III IC-39 key terms

The coordinated set of policies, controls, and monitoring activities an insurer uses to prevent, detect, investigate, and respond to dishonest claims, applications, or internal conduct. It covers governance, data, training, and reporting.

The amount and type of fraud exposure an insurer is willing to tolerate while pursuing its business goals, expressed through limits, tolerances, and escalation thresholds approved by senior management and the board.

A structured process that identifies fraud schemes relevant to each business line, estimates their likelihood and impact, and ranks them so controls and resources can be prioritized. It is refreshed as products, channels, and threats change.

The level of fraud exposure that exists before any controls are applied. It reflects the nature of the product, distribution channel, and customer base, and provides the baseline against which control effectiveness is measured.

The fraud exposure that remains after existing controls operate. Comparing residual risk with the approved appetite shows where additional controls, monitoring, or escalation are needed.

An assessment of whether a fraud control is designed appropriately and actually operates as intended. Weak design or inconsistent operation raises residual risk even when the control appears present on paper.

A governance structure in which operational management owns fraud risk, a risk and compliance function provides oversight and challenge, and internal audit gives independent assurance to the board. It clarifies accountability across the insurer.

Dividing tasks so that no single person can initiate, approve, record, and reconcile a transaction. In insurance this limits opportunities for staff to create or pay fraudulent claims without detection.

The shared attitudes, incentives, and behaviors that determine how seriously an insurer's people treat fraud. Tone from senior leaders, whistleblowing channels, and consequences for misconduct shape it.

A protected process through which employees, agents, or others can report suspected fraud or misconduct confidentially and without retaliation. Effective programs encourage early detection and support investigations.

A maintained record of identified fraud scenarios, their assessed likelihood and impact, the controls in place, and the owners responsible. It supports reporting, prioritization, and tracking of remediation actions.

A metric that signals rising or falling fraud exposure, such as claim denial rates, policy lapse patterns, or referral volumes. Thresholds trigger review or escalation before losses become material.

The use of data mining, rules, and statistical or machine learning models to identify suspicious patterns in applications, claims, and provider behavior. Outputs prioritize cases for human review.

A statistical technique that scores applications or claims by their likelihood of fraud based on historical patterns and attributes. Scores help route cases to investigation or straight-through processing.

A warning sign or anomaly that suggests possible fraud, such as a claim filed soon after policy inception or details inconsistent with prior records. Red flags prompt verification rather than proof of fraud.

Dishonest or exaggerated claims submitted to obtain payment not due under the policy, including staged incidents, inflated damages, and fabricated injuries. It is the largest fraud category for most insurers.

Misrepresentation or concealment during underwriting, such as false answers about health, occupation, or prior losses. It distorts risk selection and can be grounds for voiding coverage or rescission.

Fraud committed by employees, agents, or others with inside access, such as falsifying claims, colluding with providers, or diverting premium. Controls include segregation of duties and monitoring of unusual activity.

Secret cooperation between two or more parties to obtain an improper benefit, such as a claimant, a broker, and a repairer working together. Collusion is harder to detect because it can mimic legitimate transactions.

Planned fraud carried out by groups that create multiple identities, policies, or claims to extract repeated payouts. It often spans insurers and requires data sharing and coordinated investigation.

A network of individuals or businesses that coordinate to defraud insurers, for example through staged accidents, fake medical clinics, or recruited claimants. Detection relies on link analysis across cases.

A deliberately arranged incident, such as a collision or slip and fall, created to support fraudulent injury or damage claims. Patterns may include repeat participants, providers, and locations.

A false or misleading statement on an application or claim that could influence the insurer's decision to accept risk, set terms, or pay. It may allow the insurer to rescind or deny coverage.

Failure to reveal information the insurer asked for or that a reasonable applicant would know is relevant, such as a medical condition or prior claim. Consequences depend on materiality and intent.

A dedicated team that investigates suspected fraudulent claims and applications, gathers evidence, conducts interviews, and coordinates with law enforcement. It works alongside claims and underwriting staff.

The formal process by which claims handlers, underwriters, agents, or analytics systems pass a suspicious matter to investigators. Clear criteria and tracking ensure consistent handling and timely action.

A case flagged as suspicious by a rule or model that investigation shows to be legitimate. High false positive rates waste resources and can harm customer experience, so thresholds are tuned carefully.

The controls and techniques used to identify suspected fraud after a transaction or claim enters the process. Methods include rules, analytics, verification, and staff awareness of warning signs.

Measures taken before a transaction to stop fraud from occurring, such as identity verification, eligibility checks, and clear policy terms. Prevention reduces the volume of cases requiring investigation.

Checks applied when accepting risk, including verification of identity, disclosed history, and eligibility. They reduce application fraud and produce data used later to test claims for consistency.

Confirming that a person or entity is who they claim to be using documents, databases, or biometric checks. It underpins both onboarding and claims controls and limits impersonation and synthetic identities.

A fabricated identity built from a mix of real and false information, such as a real national ID number paired with a fictitious name. It can mature over time before being used for large claims.

Arrangements that shift some fraud-related exposure to another party, such as reinsurance terms, warranties, or contractual indemnities. The insurer still retains reputational and operational responsibility.

A collaborative body, often industry-funded, that collects and matches fraud data across insurers and shares intelligence with members and authorities. It supports detection of cross-company fraud rings.

Exchanging fraud-related information with other insurers, bureaus, or authorities under lawful agreements and privacy safeguards. It improves detection of repeat offenders and organized activity.

Legal and ethical requirements governing how personal data is collected, used, stored, and shared during fraud work. Investigators must balance detection needs against privacy rights and fair processing.

Regular education for staff, agents, and distributors on fraud indicators, reporting routes, and their responsibilities. It strengthens the first line of defense and supports a consistent risk culture.

An independent function that reviews whether fraud controls are designed and operating effectively and reports findings to the board or audit committee. It provides assurance over the broader risk framework.

The recording and reporting of detected fraud, including amounts, case types, recoveries, and trends, to management, regulators, and industry bodies. Reliable data supports risk assessment and control improvement.