Insurance Profitability Analysis Tool
This project involved developing a tailor-made data analysis tool for a U.S. insurance provider who were facing challenges analyzing a significant volume of data. The Client needed a professional and customized solution which would enable effective analysis of their data and provide actionable insights to improve their business operations. Our solution delivers real-time processing of data, flexible filtering capabilities through dashboards, and also supports dashboards detailing the evaluation of insurance loss or profit by industry vertical. Additionally, a predictive model for profitable insurance cases was built using historical data, and a reporting system was created to show significant factors and profitability based on different metrics.
> 10TB
data processed
89%
accuracy improvement
About the client
The client is a U.S. insurance provider that leverages patented analytical methods and unique data sources to more accurately evaluate risks to homes and properties. Their technology-driven platform streamlines the insurance buying process, offering a superior user experience for both homeowners and independent agents. Using only a property address, they can generate an insurance quote and customised policy in just a few seconds.
Tech stack
Pandas
Dash
Numpy
TensorFlow
Pyspark
The client's needs
The client is facing challenges analyzing a significant volume of data stored in tabular form. The data could not be readily analyzed to determine the profitability of insurance provision in specific cases or to predict profitable insurance cases.
To address these issues, the client required a custom tool that enables real-time processing of data and offers flexible filtering capabilities through dashboards. The tool needed to support the evaluation of insurance loss or profit by industry through dashboards. Predictive profitability modeling required the development of a predictive model.
The project's primary goal is to create a comprehensive reporting framework, utilising graphs which allow the client to understand which factors are significant and to what extent they have gained or lost with each customer. The visualization of insurance metrics needed to showcase how much the company gains or loses by providing services based on these metrics. For example, the tool should reveal that insuring brick houses is more profitable for the client than insuring wooden houses. Similarly, the solution should highlight that houses built between 5-6 years ago are more profitable to insure than those built 7-8 years ago.
Challenges & solutions
Challenge
Develop a customized data analysis tool that enables real-time processing of data and flexible filtering capabilities through dashboards.
Solution
We developed a tool based on the open-source Python library dash-plotly that processes client data in real-time and offers customizable dashboards with filters and formulas. End-users can choose filters based on their unique requirements to view and analyze the data.
Challenge
Build a predictive model for profitable insurance cases using historical data.
Solution
We developed a predictive model that considers various factors, including the type and age of the house, to identify profitable insurance cases.
For both classification and regression tasks, we utilized the Random Forest algorithm.
Additionally, we visualized the most influential features on profit vs. loss to gain insights into the factors that significantly affect the model's predictions. To construct the model, we utilized a dataset containing features and loss values as targets. We took great care to carefully select and preprocess the features, as well as address any missing or category data, to ensure the model's accuracy.
Challenge
Create a reporting system that generates graphs to show significant factors and the profitability of providing insurance services based on different metrics. The visualization should show insurance metrics in the form of graphs.
Solution
The reporting system we developed was based on the dash-plotly library. This library generates reports in the form of graphs to show significant factors and profitability based on different metrics.
Challenge
Process a large amount of data (10 TB) promptly.
Solution
Our solution addresses the data volume issues by correctly implementing multiprocessing methods. Accessing the data through the Postgres database when querying took less than 2 seconds.
Challenge
Optimize dashboard design and loading speed using the dash-plotly library.
Solution
We used the dash-plotly library for optimal dashboard design and fast loading speed. By leveraging dash-plotly mechanisms and processing data sequentially, switching between analysis pages took less than 1 second.
Challenge
Address file size limitations with file uploading.
Solution
The built-in dash-uploader widget does not have file size limitations.
Challenge
Optimize real-time tool performance for large amounts of data.
Solution
Our approach pre-calculated the combinations of filters that take the longest time and recorded them in a database table with a name corresponding to the filter combination with data. When selected, we query the database to retrieve the pre-calculated data set.
Challenge
Split continuous data into equally valued bins for proper graphical display.
Solution
We used the aggregated value of the target function to determine optimal data segments for displaying continuous data on a graph.
Results
DATAFOREST delivered a customized data analysis tool to the client that allows for real-time analysis of their data. This system performs responsively even during the processing of new files. The client can track the system's processing progress via Slack notifications and use the flexible application system to build various reports according to their analysis needs. The tool visualizes tabular data as graphs, enabling the client to objectively assess the profitability or losses of insuring different industries, leading to an increase in the company's revenue from profitable insurance policies. Furthermore, the client can independently upload data files through the tool's page or code and select filters for their analysis needs. They can also download the report in PDF format.
Great work! The team provided an excellent solution for consolidating our data from multiple sources and creating valuable insights for our business.
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