AIDA study plan

Diagram the insurance value chain end to end, labeling every stage from product design and underwriting through claims, and name the data generated at each stage.

Classify the major data sources - policy administration, claims, third-party and bureau data, telematics and sensors, unstructured text - by which value-chain stage consumes them and for what decision.

Write out how analytics moves loss ratio, expense ratio, and combined ratio, working through at least three concrete stages numerically.

Build a 30-term glossary of value-chain and insurance-operations vocabulary and drill it until you can define each term without notes.

Contrast the modeling approaches used in risk selection, tiering, and price optimization, and state the trade-offs and failure conditions of each.

Trace a claim from first notice of loss to settlement and mark exactly where triage, subrogation, reserving, and fraud-detection analytics intervene.

Compare life, health, property-casualty, and annuity contexts for at least two use cases each to isolate where analytics practice genuinely differs by line of business.

Draft five original exam-style items on value-chain analytics, each with a written rationale for the correct answer and for each distractor.

Evaluate data-quality dimensions - accuracy, completeness, timeliness, consistency, granularity - against a specific claims dataset scenario and prescribe remediation for each gap found.

Summarize the regulatory and ethical constraints that bound analytics in each value-chain stage: unfair discrimination, explainability, privacy, and consent.

Define data-driven strategy and distinguish it from data-informed and intuition-led decision-making using two worked examples.

Diagnose three common failure modes of analytics programs - poor data, absent executive sponsorship, undefined success metric - and propose a concrete countermeasure for each.

Frame a business problem as an analytics question that specifies the decision, the decision owner, and the measurable outcome.

Build a business case quantifying expected lift, implementation cost, and payback period for one analytics initiative.

Map stakeholders and their incentives for that initiative, and identify the two most likely blockers plus a mitigation for each.

Complete a timed 40-question mixed-domain set and log every miss by domain and by error type (knowledge gap, misread, or reasoning slip).

Design an implementation roadmap with phases, accountable owners, and explicit decision gates for one initiative.

Select leading and lagging KPIs for that initiative and specify the measurement cadence and source system for each.

Compare centralized, federated, and hybrid analytics operating models on speed, cost, governance, and talent, and recommend one for a stated context.

Anticipate change-management resistance from underwriters, claims adjusters, and distribution partners, and write a specific tactic for each group.

Sequence the model lifecycle from problem definition through deployment, monitoring, and retirement, naming the decision made at each gate.

Specify monitoring metrics for performance degradation, data drift, and bias, and set thresholds that trigger review or rollback.

Argue both sides of one ethical trade-off, such as granular pricing versus fairness, and defend a final recommendation.

Write five original scenario items that require choosing between two defensible strategies, with rationales explaining why the weaker option fails.

Rework every logged error from weeks 4 through 6 into a corrected note that states the underlying principle rather than just the right answer.

Connect a Domain 1 use case to a Domain 2 implementation decision across three end-to-end scenarios, naming the trade-off at each handoff.

Rehearse out loud a five-minute explanation of a full analytics initiative to a non-technical executive, then tighten anything that needed jargon.

Re-take quizzes on your three weakest subtopics and reach at least 85% on each.

Complete three timed 60-question mixed sets under exam conditions and record scores by domain and subtopic.

Review only items missed twice, and retire any flashcard answered correctly three times consecutively.

Recite from memory the value chain, the model lifecycle, and the KPI framework without referring to notes.

Finalize a one-page exam-day sheet of formulas, frameworks, and decision rules you keep confusing.