Let’s hear from Mr. Suradej Panich Chief Data Scientist of Sunday, about how he runs data lab, incubate and commercialise to the market.
How does Sunday apply data to a few healthcare, flood, insurance pricing including case studies?
This project displays very similar activities of what we do day-to-day as a data scientist, data analyst and data engineer within the data team at Sunday. We have been using both internal and external data sources to predict customer health risk, to determine driving score for better motor insurance pricing that suits each customer behavior and more.
How do you approach to running your lab and commercializing ideas?
In the data team, our lab, sandbox project or whatever name we named it, is an area for our team members to pursue their passion and interest without a boundary of business requirements with an objective for us to learn and try something new both from a perspective of technology or data domain. When the project graduated out of a lab, we will then consider the application of it i.e. whether it could improve our customer’s life, could it help engage us and customer better? Or even if it could help solve at least a small piece of problems in our society. And we take it from there. Some projects get listed on our Sunday Super App for our customers, some get pushed out for free for public use.
Why do you think insurance is an interesting space for aspiring and existing data scientists?
Look at the insurance industry, it has long been a data company for hundreds of years. No underwriter would make a decision to cover a risk they do not have data on hand. People live a very different life compared to the last 10-20 years, which implies the risk that insurer is covering is also evolving. Humans are generating data more than ever in our history and every step or possession they have comes with risk. Insurers who could swiftly make use of these data and keep innovating around the changes will be the one who lasts for the future. No doubt that there are huge opportunities and endless possibilities for data scientists to innovate here.