---
title: Fraudulent claim detection
description: Improve the accuracy in predicting which insurance claims are fraudulent.
---

# Fraudulent claim detection {: #fraudulent-claim-detection }

This page outlines the use case to improve the accuracy in predicting which insurance claims are fraudulent. It is captured below as a UI-based walkthrough. It is also available as a [Jupyter notebook](pred-fraud-v3.ipynb) that you can download and execute.

{% include 'includes/fraud-claims-include.md' %}

### No-Code AI Apps {: #no-code-ai-apps }

Consider building a custom application where stakeholders can interact with the predictions and record the outcomes of the investigation. Once the model is deployed, predictions can be consumed for use in the [decision process](#decision-process). For example, this [No-Code AI App](app-builder/index) is an easily shareable, AI-powered application using a no-code interface:

![](images/fraud-claim-1.png)

![](images/fraud-claim-2.png)

### Notebook demo {:#notebook-demo}

See the notebook version of this accelerator [here](pred-fraud-v3.ipynb).
