatment, duplicate billing, phantom patients, or collusion between providers and insured persons. Life insurance may involve false identity, non-disclosure of medical history, suspicious beneficiary changes, or deliberate killing. Trade credit, marine, cyber, and agricultural insurance each generate distinctive fraud patterns.

Fraud Across the Insurance Value Chain

Underwriting fraud and application misrepresentation

Underwriting fraud seeks to obtain coverage, a lower premium, broader terms, or a higher limit by presenting a distorted risk. Examples include concealing previous claims, misstating the use of a vehicle, declaring a property vacant when it is occupied, understating turnover, misrepresenting construction materials, or failing to disclose hazardous activities. In life and health insurance, relevant omissions may include medical history, smoking, occupation, hazardous pursuits, or prior diagnoses.

A misrepresentation is material when accurate information could reasonably influence the insurer’s decision to accept the risk, set the premium, impose conditions, or determine the sum insured. Materiality should not be judged only with hindsight. The insurer should examine underwriting rules, proposal questions, available evidence, and the actual significance of the omitted information.

Common underwriting red flags include unusually urgent requests for high limits, inconsistent answers across proposal forms and supporting documents, rapid changes in insured interest, reluctance to permit verification, unexplained cash payments, repeated policy cancellations and renewals, and coverage sought immediately before a known or suspected loss. A red flag is not proof of fraud; it signals the need for proportionate verification.

Staged and deliberately caused losses

A staged loss is deliberately arranged to appear accidental or fortuitous. A motor accident may involve an arranged collision, a pre-selected vehicle, coached passengers, and a repairer who coordinates the claim. A property loss may involve deliberate removal of valuable goods followed by a false burglary report. A fire may be set to destroy a financially distressed business. Deliberately causing a loss is especially serious because it transforms the insured event from an uncertain occurrence into a planned act.

Investigators look for inconsistencies in timing, physical damage, witness accounts, vehicle movement, financial circumstances, and prior communications. For example, damage inconsistent with the reported direction of impact may justify further examination, but it does not by itself establish staging. Technical findings must be combined with documentary and testimonial evidence.

Inflated and fabricated claims

An inflated claim contains a genuine loss but seeks payment beyond the actual amount. Typical methods include adding undamaged property, using replacement prices rather than policy-defined valuation, submitting duplicate invoices, overstating business interruption, or claiming items that were borrowed or already sold. A fabricated claim involves a loss that did not occur, property that was never owned, or damage that existed before the policy period.

The investigation should separate the undisputed genuine component from the disputed component. Rejecting an entire claim without examining this distinction may be unfair and commercially damaging. Equally, paying the undisputed portion without preserving evidence may weaken the insurer’s ability to challenge fraudulent elements.

Organized and networked fraud

Organized fraud is characterized by repeated methods, shared participants, common service providers, coordinated documentation, or claims distributed across insurers. Networks may include claimants, intermediaries, medical providers, garages, lawyers, valuers, contractors, or corrupt employees. Network analysis is particularly valuable because a single claim may appear ordinary when reviewed in isolation.

Indicators include identical wording in unrelated statements, common telephone numbers or bank accounts, repeated witnesses, unusual concentration of claims around a provider, multiple policies purchased shortly before losses, sequential claim dates, and links between apparently unrelated parties. Such evidence should be handled carefully because legitimate communities, families, employers, and service providers may naturally share contact details.

A Risk-Based Red-Flag Model

An effective fraud programme does not treat every irregularity as misconduct. It uses a structured model to prioritize cases. A practical model considers five dimensions:

1. Opportunity: Could the person or network realistically create, conceal, or profit from the alleged loss?

2. Incentive: Is there financial pressure, a large potential benefit, debt, business failure, or other motive?

3. Inconsistency: Do statements, documents, physical evidence, system records, or timelines conflict?

4. Pattern: Does the case resemble previous claims, known methods, or network activity?

5. Control weakness: Was there a gap in underwriting, verification, access control, segregation of duties, or payment review?

These dimensions can support a risk score, but the score should guide investigation rather than replace judgment. A simple weighted score may assign greater importance to objective inconsistencies and network connections than to subjective impressions. The organization should record the reason for each indicator, the information source, its reliability, and the proposed next action.

The most useful workflow is: detect, triage, preserve, investigate, decide, and learn. Detection may arise from a rules engine, employee referral, customer complaint, whistle-blower, underwriter, or external authority. Triage determines urgency, potential value, vulnerability of evidence, and whether immediate payment controls are required. Preservation prevents loss or alteration of records. Investigation tests hypotheses. Decision-making applies policy terms, evidence standards, law, and fairness. Learning feeds confirmed patterns back into underwriting and claims controls.

Conducting a Claims Investigation

Planning and scoping

Before contacting the claimant, the investigator should define the allegation, identify the policy and relevant coverage terms, establish the chronology, list the issues to be proved or disproved, and identify available evidence. The investigator should distinguish facts from assumptions and develop alternative hypotheses. For example, the competing explanations for a fire may include accidental electrical failure, arson by an outsider, deliberate conduct by the insured, or a pre-existing defect.

The scope must be proportionate. A low-value claim with a minor documentation error does not justify intrusive surveillance or an extensive forensic examination. A high-value claim involving possible organized fraud may justify specialist investigators, technical experts, enhanced document review, and network analysis, subject to law and internal authorization.

Evidence management

Evidence may be documentary, physical, digital, financial, photographic, or testimonial. Important materials may include the proposal form, policy schedule, endorsements, claim form, invoices, bank records, call recordings, emails, metadata, access logs, repair estimates, police reports, medical records, weather data, geolocation information, and expert reports.

Evidence management requires authenticity, integrity, relevance, reliability, and traceability. Investigators should preserve original files where possible, make forensic copies of digital material, record who collected each item and when, use secure storage, restrict access, and maintain a chain-of-custody record. A photograph should be linked to its source, date, location, and method of capture. Digital evidence should not be casually edited or circulated through uncontrolled messaging applications.

The investigator should also preserve exculpatory material. Evidence that supports the claimant’s account is as important as evidence that challenges it. Selective preservation can produce an unfair outcome and undermine the insurer’s position in litigation or regulatory review.

Investigative interviewing

Interviewing is a method of obtaining reliable information, not an exercise in intimidation. Preparation should include a review of known facts, identification of gaps, and sequencing of topics. Open questions should normally come first: “Please describe what happened from the beginning.” More focused questions can then clarify dates, locations, participants, ownership, financial value, and actions taken after the event.

A professional interview involves active listening, neutral language, appropriate pauses, and accurate recording. The interviewer should avoid disclosing all evidence at the outset, because premature disclosure can allow a dishonest account to be adjusted. However, the interviewer must not mislead unlawfully, threaten the interviewee, promise an outcome that cannot be delivered, or treat nervousness as proof of deception.

The interviewer should test inconsistencies by asking the person to explain them rather than asserting that the person is lying. A signed statement or recorded interview should accurately reflect what was said, including corrections and qualifications. Where language, disability, age, or cultural factors affect communication, appropriate support should be provided.

Forensic techniques

Forensic accounting can compare claimed income with bank statements, tax records, sales ledgers, inventory movements, payroll records, and industry indicators. In business interruption claims, the investigator may examine the pre-loss trend, seasonal variation, saved expenses, production capacity, and post-loss recovery. The objective is not simply to find a lower number but to reconstruct the financial position that would probably have existed without the insured event.

Physical and engineering examination may assess burn patterns, point of origin, tool marks, collision dynamics, damage compatibility, or signs of forced entry. Document examination may reveal altered dates, inconsistent fonts, copied signatures, unusual metadata, or invoices from non-existent suppliers. These techniques should be performed or supervised by appropriately qualified specialists. An investigator should never present a technical conclusion beyond the limits of the available expertise.

Financial Crime and Insurance Controls

Insurance products can be misused to move, disguise, or obtain funds. Life policies with investment features, annuities, premium refunds, claims payments, and intermediary accounts may create money-laundering risks. A criminal may use illicit funds to pay premiums, surrender a policy, receive a refund, or direct a claim payment to an unrelated account. Insurance may also be used to conceal beneficial ownership or transfer value through false claims.

An anti-money-laundering programme generally includes customer identification and verification, beneficial-owner identification, risk classification, ongoing monitoring, record keeping, staff training, suspicious transaction reporting, and escalation to a designated compliance function. Enhanced due diligence may be appropriate for high-risk customers, complex ownership structures, politically exposed persons, high-risk jurisdictions, unusual payment routes, or transactions lacking an apparent economic purpose.

The principle of know your customer does not mean that every customer is treated as a criminal. It means that the insurer obtains sufficient information to understand who the customer is, what the relationship is for, who controls it, and whether activity is consistent with the stated purpose. Controls should be risk-based and proportionate.

A claims investigator who observes suspicious financial activity should not conduct an informal parallel criminal investigation or alert the customer in a way that could constitute unlawful tipping-off. The matter should be referred through the organization’s AML and suspicious-activity procedures. Legal obligations differ across jurisdictions; therefore, the insurer must follow applicable legislation, regulatory instructions, reporting deadlines, confidentiality rules, and sanctions requirements.

Technology, Analytics, and AI

Fraud analytics may use rules, anomaly detection, predictive models, link analysis, natural-language processing, image analysis, and graph databases. Rules can identify a claim submitted soon after policy inception, repeated bank accounts, duplicate invoices, or an unusual claim frequency. Machine-learning models can detect combinations of variables that are difficult to identify manually. Graph analysis can reveal relationships among claimants, vehicles, providers, addresses, telephone numbers, and payment accounts.

Technology improves triage but does not eliminate the need for investigation. Models may reproduce historical bias, generate false positives, or penalize customers whose circumstances resemble previously fraudulent cases. Data quality, explainability, model validation, cybersecurity, access control, and human oversight are essential. Decisions involving claim denial, policy cancellation, or referral should not be based solely on an opaque score where law or fairness principles require meaningful review.

Digital claims processes also create new risks: synthetic identities, deepfake documents, manipulated photographs, account takeover, phishing, ransomware, and automated submission of fabricated claims. Insurers should combine fraud controls with privacy protection, secure authentication, staff awareness, vendor governance, and incident-response procedures.

Decision-Making, Referral, and Governance

A fraud decision should be based on the applicable policy wording, relevant law, credible evidence, and a documented reasoning process. The investigator should state what is established, what remains uncertain, how evidence was assessed, and why the recommended action is proportionate. Possible outcomes include payment, partial payment, request for further information, adjustment of the loss, reservation of rights, denial, policy action, recovery proceedings, internal disciplinary action, or referral to an external authority.

Referral should follow a lawful and controlled pathway. Serious suspected fraud may be referred to a specialized internal fraud unit, compliance officer, regulator, police, prosecutor, or industry fraud bureau, depending on jurisdiction and organizational policy. The referral should contain factual information, supporting evidence, chronology, and contact details, while avoiding unsupported accusations. Confidentiality should be maintained and information disclosed only to persons with a legitimate purpose.

Governance is strengthened by segregation of duties, approval thresholds, independent review of high-risk denials, whistle-blowing channels, conflict-of-interest declarations, quality assurance, audit trails, and periodic review of fraud outcomes. Performance measures should not reward investigators simply for denying claims. Useful measures include prevented loss, confirmed fraud rate, false-positive rate, recovery value, investigation cycle time, customer complaints, appeal outcomes, and the quality of referrals.

Illustrative Case Study: The Riverside Commercial Fire

Background

Riverside Furnishings, a medium-sized retailer, held property and business-interruption insurance. Three months before renewal, the company increased its contents limit and reported a higher projected turnover. Six weeks later, a fire destroyed much of the warehouse. The company claimed for stock, fixtures, lost profits, and emergency expenses.

Situation and initial indicators

The claim was potentially valid, but several indicators required examination. The policy increase occurred shortly before the fire. The inventory list contained unusually high quantities of discontinued goods. Several invoices came from suppliers sharing a common bank account. The fire brigade’s preliminary report noted that the fire development was inconsistent with a single accidental ignition point. A director had recently requested that claim payments be made to an account associated with another company.

These indicators did not establish fraud. They created competing hypotheses: an accidental fire combined with poor records, deliberate destruction by an outsider, deliberate conduct by a company insider, or an inflated claim following a genuine fire.

Analysis and investigation

The insurer appointed a claims investigator, a fire engineer, and a forensic accountant. The team preserved photographs, CCTV, alarm records, electronic inventory files, policy communications, and financial records. It obtained independent supplier confirmations and compared purchase invoices with stock movements.

Interviews were conducted separately with directors, warehouse staff, the security contractor, and key suppliers. The investigator used open questions first and then addressed inconsistencies in inventory quantities and the payment-account request. The forensic accountant found that the claimed stock exceeded both historical purchases and warehouse capacity. Several invoices had been created after the alleged purchase dates, and the claimed turnover was inconsistent with bank deposits and sales-tax records.

The fire engineer concluded that some damage was consistent with the reported fire, but certain burn patterns and the absence of expected electrical damage required further explanation. CCTV showed that a director entered the warehouse shortly before the alarm, although this fact alone was not conclusive. Financial analysis established that the company was under severe liquidity pressure.

Approach and outcome

The insurer separated the claim into verified physical damage, unsupported inventory, disputed business interruption, and suspicious payment instructions. It paid the substantiated emergency expenses and preserved the remaining issues under the applicable policy and law. The matter was referred through the insurer’s legal and compliance process to the relevant authority, supported by the evidence chronology and expert reports. The insurer also conducted a review of why the increased limit had not triggered enhanced pre-loss verification.

Learning points

The case demonstrates the importance of combining underwriting and claims information. No single red flag proved fraud. The conclusion emerged from converging evidence: timing, capacity, accounting records, physical findings, communications, and financial pressure. It also illustrates why partial validation is preferable to an automatic all-or-nothing response, why evidence must be preserved early, and why suspicious payment instructions may raise both fraud and financial-crime concerns.

Challenges, Limitations, and Common Mistakes

Fraud investigation is affected by incomplete records, delayed notification, unreliable witnesses, changing digital data, cross-border transactions, legal restrictions, and the cost of expert analysis. Organized networks may adapt quickly when a control becomes known. Models trained on historical data may fail when fraud methods change.

Common mistakes include treating a red flag as proof, relying on stereotypes, conducting an unplanned interview, failing to document the chain of custody, ignoring evidence favourable to the claimant, confusing policy breach with intentional fraud, using unqualified experts, sharing confidential information widely, and making a denial before the investigation is complete. Another serious mistake is allowing commercial pressure to determine the conclusion. Speed matters, but procedural fairness and evidential quality protect both the insurer and the customer.

Ethical and Professional Considerations

Fraud control must be balanced with the duty to treat customers fairly. Investigators should respect privacy, collect only relevant information, observe data-protection rules, avoid discriminatory profiling, and use surveillance only where lawful, necessary, and proportionate. Conflicts of interest must be disclosed. An investigator should withdraw or obtain independent supervision when personal, financial, or professional relationships could affect judgment.

The presumption that a claim is unproven is not the same as a presumption that the claimant is dishonest. Communications should therefore distinguish “insufficient evidence,” “policy exclusion,” “material misrepresentation,” and “suspected fraud.” Accurate language reduces reputational harm and supports defensible decisions. Professional competence also requires continuing education in policy interpretation, interviewing, digital evidence, financial crime, and relevant legal requirements.

Best-Practice Framework

An effective insurer should:

• integrate underwriting, claims, compliance, legal, data, and investigation functions;
• maintain product-specific fraud typologies and periodically update them;
• use red flags for triage rather than automatic rejection;
• preserve evidence promptly and maintain auditable case files;
• apply qualified specialists to technical questions;
• provide investigators with interview and privacy training;
• validate analytical models and monitor false positives;
• separate investigation, decision, payment, and review responsibilities;
• protect whistle-blowers and manage conflicts of interest;
• refer serious cases lawfully and avoid unsupported allegations; and
• use confirmed cases to improve controls, training, pricing, and customer communication.

Current Developments

Fraud management is moving toward integrated intelligence rather than isolated claims review. Insurers increasingly connect policy, claims, payment, provider, and external data, subject to lawful authority and privacy safeguards. Real-time identity verification, digital document authentication, geospatial analysis, and network analytics are becoming more important. At the same time, generative artificial intelligence can produce convincing documents, images, voices, and narratives, increasing the need for source verification and human judgment.

Regulators and customers are also demanding explainable automated decisions, stronger protection of personal data, and evidence that anti-fraud practices do not unfairly exclude vulnerable groups. The future claims professional will therefore need both investigative judgment and technological literacy.

Chapter Summary

Insurance fraud is deliberate deception or concealment intended to obtain an improper benefit or create an improper loss. It can arise during application, underwriting, policy servicing, claims settlement, payment, or internal operations. Important forms include material misrepresentation, staged losses, inflated and fabricated claims, internal fraud, and organized networks.

A risk-based investigation uses red flags relating to opportunity, incentive, inconsistency, pattern, and control weakness. Red flags guide triage; they do not prove dishonesty. Effective investigation requires planning, alternative hypotheses, proportionate inquiry, sound interviewing, expert analysis, and rigorous evidence management. Digital analytics and AI can improve detection, but they require validation, explainability, privacy protection, and human oversight.

Financial-crime controls connect claims investigation with customer identification, beneficial-ownership checks, transaction monitoring, suspicious-activity reporting, and lawful referral. Ethical practice requires fairness, confidentiality, non-discrimination, accurate communication, and preservation of exculpatory evidence. Strong governance converts individual investigations into organizational learning by improving underwriting, claims controls, payment security, and professional capability.

Key Terms

Fraud: Deliberate deception or concealment intended to obtain an improper insurance benefit or cause an improper loss.

Application fraud: False information or material omission made during proposal or underwriting.

Material misrepresentation: An inaccurate statement or omission capable of influencing underwriting or claim decisions.

Hard fraud: Deliberate invention or causing of an insured loss.

Soft fraud: Exaggeration or inflation of an otherwise genuine loss.

Staged loss: A deliberately arranged event presented as accidental or fortuitous.

Organized fraud: Coordinated fraudulent activity involving multiple people, claims, or service providers.

Red flag: An indicator that justifies additional review but does not itself prove fraud.

Triage: The prioritization of cases according to risk, urgency, value, and investigative need.

Chain of custody: A documented history of the collection, handling, storage, and transfer of evidence.

Forensic accounting: Examination of financial records to reconstruct transactions, losses, or economic conditions.

Investigative interview: A structured, fair process for obtaining and testing information from relevant persons.

Know your customer: Procedures for identifying and understanding a customer and the purpose of the relationship.

Beneficial owner: The individual who ultimately owns or controls a customer or transaction.

Money laundering: Processes used to conceal the unlawful origin of funds or assets.

Suspicious transaction report: A regulated report concerning activity suspected to involve financial crime.

False positive: A case or transaction incorrectly identified as suspicious or fraudulent.

Network analysis: Examination of relationships among people, accounts, providers, locations, and events.

Evidence integrity: The reliability and protection of evidence against alteration, contamination, or loss.

Whistle-blower: A person who reports suspected wrongdoing through an authorized channel.

Review Questions

Short Answer Questions

1. What distinguishes insurance fraud from an ordinary claims error?

2. Explain the difference between hard fraud and soft fraud.

3. Why is a red flag not proof of fraud?

4. What is the purpose of maintaining a chain of custody?

5. State two ways in which insurance products may be misused for money laundering.

Long Answer Questions

1. Explain the main forms of fraud across the insurance value chain, using suitable examples.

2. Describe a risk-based claims investigation from detection through final decision.

3. Discuss the role of investigative interviewing and forensic techniques in establishing reliable facts.

4. Explain how AML and customer-due-diligence controls support insurance fraud management.

5. Evaluate the benefits and limitations of analytics and AI in fraud detection.

Analytical / Application Questions

1. A claimant reports a burglary two days after purchasing a high-limit policy. The inventory list contains identical wording to another recent claim, and the requested payment account belongs to a third party. Design a proportionate investigation plan and identify the evidence you would preserve.

2. An insurer’s predictive model flags claims from a particular geographic area at a high rate, but subsequent reviews show many genuine claims. Analyze possible causes and recommend governance and model-validation actions.

3. A business submits a valid fire claim but includes stock that cannot be reconciled with purchase records. Explain how the insurer should separate genuine loss from suspected inflation while preserving fairness, legal compliance, and referral options.

22. Multiple Choice Questions with Answers

Question 1

Which fact most directly supports a finding of material misrepresentation during underwriting?

A. The applicant made a spelling error in an address

B. The applicant omitted a previous claim that would have changed the premium decision

C. The applicant requested electronic policy documents

D. The applicant reported a loss several years after policy inception

Correct Answer: B. The applicant omitted a previous claim that would have changed the premium decision

Question 2

What is the proper role of a fraud red flag?

A. It automatically justifies claim denial

B. It proves criminal intent when combined with a large claim

C. It indicates that proportionate additional review may be necessary

D. It permits disclosure of customer information to the public

Correct Answer: C. It indicates that proportionate additional review may be necessary

Question 3

Which example best illustrates soft fraud?

A. Arranging a collision between two cooperating vehicles

B. Claiming expensive items that were never owned

C. Adding unrelated damaged items to a genuine burglary claim

D. Creating a policy identity for a non-existent customer

Correct Answer: C. Adding unrelated damaged items to a genuine burglary claim

Question 4

What is the primary purpose of a chain-of-custody record?

A. To calculate the policy premium

B. To demonstrate how evidence was collected, handled, and preserved

C. To replace the need for expert analysis

D. To identify the claimant’s financial motive

Correct Answer: B. To demonstrate how evidence was collected, handled, and preserved

Question 5

During an investigative interview, which approach is generally most appropriate at the beginning?

A. Accuse the interviewee of lying and demand an admission

B. Reveal all evidence and ask the interviewee to agree with it

C. Begin with an open request for the interviewee’s account of events

D. Promise that the claim will be paid if the interview ends quickly

Correct Answer: C. Begin with an open request for the interviewee’s account of events

Question 6

Which feature most strongly suggests organized fraud rather than an isolated inflated claim?

A. A claimant is dissatisfied with the settlement estimate

B. Several unrelated claims share providers, witnesses, and payment accounts

C. A claim is notified outside normal business hours

D. A customer asks for an explanation of a policy exclusion

Correct Answer: B. Several unrelated claims share providers, witnesses, and payment accounts

Question 7

Which action is most consistent with a risk-based AML programme?

A. Treat every customer as equally high risk

B. Ignore beneficial ownership for low-premium policies in all circumstances

C. Apply enhanced due diligence where ownership, jurisdiction, or transaction patterns create higher risk

D. Allow investigators to warn customers whenever a suspicious report is filed

Correct Answer: C. Apply enhanced due diligence where ownership, jurisdiction, or transaction patterns create higher risk

Question 8

Why should an insurer avoid relying solely on an opaque fraud score to deny a claim?

A. Scores cannot be calculated for digital claims

B. The score may contain bias, false positives, or errors that require human review

C. Analytics are relevant only to underwriting and never to claims

D. A score always produces a lower settlement amount

Correct Answer: B. The score may contain bias, false positives, or errors that require human review

Question 9

In a mixed claim containing verified damage and unsupported inventory, what is the most defensible initial approach?

A. Pay every item because part of the loss is genuine

B. Reject the entire claim without further analysis

C. Separate and assess the substantiated and disputed components using evidence and policy terms

D. Refer the claimant to law enforcement before preserving records

Correct Answer: C. Separate and assess the substantiated and disputed components using evidence and policy terms

Question 10

Which performance measure best helps identify whether a fraud programme is unfairly over-investigating genuine customers?

A. Number of investigations opened alone

B. Total number of claim denials alone

C. False-positive rate together with appeal and complaint outcomes

D. Average premium collected per policy

Correct Answer: C. False-positive rate together with appeal and complaint outcomes