6. Fraud Analytics Lifecycle and Analytical Investigation

Introduction and Context

Insurance fraud analytics is not merely the search for unusual claims. It is a disciplined process through which an insurer converts business concerns into analytical questions, assembles reliable evidence, identifies suspicious patterns, evaluates competing explanations, and translates findings into proportionate action. The central challenge is that fraudulent behaviour is usually concealed within legitimate activity. A claim may be perfectly genuine even when it resembles a known fraud pattern, while a carefully organised fraud may appear ordinary when examined through a single variable.

The fraud analytics lifecycle therefore combines business understanding, data management, statistical analysis, investigative reasoning, operational decision-making, and governance. It connects the work of claims professionals, investigators, actuaries, underwriters, data scientists, legal advisers, compliance officers, and senior managers. Analytics may identify a claim for review, but it does not by itself establish fraud. That distinction is fundamental to fair treatment, regulatory compliance, and sound loss-control practice.

In earlier discussions of insurance fraud, attention is often given to fraud typologies, sources of data, and analytical technologies. This chapter focuses on what happens when an insurer must investigate a problem analytically. It explains how to frame the problem, define labels and baselines, explore and diagnose data, engineer temporal and behavioural features, test hypotheses, and convert evidence into detection and prevention interventions.

Learning Objectives

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

1. Explain the stages of a fraud analytics lifecycle.

2. Frame a fraud problem in business, analytical, and operational terms.

3. Distinguish fraud labels, investigation outcomes, and analytical baselines.

4. Conduct exploratory and diagnostic analysis of insurance data.

5. Engineer temporal, relational, transactional, and behavioural features.

6. Formulate and test hypotheses without confusing association with proof.

7. Evaluate analytical results using appropriate performance and business measures.

8. Translate findings into investigation, prevention, and governance interventions.

9. Recognise ethical, legal, data-quality, and model-risk concerns.

1. Framing the Fraud Problem

A poorly framed problem produces an impressive model that answers the wrong question. “Find fraudulent claims” is too broad for effective analysis. A useful problem statement specifies the insurance line, business process, population, time period, suspected behaviour, decision point, and intended action.

For example, a motor insurer might define the problem as: “Among newly reported private motor claims, identify claims requiring enhanced review before settlement, while controlling unnecessary referrals and preserving legitimate customer service.” This statement is superior to “detect motor fraud” because it identifies the unit of analysis, the stage of intervention, and the operational objective.

Problem framing should address five questions:

• What loss or risk is being investigated?
• Whose decision will the analysis support?
• At what point in the claim or policy lifecycle must the decision occur?
• What evidence is available at that point?
• What action will follow an analytical signal?

Fraud analytics usually pursues several related objectives. Detection seeks to identify suspicious activity. Prevention seeks to interrupt or discourage fraud before payment. Recovery seeks to support action after an improper payment. Prioritisation helps investigators allocate limited capacity. Measurement assesses whether an intervention actually reduces loss without creating unacceptable customer or regulatory harm.

A further distinction is required between the analytical population and the operational population. The analytical population may include all claims reported during a year, whereas the operational population may be only open claims eligible for referral. If these populations are confused, performance estimates will not match real-world use.

2. The Fraud Analytics Lifecycle

The lifecycle is iterative rather than strictly linear. Findings from investigation may change the definition of fraud; data-quality discoveries may require a revised baseline; and operational outcomes may reveal that a technically accurate model is impractical. A practical lifecycle contains the following connected stages.

Business understanding and scoping

The team defines the loss mechanism, decision owner, intervention point, constraints, and success measures. It also identifies relevant stakeholders and establishes what constitutes a useful outcome. A claims manager may value prevented leakage, whereas an investigator may value high-quality referrals and sufficient evidence.

Data discovery and preparation

Relevant data may include policy records, claim transactions, payment histories, repairer information, call-centre contacts, documents, geospatial information, device identifiers, and prior investigation outcomes. Data preparation includes linkage, standardisation, missing-value assessment, duplicate detection, chronology checks, and documentation of data lineage.

Label and baseline design

The analyst determines what “fraud” means in the dataset and how normal behaviour will be represented. This stage is often more consequential than model selection because labels may be incomplete, delayed, inconsistent, or affected by investigation practices.

Exploration and diagnosis

Descriptive analysis identifies distributions, concentrations, trends, relationships, anomalies, and data defects. Diagnostic analysis then investigates why observed differences occur. Exploration asks what is happening; diagnosis asks what may explain it.

Feature engineering and hypothesis testing

Raw records are transformed into indicators of timing, frequency, sequence, relationships, behaviour, and exposure. Hypotheses are tested using appropriate comparisons, controls, and statistical reasoning.

Analytical development and validation

Rules, scores, statistical models, network methods, or hybrid approaches are assessed. Validation should reflect the intended decision point and should avoid using information that would not have been available at that time.

Operational deployment

The output is integrated into claims or policy workflows. Referral thresholds, investigator queues, explanations, escalation procedures, and override mechanisms must be specified. A score without an operational response is not a fraud-control system.

Monitoring, learning, and review

The organisation monitors referral rates, investigation outcomes, prevented or recovered amounts, customer impacts, drift, data changes, and emerging fraud tactics. Feedback from investigators should improve the analytical process, but feedback must be governed so that subjective or inconsistent decisions do not silently distort labels.

3. Labels, Outcomes, and Baselines

The problem of the fraud label

A label is the outcome assigned to an observation for analytical purposes. In insurance, labels may include “confirmed fraud,” “suspected fraud,” “referred,” “declined,” “withdrawn,” “settled,” or “no issue found.” These are not interchangeable. A claim referred for investigation is not necessarily fraudulent; a claim with no recorded investigation outcome is not necessarily legitimate.

A confirmed-fraud label should ideally be based on a documented organisational or legal outcome. Yet confirmed labels are often scarce and selective. Investigators may focus on high-value claims, complex cases, or claims already identified by rules. Consequently, labelled data may reflect past selection practices rather than the full incidence of fraud.

The analyst should maintain separate fields for allegation, referral, investigation decision, claim decision, recovery, and legal outcome. A multi-stage outcome structure is often more informative than a single binary label. It also makes clear where uncertainty remains.

Positive, negative, and uncertain cases

A positive case is one classified as fraudulent under a defined standard. A negative case is a case treated as non-fraudulent, but this does not always mean innocence was proved. An uncertain or unresolved category is valuable because forcing ambiguous observations into positive or negative classes can produce misleading models.

Baselines

A baseline is the reference against which unusualness or improvement is judged. Possible baselines include:

• the overall claim population;
• claims within the same product, region, channel, or policy tenure;
• historical performance before an intervention;
• a matched comparison group;
• the investigator queue under the existing process.

Baseline selection must reflect exposure and opportunity. Comparing a specialist commercial portfolio with a mass-market portfolio may produce misleading differences because claim frequency, claim size, documentation, and reporting processes differ. Rates should generally be calculated using an appropriate denominator, such as claims per thousand policies or investigated cases per thousand reported claims.

Leakage and temporal validity

Target leakage occurs when a model uses information created after the decision it is supposed to support. Examples include a final investigation outcome, a later payment reversal, or a post-settlement legal note. Leakage produces inflated historical performance and disappointing deployment results. Every feature should therefore have an “as-of” time: the latest point at which it could legitimately have been known.

4. Exploratory and Diagnostic Analysis

Exploratory analysis begins with the quality and shape of the data, not with sophisticated algorithms. The analyst should understand claim volumes, missingness, duplicates, unusual values, category frequencies, monetary distributions, reporting delays, and changes over time. Insurance amounts are commonly skewed, so averages alone may be misleading; medians, percentiles, trimmed means, and segmented summaries are often necessary.

Useful exploratory questions include:

• Are claims concentrated among particular products, channels, repairers, brokers, or geographic areas?
• Do reporting delays differ by claim type or outcome?
• Are there sudden changes caused by system migration or coding changes?
• Are multiple claims linked to the same address, phone number, bank account, vehicle, provider, or device?
• Do suspicious outcomes vary with claim amount after controlling for product and exposure?

Diagnostic analysis goes beyond observing that a group has a higher fraud rate. It asks whether the difference may be explained by another factor. For example, claims submitted through a particular channel may show higher investigation rates because that channel handles more complex claims, not because the channel itself causes fraud. Stratification, matched comparisons, regression, and sensitivity analysis can help examine such explanations.

The analyst should distinguish three types of unusualness. Statistical unusualness means that an observation is rare in the data. Business unusualness means that it conflicts with process knowledge or policy terms. Investigative unusualness means that separate facts combine into a credible concern. None of these alone proves fraud.

Visualisation is valuable when it reveals patterns that summary tables conceal. Time-series plots may show bursts of activity; histograms expose skewness; cohort charts compare policy ages; and network diagrams reveal shared entities. Visual displays should include denominators, clear definitions, and appropriate scales so that rare but dramatic events are not exaggerated.

5. Temporal and Behavioural Feature Engineering

Feature engineering translates raw records into information that represents how fraud may occur. It requires domain knowledge and careful temporal logic.

Temporal features

Temporal features may include the number of days between policy inception and loss, time from loss to notification, time from notification to document submission, claim frequency within rolling windows, and changes in activity before and after a policy event. Rolling windows are often preferable to fixed calendar periods because they align with individual claim histories.

Sequence features can represent the order of events: policy purchase, coverage change, loss report, document upload, repair estimate, and payment. A suspicious pattern may involve a rapid sequence that is unusual for the product, but sequence interpretation must account for legitimate emergency or catastrophe scenarios.

Calendar effects also matter. Reporting near renewal, immediately after a coverage increase, or in a concentrated period may warrant examination, but such patterns can have legitimate operational explanations. Seasonal controls and product-specific baselines are therefore essential.

Behavioural features

Behavioural features describe patterns of interaction rather than isolated facts. Examples include repeated changes in contact details, frequent failed payment attempts, unusually rapid document submission, repeated use of identical narratives, high correction rates, and communication patterns associated with multiple claims.

Behavioural features should be designed cautiously. A claimant’s language proficiency, disability, access to technology, or use of a representative may affect communication patterns without indicating fraud. Behavioural proxies that encode protected or vulnerable characteristics create fairness risks.

Relational and network features

Fraud may be organised through relationships among claimants, policies, vehicles, addresses, repairers, medical providers, bank accounts, telephone numbers, or devices. A network representation treats these entities as nodes and their associations as edges. Features may include the number of linked claims, repeated counterparties, unusually dense clusters, or a provider’s concentration of interactions with previously suspicious entities.

Network evidence is a prioritisation aid, not automatic proof. Shared addresses can reflect family relationships; shared devices can reflect a household or public network; and common repairers may be assigned by the insurer. Relationship strength, timing, legitimate business explanations, and independent corroboration should be considered.

Monetary and exposure features

Claim amount, estimated loss, paid amount, policy limit utilisation, deductibles, and historical loss ratio may be informative. Monetary features should be normalised by product and exposure. A high-value claim is not inherently suspicious; risk may arise from a combination of value, timing, documentation, and relational evidence.

6. Hypothesis-Driven Analytical Investigation

Analytics becomes investigative when it moves from pattern recognition to reasoned examination. The analyst should formulate a hypothesis before selecting evidence. A hypothesis might be: “Claims reported within ten days of a policy limit increase have a higher rate of confirmed fraud than otherwise similar claims.” This can be evaluated with a defined population, comparison group, time window, outcome definition, and statistical method.

A good hypothesis is specific, testable, relevant to a decision, and open to disconfirmation. The analyst should identify possible alternative explanations. In the example above, the increase may reflect seasonal underwriting campaigns, a particular distribution channel, or changes in claim severity rather than fraudulent behaviour.

The general process is:

1. State the suspected mechanism.

2. Define the unit of analysis and observation date.

3. Specify the outcome and comparison group.

4. Identify confounding factors and exclusions.

5. Examine effect size, uncertainty, and practical significance.

6. Conduct sensitivity checks using alternative definitions.

7. Seek corroborating evidence from investigators and process owners.

8. Decide what intervention, if any, is justified.

Statistical significance should not be confused with operational importance. A very large dataset may make a trivial difference appear statistically significant. Conversely, a practically important pattern may be uncertain when confirmed-fraud cases are few. Analysts should report rates, absolute differences, confidence intervals where appropriate, and expected financial or operational impact.

Correlation does not establish causation. A feature may be associated with fraud because it is a proxy for another factor, because it results from earlier detection, or because the investigation process created the association. Causal claims require stronger designs, such as controlled interventions, natural experiments, or carefully justified quasi-experimental methods.

7. From Analytical Finding to Intervention

A finding becomes valuable when it changes a decision appropriately. Intervention design should match the strength of evidence and the cost of error.

A low-risk intervention may be an educational message, a request for clearer documentation, or a process reminder. A moderate intervention may be enhanced verification or a specialist review. A high-impact action, such as claim denial, policy cancellation, or external reporting, requires stronger evidence, procedural safeguards, and authorised human judgment.

A practical operating model separates three layers:

• Signal layer: rules, scores, anomalies, and network indicators generate alerts.
• Investigation layer: trained personnel review evidence, contact relevant parties, and document reasoning.
• Decision and learning layer: authorised decision-makers act, record outcomes, and feed validated information back into monitoring.

Thresholds should be selected using capacity, financial value, customer impact, and error costs. A model with high precision but very low coverage may miss substantial fraud. A model with high recall but excessive false positives may overwhelm investigators and damage trust. Queue prioritisation can combine estimated loss, confidence, urgency, and available capacity rather than relying on a single threshold.

Prevention may involve underwriting controls, identity verification, payment controls, supplier governance, policy wording, customer communication, or changes to claims workflow. Detection and prevention should be considered together. If a recurring pattern is visible before policy issuance or before payment, the organisation may prevent loss more efficiently than investigating it afterward.

8. Evaluation and Performance Management

Analytical performance should be assessed at several levels. Classification measures include precision, recall, specificity, and the area under a precision-recall curve. In fraud settings, precision-recall analysis is often more informative than accuracy because confirmed fraud is usually rare. A confusion matrix helps clarify the consequences of false positives and false negatives.

Operational measures include referral volume, investigator acceptance rate, investigation cycle time, proportion of referrals with actionable evidence, and workload per investigator. Financial measures may include prevented payment, recovered amount, avoided expense, and net benefit after investigation cost and customer remediation. Governance measures include complaint rates, appeal outcomes, disparate impact indicators, documentation quality, and override patterns.

Evaluation must use a realistic time-based validation design. Randomly mixing old and new claims can conceal performance deterioration caused by changing fraud tactics or policy rules. Results should be segmented by product, channel, geography, claim severity, and relevant customer groups to identify uneven performance.

9. Illustrative Case: The Rapid-Change Motor Claim Pattern

Background

A fictional insurer, Meridian Motor, operates a national private motor portfolio. Claims staff report an increase in suspicious theft claims, but confirmed-fraud labels are limited because investigations take several months. Management asks the analytics team to determine whether an early intervention is justified.

Situation and problem

The team initially observes that claims following recent changes to policy limits have a higher referral rate. However, referral is not treated as confirmation. The analytical question is whether claims reported shortly after a coverage change exhibit a higher confirmed-fraud rate after controlling for product, distribution channel, vehicle value, and policy tenure.

Analysis

The team constructs a claim-level dataset using only information available at first notification. It creates features for days from policy change to reported loss, number of prior claims in twelve months, reporting delay, vehicle and address links, document-upload timing, and claim amount relative to the policy limit. Claims with unresolved outcomes are retained as an uncertainty category rather than incorrectly labelled negative.

Exploratory analysis identifies a concentration of claims within a short period after certain limit increases. Diagnostic analysis shows that the pattern is strongest in one distribution channel and among policies with short tenure. A matched comparison of claims without recent limit changes reduces, but does not eliminate, the difference. Interviews with claims managers reveal that the channel had recently introduced a customer communication campaign encouraging policy reviews. This explains part of the increase and demonstrates why operational context matters.

A second analysis finds that a smaller subgroup combines recent limit changes, repeated linked addresses, and unusually short reporting delays. Investigation records show that this combination produces a materially higher yield than any single feature, although the sample is small and confidence intervals are wide.

Approach and solution

Meridian does not automatically deny claims. It implements a controlled pilot in which the combined signal triggers enhanced verification and prioritised specialist review. The workflow displays the contributing factors, prevents investigators from treating the score as proof, and requires a documented outcome. Claims with vulnerable customers or clear emergency explanations receive additional safeguards. The team monitors false-positive complaints, cycle time, payment delay, and confirmed outcomes.

Outcome

The pilot improves the quality of referrals while keeping the review population within investigator capacity. Some alerts are closed after legitimate explanations are documented. The insurer also changes its policy-change process by adding clearer verification for certain high-risk transactions and improves data capture for investigation outcomes.

Learning points

The case illustrates that a referral label is not a fraud label, recent events require temporal discipline, and patterns must be interpreted in operational context. It also demonstrates the value of combining weak signals cautiously, using human review for consequential decisions, and treating prevention as a complement to detection.

10. Challenges, Limitations, and Common Mistakes

Fraud analytics is vulnerable to several forms of error. Incomplete labels create hidden fraud and false negatives. Selective investigation creates sample bias. Data linkage errors may falsely connect unrelated people or entities. Changes in policy wording, claims systems, or investigator practice can create artificial trends. Rare-event modelling may produce unstable estimates, especially when segments are small.

Common mistakes include using post-outcome information, optimising for accuracy, treating every anomaly as fraud, ignoring exposure denominators, confusing referral with confirmation, and deploying a model without a defined workflow. Another error is allowing investigators to receive unexplained scores. Explanations do not need to reveal sensitive model details, but they should identify the principal evidence supporting review.

Over-alerting is a serious organisational risk. It consumes investigator capacity, delays legitimate claims, creates customer frustration, and may cause staff to disregard alerts. Under-alerting can permit losses and weaken confidence in controls. Thresholds and workflows should therefore be reviewed as business decisions, not left solely to technical teams.

11. Governance, Ethics, and Professional Responsibility

Fraud control must be effective and fair. Customers should not be treated as dishonest merely because they belong to a group associated with a historical pattern. Analysts should examine whether variables act as proxies for protected characteristics or socioeconomic disadvantage. Special care is required when using geolocation, communication behaviour, device data, health information, or external data sources.

Data collection and use should have a lawful and legitimate basis, follow purpose limitation and proportionality principles, and observe applicable privacy, insurance, consumer-protection, and records-management requirements. Access should be controlled, data lineage documented, and retention periods justified.

Human oversight is essential when analytical outputs may lead to adverse decisions. Reviewers should have authority to challenge a signal, record alternative explanations, and correct data errors. Governance should specify model ownership, validation, change control, performance monitoring, incident management, and audit trails. An ethical fraud programme protects both the insurer and legitimate policyholders by reducing losses without normalising suspicion or arbitrary treatment.

12. Technology and Emerging Practice

Modern fraud programmes increasingly combine rule engines, statistical models, machine learning, natural-language processing, graph analytics, workflow platforms, and investigator workbenches. Automation can extract information from documents, identify repeated narratives, link entities, and prioritise queues. Generative AI may assist with summarising case files, but its outputs require verification because hallucinated or incomplete summaries can distort investigations.

Real-time or near-real-time analytics is becoming more important in digital distribution and instant claims settlement. Such systems must be designed for latency, resilience, explainability, and appropriate human escalation. Privacy-enhancing techniques, federated analysis, and secure information sharing may support collaboration without unnecessary disclosure of personal data.

Emerging practice is also moving toward adaptive monitoring. Instead of treating a model as a finished product, insurers track population drift, changes in fraud behaviour, changes in data-generating processes, and shifts in investigator decisions. Continuous learning is useful only when labels are reliable and governance prevents uncontrolled model changes.

Best Practices for Analytical Investigation

A mature fraud analytics function should:

• begin with a precise business decision and intervention point;
• document labels, uncertainty, denominators, and data lineage;
• enforce time-of-decision controls to prevent leakage;
• combine domain expertise with statistical discipline;
• use matched or segmented baselines where appropriate;
• distinguish anomaly, suspicion, investigation, and proof;
• evaluate both analytical and operational performance;
• provide interpretable evidence to investigators;
• monitor fairness, customer impact, and model drift;
• use pilots and controlled implementation for consequential changes; and
• feed validated investigation outcomes back into prevention and learning.

Chapter Summary

Fraud analytics is an iterative lifecycle linking business framing, data preparation, labelling, baseline design, exploration, diagnosis, feature engineering, hypothesis testing, validation, operational deployment, and monitoring. Effective investigation depends on understanding what a label represents and what it does not represent. Temporal validity is essential because information available after a claim decision must not be used to evaluate an earlier decision.

Exploratory analysis describes distributions, trends, relationships, and anomalies, while diagnostic analysis examines alternative explanations and confounding factors. Temporal, behavioural, monetary, and network features can reveal meaningful patterns, but none proves fraud independently. Hypothesis-driven analysis, realistic baselines, and careful attention to uncertainty improve the quality of decisions.

Analytical outputs should be converted into proportionate interventions through a governed signal-investigation-decision process. Success is measured not only by precision or recall but also by financial benefit, workload, customer impact, fairness, and sustainability. Ethical governance, human oversight, data protection, and continuous monitoring are necessary for a trustworthy fraud-control capability.

Key Terms

Fraud analytics lifecycle: The iterative sequence from problem framing through analysis, intervention, monitoring, and learning.

Problem framing: Defining the business risk, population, decision point, evidence, and intended action.

Analytical population: The set of observations included in an analysis.

Fraud label: An outcome assigned to a record for analytical use, such as confirmed fraud or unresolved.

Baseline: A reference population or performance level used for comparison.

Target leakage: Use of information unavailable at the time the intended decision was made.

Exploratory analysis: Examination of data distributions, patterns, anomalies, and quality.

Diagnostic analysis: Investigation of possible explanations for observed patterns.

Feature engineering: Transformation of raw data into analytical variables.

Temporal feature: A variable representing timing, sequence, frequency, or change over time.

Behavioural feature: A variable describing patterns of interaction or conduct.

Network feature: A variable derived from relationships among connected entities.

Hypothesis: A specific, testable statement about a suspected relationship or mechanism.

Confounding factor: A variable that may explain or distort an observed association.

Precision: The proportion of flagged cases that are correctly identified positives.

Recall: The proportion of all actual positive cases that are identified.

Model drift: Change in data, relationships, or performance after deployment.

Human-in-the-loop: A design in which people review, interpret, or authorise consequential analytical decisions.

Intervention: An action taken in response to an analytical signal or finding.

Data lineage: Documentation of data origins, transformations, uses, and ownership.

Review Questions

Short Answer Questions

1. Why is “find fraudulent claims” an inadequate analytical problem statement?

2. Distinguish a referral label from a confirmed-fraud label.

3. What is target leakage? Give one insurance example.

4. How does diagnostic analysis differ from exploratory analysis?

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

Long Answer Questions

1. Explain the stages of the fraud analytics lifecycle and show how feedback makes the lifecycle iterative.

2. Discuss the importance of labels and baselines in insurance fraud analysis.

3. Explain how temporal, behavioural, monetary, and network features can support analytical investigation.

4. Describe a hypothesis-driven process for examining a suspected fraud pattern.

5. Discuss the governance and ethical requirements for deploying fraud analytics in claims operations.

Analytical / Application Questions

1. An insurer finds that claims reported soon after policy amendments have a high referral rate. Design an analysis that distinguishes fraud from legitimate explanations.

2. A model has high recall but overwhelms investigators with false positives. Recommend changes to thresholds, workflow, evaluation, and prevention strategy.

3. Design an intervention for a network signal linking several claims to the same repairer, while ensuring that legitimate shared business relationships are not automatically treated as fraud.

22. Multiple Choice Questions with Answers

Question 1

Which statement best distinguishes a fraud referral from a confirmed-fraud label?

A. A referral is always legally established fraud.

B. A referral identifies a case for review, whereas confirmation requires a defined evidential outcome.

C. A confirmed-fraud label is assigned before any investigation.

D. There is no analytical difference between the two.

Correct Answer: B. A referral identifies a case for review, whereas confirmation requires a defined evidential outcome.

Question 2

An analyst uses the final investigation outcome to score a claim at first notification. What problem is most likely present?

A. Underfitting

B. Sampling with replacement

C. Target leakage

D. Seasonality

Correct Answer: C. Target leakage

Question 3

Which is the strongest example of an appropriate baseline?

A. All claims from unrelated insurance products combined without adjustment

B. Only claims already selected by investigators

C. Comparable claims from the same product and decision context

D. The single largest claim in the portfolio

Correct Answer: C. Comparable claims from the same product and decision context

Question 4

A higher fraud rate is observed in one distribution channel. What should diagnostic analysis examine first?

A. Whether the channel should immediately be closed

B. Whether product mix, claim complexity, or customer composition explains the difference

C. Whether all claims in the channel are fraudulent

D. Whether accuracy can be maximised by removing the channel

Correct Answer: B. Whether product mix, claim complexity, or customer composition explains the difference

Question 5

Which feature is primarily temporal?

A. Number of claims linked to a repairer

B. Claim amount relative to policy limit

C. Days between a policy change and loss notification

D. Customer language preference

Correct Answer: C. Days between a policy change and loss notification

Question 6

Why should a shared address not automatically be treated as evidence of fraud?

A. Addresses cannot be stored in insurance systems

B. A shared address may have legitimate explanations such as a household relationship

C. Network features are never useful

D. Only claim amount can indicate fraud

Correct Answer: B. A shared address may have legitimate explanations such as a household relationship

Question 7

Which practice best supports a valid hypothesis test?

A. Define the comparison group after seeing the preferred result

B. Use a specific outcome, population, comparison group, and alternative explanations

C. Treat statistical significance as proof of causation

D. Exclude all unresolved cases as confirmed negatives

Correct Answer: B. Use a specific outcome, population, comparison group, and alternative explanations

Question 8

A model flags many legitimate claims and delays settlement. This is primarily a problem of:

A. Excessive false positives and operational burden

B. Perfect recall

C. Data lineage improvement

D. Under-reporting of exposure

Correct Answer: A. Excessive false positives and operational burden

Question 9

What is the most appropriate response to a high-impact analytical signal?

A. Automatic denial in every case

B. No action because analytics has no value

C. Proportionate human review supported by documented evidence and safeguards

D. Permanent exclusion of the customer from insurance

Correct Answer: C. Proportionate human review supported by documented evidence and safeguards

Question 10

Which measure is especially useful when confirmed fraud is rare and the organisation needs to know how many alerts are worthwhile?

A. Precision

B. Calendar year

C. Policy tenure alone

D. Raw database size

Correct Answer: A. Precision