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ACCA Strategic Professional · Strategic Business Leader · Enabling success: disruptive technologies

Marlowe Bank trains a model on ten years of past loan decisions to predict default risk. An internal review finds that the model rejects applicants from certain postcodes at much higher rates, reflecting historic lending practice. Which risk does this MOST directly illustrate?

This illustrates algorithmic bias from biased training data. The model learned historic lending patterns, including postcode-based discrimination, and reproduces them in new decisions. The board should respond with governance measures such as data audits, fairness testing and human oversight, since the issue is ethical and reputational rather than technical failure.

  1. AAlgorithmic bias arising from biased training dataCorrect
  2. BModel overfitting caused by too few input variables
  3. CCyber-attack through data poisoning by outsiders
  4. DTechnology obsolescence of the lending platform

Explanation

Models learn patterns in historical data, including past discriminatory practice, and then reproduce them. This is algorithmic bias, with ethical, reputational and legal implications for governance. Overfitting is a different statistical issue and there is no evidence of external attack or obsolescence.

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