Working in insurance sector, we have learnt that agents are the centre piece and end point through which insurance companies sell and customers buy insurance. An Agent typically interacts with 5-50 potential customers in a business day. Solving agents issues helps not only the customers but also benefits the insurance companies.
Understanding the insurance customer needs helps to determine the risks that the insurance companies have to bear and also gain insight how to spread the risks among the insured and insurance companies.
What it does
The Policy Risk Advisor app helps agents to request quotes from different insurance companies that partner with Swiss Re. All the coverage related information along with quotes are sent to trade off analytics to do a risk based assessment and determine the appropriate policy based on desired coverages
How we built it
Challenges we ran into
It was difficult to incorporate/merge multiple data sources and run them through the trade off analytics app. Some of the other Watson API's were also not giving the desired results. For Example: Alchemy Insights New API was not working correctly.
Accomplishments that we're proud of
The desktop app has a simple user friendly UI, to capture the necessary information and increase agent satisfaction. the trade off analytics widget shows the quote data in a visual form which helps the agent make the decision faster.
What we learned
We have learned the trade off analytics is a good solution and has many application in health care , P&C insurance, financial etc.
What's next for Policy Risk Advisor - Swiss Re Hackathon
In the next 2 months we plan to incorporate advance machine learning algorithms to better match customer profile and give more accurate quotes. We also plan to make the app mobile responsive.
We are also planning to re-design the app for customers who want to buy insurance directly.