CPCU 550 study plan

Map the full CPCU 550 data-and-technology domain into a personal outline that lists every major topic area, then rank each area by your current confidence

Define the core vocabulary of data in insurance operations, including structured versus unstructured data, data governance, data quality dimensions, and the data lifecycle, in your own written definitions

Trace how a single policy transaction (quote, underwrite, issue, endorse, renew, claim) generates and consumes data at each step

Set up a study system (spaced-repetition deck, error log, and weekly review slot) and complete one baseline self-quiz to record your starting accuracy

Explain the difference between data, information, and analytics insights using an insurance example for each

Describe the main analytics types (descriptive, diagnostic, predictive, prescriptive) and match each to a realistic insurer use case such as claims triage or pricing segmentation

Identify the roles and responsibilities in a data governance program, including data ownership, stewardship, and stewardship escalation paths

Diagnose common data quality failures (duplication, incompleteness, inconsistency across systems) and propose a control that reduces each

Summarize how insurers collect and use external data sources such as credit-based insurance scores, geospatial data, telematics, and third-party risk databases, and note the regulatory sensitivities of each

Explain the underwriting and pricing uses of predictive models, including segmentation, risk selection, and the trade-off between accuracy and explainability

Outline the major data privacy and protection obligations an insurer faces and how they constrain data use in underwriting and marketing

Describe how data flows between core systems (policy administration, claims, billing) and where integration failures create operational risk

Explain the components of an insurance technology stack: policy administration, claims management, billing, rating, and agency management systems, and what each system owns

Compare legacy versus modern core-system architectures and list the migration risks an insurer must manage

Describe how APIs and integration layers connect internal systems to brokers, aggregators, and third-party data providers

Connect data and technology concepts to underwriting, claims, and distribution workflows by writing a short end-to-end scenario

Explain the role of artificial intelligence and machine learning in insurance operations, distinguishing supervised, unsupervised, and generative applications

Identify model risk management practices, including validation, monitoring, drift detection, and documentation

Describe emerging technologies relevant to insurance such as telematics, drones, satellite imagery, blockchain, and robotic process automation, and state one concrete use case and one limitation for each

Analyze the ethical and bias risks of automated decisioning in underwriting and claims, and propose mitigation steps

Explain cybersecurity threats facing insurers, including ransomware, social engineering, and third-party/vendor breaches, and describe layered defenses

Outline how insurers manage technology third-party risk, from due diligence through contract terms and ongoing monitoring

Describe business continuity and disaster recovery planning for technology outages and the insurance-specific consequences of downtime

Connect cyber and technology risk to underwriting (cyber coverage) and to enterprise risk management reporting

Integrate the full domain by building a concept map linking data governance, analytics, core systems, AI, and cyber risk to insurance value chain activities

Work through at least 40 practice questions or scenario prompts, logging every miss with the underlying concept and a corrected explanation

Re-teach your three weakest topic areas aloud or in writing without notes, then verify against your source material

Refine your error log into a prioritized list of the ten concepts most likely to cost you points

Complete two timed, full-length practice sets under exam conditions and record accuracy and pacing

Review every missed or guessed question and close each gap by restating the correct reasoning in your own words

Recite the key definitions, frameworks, and trade-offs from memory using your top-ten list as a prompt

Finalize exam-day logistics and a last-48-hour review schedule limited to your error log and top-ten list