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Eliminating data bias with fuzzy logic

AI offers insurers many benefits, but one downside could be unintended bias in how claims are assessed based on historical outcomes.

By Gary Saarenvirta | March 23, 2022 at 12:00 AM
Simply removing variables concerning race, gender, socioeconomic, geodemographic or other attributes is not enough to eliminate historical bias. Simply removing variables concerning race, gender, socioeconomic, geodemographic or other attributes is not enough to eliminate historical bias. (Photo: ipopba/Adobe Stock)

Due to significant industry challenges which have been considerably accelerated over the last 18 months, such as emerging fraud and increasingly sophisticated schemes, AI has rapidly evolved into a strategic priority and is among the leading keys for success among insurers today.

There is no doubt AI is propelling companies to success and providing them with a competitive edge — this can be attributed to AI’s ability to swiftly compare and process millions of data points, like claim details. However, for many insurers, organizational challenges remain which constrain fully embracing the power of AI. Prominent among these challenges are ethical concerns, namely the issue of bias.

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