Notes

Write a note in this area. It's really easy to share with others. Click here ...# Python Code (Data Extraction and Transformation)
import pandas as pd
import re

# Sample email attachment data
email_data = """
Customer Name: John Doe
Loan Amount: $5000
Interest Rate: 4.5%
"""

# Extract data using regular expressions
customer_name = re.search(r'Customer Name: (.+)', email_data).group(1)
loan_amount = float(re.search(r'Loan Amount: $(d+)', email_data).group(1))
interest_rate = float(re.search(r'Interest Rate: (d+.d+)%', email_data).group(1))

# Create a DataFrame
data = {'Customer Name': [customer_name],
'Loan Amount': [loan_amount],
'Interest Rate': [interest_rate]}
df = pd.DataFrame(data)

# Print the structured data
print("Structured Data:")
print(df)

# UiPath Code (RPA Workflow)
# Use UiPath Studio to design and automate the process of extracting data from emails and entering it into the CRM.
# Create a sequence to:
# 1. Log into the email account
# 2. Search for emails with attachments
# 3. Download attachments
# 4. Use OCR activities to extract data from PDFs
# 5. Log into the CRM (Centrex)
# 6. Input the extracted data into the CRM
# 7. End the workflow

# The UiPath code should follow the UiPath Studio workflow design, with activities and sequences connected in the required order.

# Rule Engine (Pseudo Code)
def underwriting_decision(data):
# Pseudo logic for underwriting decision
if data['Loan Amount'] < 10000 and data['Interest Rate'] < 5.0:
return "Approved"
else:
return "Rejected"

# Apply the rule engine to the structured data
decision = underwriting_decision(data)

# Print the underwriting decision
print("Underwriting Decision:", decision)