Add context paragraphs

app.py

@@ -61,6 +61,65 @@ with demo:
         """
     )
 
+    with gr.Accordion("What is credit scoring for card approval?", open=False):
+        gr.Markdown(
+            """
+            It is a complex process that involves several entities: the applicant, the bank, the 
+            credit bureau, and the credit scoring agency. When you apply for a credit card, you
+            provide personal and financial information to the bank. This might include your income,
+            employment status, and existing debts. The bank uses this information to assess your 
+            creditworthiness. To do this, they often turn to credit bureaus and credit scoring 
+            agencies. 
+            - Credit bureaus collect and maintain data on consumers' credit and payment 
+            histories. This data includes your past and current debts, payment history, and the 
+            length of your credit history. 
+            - Credit scoring agencies use algorithms to analyze 
+            the data from credit bureaus and generate a credit score. This score is a numerical 
+            representation of your creditworthiness. 
+            - The bank uses your credit score, along with 
+            the information you provided, to make a decision on your credit card application. A 
+            higher credit score generally increases your chances of being approved and may result 
+            in better terms (like a lower interest rate). 
+            """
+        )
+
+    with gr.Accordion("Why is it critical to add a new privacy layer to this process?", open=False):
+        gr.Markdown(
+            """
+            The data involved is highly sensitive. It includes personal details like your Social 
+            Security number, income, and credit history. There's significant sharing of data 
+            between different entities. Your information is not just with the bank, but also with 
+            credit bureaus and scoring agencies. The more entities that have access to your data, 
+            the greater the risk of a data breach. This can lead to identity theft and financial 
+            fraud. There's also the issue of data accuracy. Mistakes in credit reports can lead to 
+            unjustly low credit scores, affecting your ability to get credit. 
+            """
+        )
+
+    with gr.Accordion(
+        "Why is Fully Homomorphic Encryption (FHE) a solution for better credit scoring?", 
+        open=False,
+    ):
+        gr.Markdown(
+            """
+            Fully Homomorphic Encryption (FHE) is seen as an ideal solution for enhancing privacy 
+            and accuracy in credit scoring processes involving multiple parties like applicants, 
+            banks, credit bureaus, and credit scoring agencies. It allows data to be encrypted and 
+            processed without ever needing to decrypt it. This means that sensitive data can be 
+            shared and analyzed without exposing the actual information to any of the parties or 
+            the server processing it. In the context of credit scoring, this would enable a more 
+            thorough and accurate assessment of a person's creditworthiness. Data from various 
+            sources can be combined and analyzed to make a more informed decision, yet each party's 
+            data remains confidential. As a result, the risk of data leaks or breaches is 
+            significantly minimized, addressing major privacy concerns. 
+
+            To summarize, FHE provides a means to make more accurate credit eligibility decisions 
+            while maintaining strict data privacy, offering a sophisticated solution to the delicate 
+            balance between data utility and confidentiality.
+            """
+        )
+
+
     gr.Markdown("## Step 1: Generate the keys.")
     gr.Markdown("<hr />")
     gr.Markdown("<span style='color:grey'>Applicant, Bank and Credit bureau setup</span>")