Why Stress-Testing AI Models Matters — Now More Than
Ever
Artificial intelligence (AI) and machine learning (ML) are now
embedded in the core of banking — powering decisions in
credit, fraud, anti-money laundering (AML), and more. These systems
bring scale and speed — but also a new type of risk.
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AI models are often black boxes: They learn from
data, but under stress — like a downturn, market shock, or
regulatory change — they can behave unpredictably. Unlike
traditional models, they do not degrade gradually. They can fail
fast — misclassifying risk, introducing bias, or generating
false positives — which creates reputational, financial, and
compliance risk. For example, during COVID-19, many credit
underwriting models that had been trained on years of stable
economic data suddenly rejected large numbers of qualified
borrowers, or conversely underestimated default risk, because they
had never encountered pandemic-like conditions in their training
history. This
