Is Machine Learning the Silver Bullet in Underwriting?

Using machine-learning to underwrite property insurance has been UrbanStat's main focus for the last 3 years. It started as a simple Minimum Viable Product (MVP), we had our highs and lows, we made many mistakes, and every time we see a new data set, we are surprised how much we are still learning.

For engineers, machine learning is a simple concept albeit the complicated math behind it. For non-engineers, it is an abstract concept where you input your data, and it generates magical results.

Machine learning is a powerful tool that helps a variety of different industries in so many great ways. It always requires lengthy preparations on data; certain problems need more research or understanding of an entire industry and its regulations. Insurance is unquestionably one of them.

Predicting the policyholders who will file a claim within the next 12 months sounds like a true supervised learning problem at first. However, when you start thinking about how the industry works, you start seeing that there are no real True Positives or True Negatives.

Supervised learning problems require a historical data set with known outcomes. When it comes to claim prediction, there is the problem of lack of claims. We don't truly know (most of the time) what happens to the customer that switch carriers. Did they stay claim-free?

Another issue is the trouble of defining success. Since insurance is a long-term game, a customer who didn't claim anything for five years could file a large claim in year 6. If you score this customer High in year 4, is your algorithm successful or not?

One of the most significant problems is the imbalance between the customers who claim and don't claim. Most insurance companies have around 1-6% of claim frequency. It means that out of every 100 policyholders, only 1-6 policyholders will file a claim.

Insurance is a highly regulated industry. The way that insurance carriers can use these technologies could be constrained by regulators. The industry should not pass human biases to the algorithms. This is why UrbanStat never uses PII (gender, age, ethnicity, etc.) or credit scores/financial risk information.

Our carrier partners have been generating amazing results improving their loss ratios anywhere from 2 points to 17 points. We've recently looked at about 8 years long data from about 10 carriers in 3 different continents. Loss ratio of our clients has dropped by 10 points on average after they started using our platform.

The tech (machine learning) is not the silver bullet here. The actual silver bullet is the combination of machine-learning, traditional probabilistic modeling, and the last but not least, human intuition. We call this combination 'the Three Pillars of Risk Analysis'.