Update fraud detection demo

docs/gallery/articles/fraud_detection/index.md

@@ -4,11 +4,12 @@ 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.png
 hide:
     - toc
 ---
-A Taipy Application that analyzes credit card transactions to detect fraud.
+
+A Taipy Application that analyzes credit card transactions to detect fraud and how users can interact with each other in Taipy.
 
 !!! note "Taipy Enterprise edition"
 
@@ -17,25 +18,49 @@ A Taipy Application that analyzes credit card transactions to detect fraud.
     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.png){width=90% : .tp-image-border }
+
+Within tis page, you can access various analyses and visualizations of the data:
+
+- 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/Users.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 displaying notifications from the application or other users.
+
+![User Page](images/User.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.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.png){width=90% : .tp-image-border }