AIDA key terms

The end-to-end set of activities an insurer performs to create and deliver value, from product development and marketing through underwriting, policy administration, claims handling, and customer service.

Analytics that summarizes historical data to show what happened, such as loss ratio trends or claim counts by region, typically presented through reports and dashboards.

Analytics that examines data to explain why an outcome occurred, often by comparing segments, drilling into root causes, and testing relationships among variables.

Analytics that uses historical data and statistical or machine-learning models to estimate the likelihood of future outcomes, such as claim frequency or customer churn.

Analytics that recommends specific actions or decisions by combining predictions with business rules and optimization, for example suggesting the best settlement offer or pricing action.

The process of exploring large datasets with statistical and computational techniques to uncover previously unknown patterns, relationships, and anomalies that can inform business decisions.

A statistical or machine-learning technique that maps input variables to the probability of an outcome, producing a score used in underwriting, pricing, marketing, or claims decisions.

The grouping of insureds with similar expected loss characteristics so that premiums can be set fairly and adequate funds collected to pay claims and expenses.

Technology that collects data on vehicle use, such as mileage, braking, and time of day, through devices or smartphones, enabling usage-based insurance and behavior-based risk assessment.

The process of quickly evaluating each claim's severity, complexity, and fraud indicators to route it to the appropriate adjuster, workflow, or investigation resource.

The use of data analysis and models to identify suspicious claims or applications by detecting anomalies, unusual patterns, and links among people, addresses, and providers.

Computer-based estimation of potential losses from natural or man-made catastrophes using hazard, exposure, and vulnerability data to support underwriting, pricing, and reinsurance decisions.

The estimation of funds an insurer must hold for claims that are reported but unpaid and for losses that have occurred but not yet been reported.

Dividing a customer or prospect population into groups with similar needs, behaviors, or value so that marketing, product, and service efforts can be targeted more effectively.

The expected net profit an insurer will earn from a customer over the entire relationship, used to guide acquisition spending, retention efforts, and cross-selling decisions.

The presentation of data in charts, graphs, maps, and dashboards so that patterns and insights can be understood quickly by decision makers.

Datasets too large, fast, or varied for traditional tools, commonly described by volume, velocity, variety, veracity, and value, requiring new storage and analytical methods.

The degree to which data is accurate, complete, consistent, timely, and fit for its intended use in analysis and decision making.

A branch of artificial intelligence in which algorithms learn patterns from data and improve with experience, enabling prediction and classification without explicit rule programming.

A field of artificial intelligence that enables computers to read, interpret, and analyze human language, used to mine claim notes, emails, and customer reviews for insight.

Automated, rules- or model-based handling of a transaction from submission to completion with little or no manual intervention, common in personal and small commercial lines.

A plan that sets an organization's direction and priorities using evidence from data analysis, with measurable goals and decisions reviewed as new information emerges.

A documented plan describing how an organization will acquire, manage, govern, and use data as an asset to support its business goals.

A framework describing progressive stages of analytics capability, from basic reporting to predictive and prescriptive use, helping organizations assess gaps and plan improvements.

The shared values, behaviors, and practices that encourage people throughout an organization to use data and evidence in decisions rather than intuition alone.

The ability to read, interpret, question, and communicate with data, including understanding basic statistics and recognizing misleading charts or unsupported conclusions.

An executive responsible for enterprise data strategy, governance, quality, and analytics value creation, often also overseeing privacy and data-related risk.

The policies, roles, standards, and processes that determine how data is defined, protected, accessed, and used across an organization.

Assignment of responsibility for specific data assets to individuals who ensure quality, consistent definitions, and appropriate use within business units.

A discipline for creating and maintaining consistent, authoritative reference data, such as customer, policy, or product records, shared across systems to reduce conflicts and errors.

A structured justification for an investment that states the problem, proposed solution, expected benefits, costs, risks, and how results will be measured.

A centralized team that provides expertise, standards, tools, and shared services to build and spread analytics capability across an organization's business units.

Structured methods used to prepare people for new processes, tools, or roles and reduce resistance so that changes are adopted and sustained.

The process of creating economic value from data through improved decisions, new products and services, efficiency gains, or selling insights to partners.

Principles and practices governing the fair, transparent, and responsible use of data, including avoiding harm, respecting privacy, and preventing bias.

Data that can identify a specific person directly or when combined with other information, such as name, address, Social Security number, or account details.

The controls used to identify, validate, monitor, and document models, addressing errors, inappropriate use, and outdated assumptions that could cause poor decisions.

The practice of combining data, narrative, and visuals to explain findings clearly and persuade decision makers to act on the insight.

A sequenced plan of analytics initiatives, resources, and milestones that moves an organization from its current capability toward its data-driven goals.