Let's look at a real world example.

In 2019, Apple's credit card issued in partnership with Goldman Sachs was scrutinized after reports that despite having similar financial profiles, women were offered significantly lower credit limits than men.

The algorithm behind the lending decision was not fully disclosed, raising concerns around algorithmic opacity and gender bias.

This case made headlines and prompted investigations by regulators.

Although the companies denied intention bias, the lack of transparency made it impossible for customers or regulators to verify fairness.

The lesson here is clear.

Even well-intentioned AI systems can perpetrate harm, if not tested, explained, and governed in high stakes domains like credit insurers, banks, and fintechs must be able to detect, explain, and correct biased outcomes.

Bias in AI is not just a technical flaw.

It's a human risk with real financial consequences.

As AI becomes more deeply embedded in lending, trading, and underwriting, financial professionals must prioritize furnace by building inclusive models, auditing data, and demanding transparency.