Data Cleaning Solution

for Insurance Company

Overview

Our client is one of the leading Insurers in the UK and operates globally. It struggles with a huge flow of data. The large customer base makes it difficult to maintain an error-free and consistent database. As a result, many irregularities have crept into the system. Take a look at the results of the data cleaning we performed.

Overview

Goal

Client had a large dataset containing information about policyholders, such as name, age, gender, address, policy type, premium amount, and claims history. The company wanted to use this data to improve their risk assessment process and provide better coverage to their customers.

In order to be able to quickly control the quality of the data, the design and development of an analytical platform for data processing was started, which was supported by a Machine Learning solution.

Solution

In a nutshell, our data architect was responsible for designing and developing a solution that would clean the data. A pipeline was created in Azure technology that, in conjunction with a functioning Machine Learning (ML) solution, cleaned data of errors such as typos or incorrect formats. Then data was saved back to Snowflake, where it was stored and analyzed.

01

The first step we took in the data cleaning process was to check for missing values. We also implemented a system to ensure that all required fields were filled out before a policy could be approved.

03

Finally, we checked for data integrity to ensure that the data was accurate and complete.

02

We standardized the data to ensure that it was consistent across the dataset and remove duplicates that could cause problems when analyzing data. This involved such practices as identifying policyholders with similar or identical names and addresses, and verifying that they were indeed unique individuals.

Results

Our client told us that the implemented service has benefited the company on 3 levels:

Facilitated communication: The data cleaning process removed errors and inconsistencies from the data set, making it easier for the insurance company to communicate with customers. This enabled them to reach customers faster and with more accurate information, leading to improved customer satisfaction.

Better sales performance: By cleaning up the data, the insurance company was able to more accurately identify its customers and their unique needs. This allowed it to assign better offers, and indirectly led to increased sales revenue.

Reduced risk: With accurate addresses and data in the system, the insurance company was able to better manage customer inquiries and avoid communication errors. This helped reduce the risk of miscommunication or misunderstandings, which led to a more efficient claims process and increased customer confidence in the company.