This is the first in a series of articles by Valen Analytics looking at the hurdles that insurers must overcome to effectively implement and gain value from data analytics programs.
Executive SummaryBrokers may be the first to notice a bias in a carrier's predictive model—indirectly—because of the impact of the bias on their commissions, notes Kirstin Marr, president of Valen Analytics. With predictive analytics now increasingly being used by insurers and incorporated into decision-making, analytics also have a growing impact on carrier bottom lines, she says, offering some warning signs of bias and methods to overcome it.
Becoming a data-driven company in the insurance market today is essential, but it requires an understanding of the incremental adjustments necessary to keep a predictive model accurate and operating to its full potential.
Monitoring models in production is critical, as is knowing when they may inadvertently contain biases that could negatively impact the financial health of an insurer’s book of business. Bias can creep in from a number of sources that skew a dataset, and a change in market conditions or strategy can render a model obsolete. There are several remedies to be aware of to ensure a model matches your risk appetite.