Insurance Profitability Analysis Tool: Case Success Story

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.

Insurance

Data Science

Data Insights & Forecasting

Insurance

Data Science

Data Insights & Forecasting

data processed

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.

Luxury Retailer AI Demand Forecasting Implementation

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.

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Great work! The team provided an excellent solution for consolidating our data from multiple sources and creating valuable insights for our business.

Sean B.

CEO Insurance provider

The Way We Deal with Your Task and Help Achieve Results

Step 1 of 5

Web development discovery

It's a good time to get info about each other, share values, and discuss your project in detail. We will advise you on a solution and help you understand if we are a perfect match for you.

Step 2 of 5

Discovering And Feasibility Analysis

One of our core values is flexibility. Hence, we work with either one-page high-level requirements or a whole pack of tech docs. In AI demand forecasting case studies, there are numerous models and approaches, so at this stage, we perform a set of interviews to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create a solution architecture, a project plan, and a list of insights or features to achieve.

Step 3 of 5

Solution Development

The work starts with data gathering, data cleaning, and analysis. Feature engineering helps to determine your target variable and build several models for the initial review. Further modeling requires validating results and selecting models for further development. Ultimately, we interpret the results. Nevertheless, demand forecasting solution modeling is a process requiring many back-and-forth iterations. We are result-focused, as it's also one of our core values.

Step 4 of 5

Solution Delivery

AI demand forecasting solutions can be a list of insights or models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect the client's participation in the project. While modeling, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high level of communication is also our core value.

Step 5 of 5

Support And Continuous Improvement

We understand how crucial the solutions that we code for our clients are! We aim to build long-term relations, providing guarantees and supporting agreements. Moreover, we are always happy to assist with further developments, and statistics show that 97% of our clients return to us with new projects.

Success stories

Check out a few case studies that show why DATAFOREST will meet your business needs.

All Success Stories

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The team reliably achieves what they promise and does so at a competitive price. Another impressive trait is their ability to prioritize features more critical to the core solution.

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900h+

manual work reduced

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system integrations

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Their technical knowledge and skills offer great advantages. The entire team has been extremely professional.

Performance Measurement

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17%

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25%

Improvement in Employee KPI Achievement Rate

Amir R.

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They easily understand industry-specific data and KPIs, and their efficiency as a team allows them to deliver results quickly.

Client Identification

The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.

customer retention boost

25%