Challenge & Pain Points:
Insurance is about calculable risks. Irrespective of the insurer’s business (pricing) objectives, it is not possible to sustain a viable operation without an accurate estimation of the monetary risk (cost) and its distribution over the insurer’s liability period.
Traditional approaches such as statistical frequency-severity modeling, or GLMs are limited with inherent assumptions, manual feature engineering and low dimensionality due to their ability to exploit limited number of features. In practice, imbalanced data distributions invalidating the assumptions and limited features used result in sub-optimal cost estimations leading to adverse selection of risk instances.
While costing is a regression problem, pricing is an optimization problem towards a usually complex objective function that considers multiple business aspects. Pricing has never been as simple as adding a profit margin over the estimated costs and interests. Although technical profit is still an important factor, it is not the only component of pricing objective function in today’s sophisticated world of insurance. Other components that must be covered by the pricing model include reinsurance, portfolio, customer lifetime value, operations, cash flow and reserve strategies.
Insursight.ai Solution:
Statistical models start by assuming a distribution model for a given dependent variable, such as frequency and severity (or their product), and estimate the model parameters from prior data while selecting the features to use through some hypothesis testing strategy. In a similar fashion, GLMs start by assuming a non-linearity (the link function) through which a linear relation can be built between dependent variables and manually selected features.