How can you model data for insurance analytics?

Insurance analytics models use data to predict target variables such as the probability of a customer filing a claim, the cost of a claim, or the likelihood of a customer churning. To model data for insurance analytics: Identify the target variable and select relevant features Clean and prepare the data, and choose a modeling algorithm Train the model on the data and evaluate its performance Deploy the model if it performs well on the test set Common modeling algorithms include linear & logistic regression, decision trees, random forests, and support vector machines. The best modeling approach depends on the specific dataset and desired outcome. It is important to carefully evaluate the model's performance before deploying it to production.

You need a strong view on what data you need for the intended consumers within the company. That is likely to include actuarial risk, capital and pricing, but also claims processing, risk and compliance. Clearly define the objectives on the analysis, what data is required, and scope out what data is available. There are likely to be some gaps to fill. You will also need to know about how the data is collected and what the fields represent. Don't take column names for granted. Start with this complete view of what you are going to do with the data, it will give you a great framework for assessing if the data you have is complete and appropriate, and where the gaps are which need work.

In the insurance industry, data modeling is of central importance in order to cover various aspects: Insurance product modeling: Data models are used to structure insurance products and define their properties, coverage areas, premiums, etc. Claims modelling: Data models help to capture information about claims, loss amount, settlement and pattern recognition in order to optimize claims processes. Customer modelling: Data models can be used to create customer profiles in order to assess risks, make personalized offers and improve customer loyalty. These applications are just 3 examples of how data modeling is used in the insurance industry to support decision-making, increase efficiency and improve customer service.

I have not worked directly with insurance data, but I have undertaken similar exercises to evaluate risks in cash distribution. I used GLMs to assess risk and determine pricing, while leveraging more advanced predictive models such as gradient boosting or random forests to forecast the likelihood and severity of claims. To identify potential fraud, I employed anomaly detection algorithms that flag inconsistent patterns in claim submissions. This comprehensive approach enabled us to accurately assess risks, optimize monthly amounts, improve person of concern experience, and protect against fraudulent activities.

It is important to consider how you are expecting to build your modelling infrastructure. Programmatic tool like python will give you a lot of flexibility and control on your pipeline but need more specialist skills, low-code packages etc tools like altryx or knime can be managed more easily by business users but are less suited to large complex environments. More thought should also be given to more modern practices such as leveraging a data mesh. Again, have purpose and scalability in mind.

Data modeling tools play a crucial role in managing complex data in the insurance industry. They can support not only the creation of models, but also the targeted adaptation to the specific requirements and structures that insurance data often has. The ability to integrate smoothly with different tools to visualize and analyze data is particularly important to meet the challenges in this area.

Best practices in data modeling for the insurance industry could include for example: Clean data standardization: Uniform data standards are crucial to ensure a consistent and clear data structure. This means that data models should be designed to simplify the integration and interpretation of data. Security and privacy: Data modeling should also consider security aspects, especially in the insurance industry where sensitive customer data is managed. Security protocols and privacy policies should be integrated into the data model to ensure the confidentiality and integrity of the data.

Context is important, know the purpose the data is being used for, and who the consumers are before starting a project. This will be a good guide to start these discussions

Integrating empathy into the consumer experience is pivotal. By genuinely understanding and addressing the needs of your customers, you not only enhance their overall satisfaction but also foster a connection that goes beyond transactions. This empathetic approach contributes significantly to building long-term relationships, leading to increased Customer Lifetime Value (CLTV). It's not just about transactions; it's about creating a lasting impact through understanding and meeting the emotional aspects of consumer needs.