Automating insurance claims processing in Python using Deepnote

Insurance claims processing can be a complex and time-consuming task. Automating this process can significantly enhance efficiency, reduce errors, and improve customer satisfaction. In this guide, we'll walk through how to automate insurance claims processing using Python in Deepnote, a collaborative data science platform. We'll cover data extraction, validation, processing, and generating outputs.

Prerequisites

Before we begin, ensure you have the following:

A Deepnote account.
Basic knowledge of Python and data handling libraries such as Pandas.
Access to sample claims data (CSV or any other format).

Importing libraries and data

Import necessary libraries: At the top of your notebook, import the required libraries:

import pandas as pd
import numpy as np

Load your claims data: Upload your claims data file to the project (e.g., claims_data.csv), use Pandas to read the data into a DataFrame:

claims_df = pd.read_csv('claims_data.csv')

Data validation

Define validation functions: create functions to validate key fields in the claims data, such as policy number, claim amount, and dates.

def validate_policy_number(policy_number):
    return isinstance(policy_number, str) and len(policy_number) == 10

def validate_claim_amount(amount):
    return amount > 0

def validate_dates(date):
    try:
        pd.to_datetime(date)
        return True
    except ValueError:
        return False

Apply validation: apply these validation functions to the relevant columns in your DataFrame.

claims_df['policy_valid'] = claims_df['policy_number'].apply(validate_policy_number)
claims_df['amount_valid'] = claims_df['claim_amount'].apply(validate_claim_amount)
claims_df['date_valid'] = claims_df['claim_date'].apply(validate_dates)

Data processing

Filter invalid records: filter out invalid records and focus on valid ones:

valid_claims = claims_df[claims_df['policy_valid'] & claims_df['amount_valid'] & claims_df['date_valid']]

Processing logic: implement any specific processing logic needed for valid claims. For instance, calculate the total claim amount per customer or categorize claims by type:

total_claims_per_customer = valid_claims.groupby('customer_id')['claim_amount'].sum().reset_index()

Generating outputs

Create summary reports: generate summary reports or outputs as needed:

summary_report = valid_claims.groupby('claim_type').agg({
    'claim_amount': ['sum', 'mean', 'count']
}).reset_index()

summary_report.columns = ['claim_type', 'total_amount', 'average_amount', 'claim_count']

Save the outputs: save the processed data and reports to CSV files:

valid_claims.to_csv('valid_claims.csv', index=False)
summary_report.to_csv('summary_report.csv', index=False)

Automate and schedule

Deepnote allows you to schedule notebook runs to automate the process periodically. Set up a schedule to run the notebook daily, weekly, or as needed to ensure your claims processing stays up to date.

Set up a schedule:

Click on the Schedule tab in your notebook.
Configure the schedule according to your needs (e.g., daily at midnight).

Conclusion

Automating insurance claims processing with Python in Deepnote can significantly streamline your workflow, reduce errors, and improve efficiency. Following the steps outlined in this guide, you can set up a basic automation system and expand it further as per your requirements.

Feel free to customize the functions and processing logic to better fit your use case. Happy automating!