Risk Management for
the Insurance Industry:
A Machine Learning Approach
Bruce Ratner, Ph.D.
The insurance industry market conditions today remain unstable, as regulators are increasing capital requirements to remain profitable while keeping earnings consistent. To achieve this bi-objective, insurance executives need to employ a risk management modeling approach powerful enough to develop analytical strategic for identifying and understanding what drives risk by dimensions such as geography, business unit and market segment. The current statistical methodology utilized lacks the data mining muscle to produce the necessary analytical strategic, because of today’s gargantuan insurance data.
The purpose of this article is to present a machine learning approach – the GenIQ Model© – that has the data mining muscle for digging into the massive data to extract the needed analytical strategic. The GenIQ Model is a machine learning alternative model to the statistical regression model that lets the data define the model – automatically data mines for new variables, performs variable selection, and then specifies the model equation. GenIQ, exclusive of statistical regression’s imposed data restrictions, assumptions, and pre-specified form, clearly offers a superior approach for identifying and understanding the key drivers of risk.
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