Merge pull request #1224 from Avaiga/doc/update-fraud-detection-demo

Update fraud detection demo

docs/gallery/articles/fraud_detection/index.md

@@ -4,38 +4,64 @@ category: finance
 data-keywords: dashboard vizelement layout chart ai multi-page classification enterprise
 short-description: A Taipy Application that analyzes credit card transactions to detect fraud.
 order: 20
-img: fraud_detection/images/fraud_threshold.png
+img: fraud_detection/images/transactions_page.png
 hide:
     - toc
 ---
-A Taipy Application that analyzes credit card transactions to detect fraud.
+
+A Taipy Application to analyze credit card transactions, detect fraud, and collaborate with other 
+application users.
 
 !!! note "Taipy Enterprise edition"
 
     Taipy provides robust, business-focused applications tailored for enterprise environments. To
     maintain standards of security and customization, these applications are proprietary like this
-    application. If you’re looking for solutions that are immediately deployable and customizable to
+    application. If you're looking for solutions that are immediately deployable and customizable to
     your business needs, we invite you to try them out and contact us for more detailed information.
 
-    [Try it live](https://fraud-detection.taipy.cloud/Transactions){: .tp-btn target='blank' }
+    [Try it live](https://fraud-detection.taipy.cloud/Login){: .tp-btn target='blank' }
     [Contact us](https://taipy.io/book-a-call){: .tp-btn .tp-btn--accent target='blank' }
 
 
 # Understanding the Application
 
-This application shows a list of credit card transactions. The user can select a date range to
-predict fraud. The application will then use an XGB model to mark potentially fraudulent
-transactions in red or yellow.
+This application displays a list of credit card transactions. A model estimates whether a 
+transaction is fraudulent; this task can be automatically handled by a pipeline. However, 
+some transactions may require further human review.
+
+![Transactions](images/transactions_page.png){width=90% : .tp-image-border }
+
+Within this page, you can access various analyses and data visualizations:
+
+- List of transactions
+- Client information
+- Fraud details
+
+This demo includes user management and collaboration features. You need to select one of the 
+available users to access the application.
+
+![Users](images/login_page.png){width=90% : .tp-image-border }
+
+After logging in, you can navigate to your user page to view the transactions assigned to you 
+for investigation. You can see both your past transactions and those requiring your attention. 
+Clicking on a transaction in the table will select it and navigate you to the Analysis page. 
+
+This page also includes a newsfeed showing the application or other users' activities.
+
+![User Page](images/user_page.png){width=90% : .tp-image-border }
 
-![List of Transactions Page](images/fraud_transactions.png){width=90% : .tp-image-border }
+The Analysis page presents several pieces of information. The left section explains the model's 
+results (providing explanations on the model output), the middle section displays details about 
+the transaction, and the right section shows information about the client. Here, you can verify 
+the client's identity using a deep learning model.
 
-The user can select a transaction to see an explanation of the model's prediction, as well as the client's
-other transactions.
+You can decide whether the transaction is fraudulent or not. If you are unsure, you can share the 
+transaction with someone else for further review.
 
-![Prediction Explanation Page](images/fraud_explanation.png){width=90% : .tp-image-border }
+![Analysis](images/analysis_page.png){width=90% : .tp-image-border }
 
-The user can also choose the threshold of the model. The threshold is the model output
-above which a transaction is considered fraudulent. The user can select the model according
-to the displayed confusion matrix and by looking at False Positive and False Negative transactions.
+For educational purposes, you can adjust the model's threshold— the output value above which a 
+transaction is considered fraudulent. You can select the threshold by examining the displayed 
+confusion matrix and reviewing false positive and false negative transactions.
 
-![Threshold Selection Page](images/fraud_threshold.png){width=90% : .tp-image-border }
+![Threshold Selection Page](images/threshold_page.png){width=90% : .tp-image-border }