Insurance Risk Analysis Training Course

Participants can assess insurance data fitness and create a documented analytical dataset.

• Data dictionaries define policy, claims, exposure, payment and cancellation fields
• Reconcile policy administration, claims and finance extracts before model development
• Missingness profiles distinguish operational gaps, not-recorded values and structural zeros
• Outlier treatment uses business rules, winsorisation and investigation logs
• Exposure measures align earned time, sums insured and risk units
• Create reproducible data lineage from source extract through analytical dataset

Participants can identify material portfolio patterns and prioritise analytical questions.

• Loss ratios decomposed by product, channel, territory, cohort and underwriting year
• Frequency, severity and pure premium compared across relevant risk segments
• Claims development triangles reveal emergence patterns and reporting delays
• Cohort analysis separates policy duration effects from calendar-year movements
• Concentration measures identify accumulation by geography, broker and insured group
• Visual diagnostics expose data anomalies before predictive modelling begins

Participants can select and interpret probability distributions for insurance loss variables.

• Probability distributions describe claim counts, claim costs and aggregate losses
• Poisson and negative binomial models handle claim frequency variation
• Gamma, lognormal and Pareto distributions model positive claim severities
• Percentiles, tail value at risk and expected shortfall quantify loss uncertainty
• Skewness and heavy tails tested before selecting severity assumptions
• Aggregate loss distributions combine frequency and severity model components

Participants can build claim frequency models using exposure-adjusted insurance data.

• Claim counts modelled against earned exposure using Poisson regression
• Exposure offsets ensure fair comparison between unequal policy durations
• Overdispersion tests determine whether negative binomial modelling is required
• Rating factors selected from product, driver, location and prior claims
• Interaction terms test combined effects between material underwriting characteristics
• Frequency predictions converted into expected claims per exposure unit

Participants can estimate claim severity and explain the drivers of claim cost variation.

• Paid and incurred claim amounts distinguished for severity model design
• Gamma regression estimates conditional claim costs using positive observations
• Large-loss thresholds separate ordinary claims from exceptional loss events
• Inflation adjustments place historic claim costs on comparable value bases
• Policy limits, deductibles and recoveries adjusted before severity estimation
• Severity outputs translated into expected cost and pricing implications

Participants can specify and interpret generalised linear models for insurance pricing.

• Generalised linear model components: distribution, link function and linear predictor
• Deviance compares alternative pricing model specifications and rating factors
• Relativities convert model coefficients into understandable pricing adjustments
• Reference categories establish interpretable baseline risks for each factor
• Multicollinearity checks prevent unstable coefficients and misleading factor effects
• Technical premium combines expected frequency, severity, expenses and profit margin

Participants can segment insurance portfolios using statistically and commercially meaningful criteria.

• Segmentation criteria balance predictive value, commercial relevance and fairness
• Decision trees identify non-linear splits within underwriting and claims data
• Random forest outputs compared with interpretable tree-based segmentation results
• Cluster analysis groups policies using similarity in risk characteristics
• Lift charts rank segments by observed loss or conversion outcomes
• Segment stability tested across underwriting periods and portfolio cohorts

Participants can use model evidence to support underwriting rules and referral decisions.

• Underwriting scorecards combine risk factors into consistent referral criteria
• Acceptance thresholds reflect expected loss, expenses, capacity and target return
• Rule overrides recorded with rationale, authority level and subsequent performance
• Risk-adjusted pricing compares technical premium with quoted premium outcomes
• Adverse selection indicators identify deteriorating mix within new business
• Underwriter performance analysis separates decision quality from portfolio mix

Participants can identify analytical indicators of fraud, leakage and abnormal claims behaviour.

• Fraud typologies distinguish opportunistic, organised, provider and internal fraud
• Red flags combine claim timing, claimant history, repairer and payment patterns
• Anomaly detection flags unusual claims without requiring confirmed fraud labels
• Network analysis identifies repeated links among claimants, providers and addresses
• False-positive costs considered when setting investigation referral thresholds
• Investigation outcomes feed back into fraud model monitoring datasets

Participants can test predictive model performance and document validation conclusions.

• Training, validation and test samples prevent overstated model performance
• Confusion matrices measure classification outcomes at selected decision thresholds
• Receiver operating characteristic curves assess ranking discrimination for binary outcomes
• Calibration plots compare predicted probabilities with observed event rates
• Population stability index detects material changes in input distributions
• Validation reports record limitations, assumptions, findings and remedial actions

Participants can establish proportionate governance controls for insurance analytical models.

• Model inventory records purpose, owner, users, inputs and materiality rating
• Model documentation specifies methodology, assumptions, transformations and known limitations
• Independent review challenges conceptual soundness, implementation and outcome reasonableness
• Change control distinguishes parameter updates, redevelopment and emergency corrections
• Monitoring thresholds trigger investigation, recalibration, replacement or model retirement
• Approval committees receive concise evidence before material model deployment

Participants can assess reserve estimates using claims development data and uncertainty measures.

• Case reserves, incurred but not reported, and settlement costs distinguished
• Chain-ladder method projects ultimate claims from cumulative development patterns
• Bornhuetter-Ferguson method combines expected loss ratios with reported experience
• Development factors assessed for volatility, credibility and operational process changes
• Reserve diagnostics compare actual emergence against prior ultimate projections
• Reserve ranges communicate uncertainty rather than a single point estimate

Participants can assess concentration and accumulation risk under severe loss scenarios.

• Accumulation analysis aggregates insured values by peril, zone and event footprint
• Probable maximum loss estimates inform underwriting limits and reinsurance decisions
• Return periods distinguish frequent losses from severe low-frequency event scenarios
• Event loss tables compare gross, net and reinsurance-recovered loss outcomes
• Exposure geocoding quality tested before geographic accumulation conclusions are drawn
• Scenario assumptions document hazard, vulnerability, policy terms and recoveries

Participants can evaluate how reinsurance structures affect retained insurance risk.

• Quota share, surplus, excess-of-loss and aggregate covers compared
• Attachment points and limits determine ceded and retained loss shares
• Reinstatement provisions model additional protection after qualifying loss events
• Net loss distributions quantify risk transfer under alternative treaty structures
• Counterparty exposure assessed through reinsurer credit quality and recoverable balances
• Reinsurance optimisation balances premium cost, volatility reduction and capital relief

Participants can connect insurance loss uncertainty with solvency and capital risk measures.

• Solvency capital requirements link adverse scenarios with available financial resources
• Own risk and solvency assessment connects strategy, risk profile and capital
• Stress testing applies plausible shocks to loss, lapse, market and credit assumptions
• Reverse stress testing identifies scenarios that threaten business viability
• Correlation assumptions affect diversification benefits within aggregate capital calculations
• Management actions assessed for timing, feasibility and adverse side effects

Participants can explain how insurance risk analysis informs IFRS 17 reporting and management insight.

• IFRS 17 measurement components include fulfilment cash flows and contractual service margin
• Risk adjustment reflects compensation required for non-financial insurance risk
• Groups of contracts require profitability monitoring and loss-component identification
• Claims assumptions reconcile actuarial estimates with finance reporting evidence
• Actual-versus-expected analysis explains movements in insurance service results
• Risk dashboards connect operational indicators with financial reporting impacts

Participants can communicate model results, uncertainty and recommended actions to decision-makers.

• Executive risk narratives state decision context, evidence, uncertainty and recommendation
• Waterfall charts explain movements in loss ratio, reserves and capital
• Sensitivity tables show material assumptions and corresponding financial effects
• Model limitations presented plainly without overstating predictive certainty
• Risk indicators assigned owners, thresholds, reporting frequency and escalation actions
• Committee papers distinguish technical findings from management decisions required

Participants can plan the controlled implementation and ongoing monitoring of an insurance risk model.

• Implementation plans define business owner, technical owner, milestones and dependencies
• Data refresh schedules specify source controls, reconciliations and exception handling
• Champion-challenger testing compares proposed models against current decision methods
• Monitoring dashboards track performance, drift, overrides and business outcomes
• User guidance clarifies permitted uses, prohibited uses and escalation routes
• Post-implementation reviews confirm benefits, control effectiveness and remediation priorities

Participants can build, review and present a complete insurance risk analysis with model evidence and management actions.

• Define a portfolio risk question, decision owner and measurable success criteria
• Prepare policy, claims and exposure data with documented quality controls
• Build frequency, severity or classification models suited to the stated question
• Validate outputs using calibration, discrimination, stability and business reasonableness checks
• Quantify scenario impacts on loss ratio, reserves, capital or reinsurance recoveries
• Present findings, limitations, controls and implementation recommendations to a review panel