---
title: "AccordionV2 Sidenav Demo"
url: "https://www.guidewire.com/it/dev/accordionv2-sidenav.md"
language: "it"
locale: "it"
---

# AccordionV2 Sidenav Demo

## Top Terms in Insurance Analytics Fundamentals

## Insurance Analytics

| What is it? | The systematic use of data, statistical methods, and models to generate insights and information that support insurance decisions. |
| --- | --- |
| When is it relevant? | Across the insurance lifecycle, from marketing, sales and distribution, to underwriting and pricing, and through to claims management, portfolio management, and customer renewals. |
| What does it do? | Transforms data into information, insights and recommendations. |
| What outcome does it create? | More accurate, consistent, and profitable insurance decisions. |
| Examples | Using expected loss models to inform insurance pricing, or claims models to guide efficient claims handling. |

## Descriptive Analytics

| What is it? | Analysis that summarizes historical and current insurance data. |
| --- | --- |
| When is it relevant? | During operational monitoring, reporting, and governance activities. |
| What does it do? | Establishes visibility into performance metrics and trends. |
| What outcome does it create? | A factual baseline for accountability and oversight, and a shared understanding of what has occurred. |
| Examples | Dashboards tracking claim frequency and loss ratio trends. |

## Diagnostic Analytics

| What is it? | Analysis that explains why outcomes occurred. |
| --- | --- |
| When is it relevant? | When performance deviates from expectations. |
| What does it do? | Identifies drivers, correlations, and root causes. |
| What outcome does it create? | Insight into performance variability. |
| Examples | Analyzing loss ratio deterioration by geography. |

## Predictive Analytics

| What is it? | Statistical and machine learning techniques that use patterns in the data to forecast likely future outcomes. |
| --- | --- |
| When is it relevant? | Before decisions are made, especially when foreknowledge of the outcome would affect the decision made. |
| What does it do? | Estimates probabilities and expected values of future events. It informs a decision-maker of what is more or less likely to happen. |
| What outcome does it create? | Reduced uncertainty and improved anticipation of risk and opportunity. |
| Examples | Predicting claim severity at first notice of loss. |

## Prescriptive Analytics

| What is it? | Analytics that recommends or automates actions based on predictions. |
| --- | --- |
| When is it relevant? | At the point of decision execution within insurance workflows. |
| What does it do? | Guides or automates optimal actions, reflecting corporate direction based on the predicted information. |
| What outcome does it create? | Improved decision execution and measurable performance gains. |
| Examples | Routing claims to fast-track or special investigation. |