Insurance Claim System Development

Sameer wants to start a business. He did Ph.D. in machine learning a couple of years back.

A couple of his colleagues want to start something in this area.?

AI in the insurance sector to gain the business benifits to the customer.

Appyling AI power in the Insurance Business.

Team members were thinking about how they could leverage the power of AI to enhance the business, minimize the loss, and also minimize the fraud claims.

Build a digital tool. Automate everything!

This digital tool optimizes support costs and lowers operational expenses, offers more opportunities to collect data, and provides detailed insights about the target audience, automates claims processing and other business tasks, enhances user engagement, and increases the income of an insurance agency.

Current practice teams are using manual and based on the historical data set. ?Full with human expertize dependence and full with human error.

The teams which have taken this assignment started to deep research in this area of what problem we are solving with the power of data.

Team members started thinking about what predictive models we were building to solve that business problem.

The claim process? Can we know through data if the number of claims will come in every month?

From the same predictive model, can we detect if the claim is fraud or not?

• Who are the people who are claiming that anyone is a fraud?
• How can we predict consumer behaviour? with time scale!
• By knowing all these cases early, can an organization save money?
• Can the team predict the payment?
• Can we know the (Demographic features of users or customers such as age, gender, occupation, and address, The frequency and recency with which customers or users,The monetary value of a customer’s interactions with a service, mix of products or services used etc)?

Many fraud claims ask for more money, so the insurance company has to go through the investigation process, which causes a lot of time and money, if such a predictive model informs early all the time and money can be saved.

The question to be asked is, do we have enough data to build such a model?

Do we have all the related objects and connections among objects data available, e.g. claims, payment, and all the important connected details with granular information? Both the volume of data and time horizon information.

Based on this, team members need to analyze the feasibility of building the predictive analysis model.

The next phase could be improving the accuracy of the model.

Organizations are also deep diving into the legal issues of using the data universally. There are significant differences in legislation in different jurisdictions, but a couple of key relevant principles almost always apply. The team needs to know data protection legislation and, in particular; the rules surrounding the use of personal data.

From the product coaching perspective, please research and come out with the proposal

• How do you think you can help the team address some of the challenges? What could be the challenges to implementing this solution?
• How should team members be ready to solve this business problem?
• Do you see any legal challenges the team will experience to implement the solution?
• What could be the design challenges the team will experience to develop the solution?
• What type of infra and competency challenges do team need to overcome?
• Team members are not able to understand the different data types? What do we do?
• Team members are not able to understand the different data storage! What do we do?
• Team members were not able to design the data architecture. What do we do?
• Team members were not able to extract the data from the source. What do we do?
• Challenges with data transformation and data loading. What do we do?

What else we need to know and take care, Please suggest.