medium · FRM Part 2 Current Issues
A bank finds that its AML (Anti-Money Laundering) AI model has a 95% false-positive rate.
Why might a CRO still prefer this over a rule-based system with a 99% false-positive rate?
- Because Basel III explicitly mandates that all bank AML alert systems maintain a set minimum false-positive detection rate.
- Because AI-driven AML detection models are automatically exempt from Model Risk Management review whenever their error rates run too high.
- Because the AI model likely improves the 'recall' (catching more true laundering) while still reducing the total investigation workload.
- Because a 95% false-positive rate, taken alone without any further supporting analysis, proves the model treats every customer segment fairly.
Sign up free to see the explanation and track your rank →
More FRM Part 2 Current Issues practice
- According to the BCBS standard for cryptoassets, a bank's to… — What is this limit?
- What primary balance (as a % of GDP) is required to stabilize the debt-to-GDP ratio?
- SVB's management removed interest rate hedges in 2022. According to the Economic Value of
- In monitoring model stability, which metric is commonly used to quantify the shift in dist
- What does a SHAP value of zero for a specific feature in a credit scoring model imply?
- A bank provides a 'NAV facility' to a private equity fund. If the fund's underlying assets
- What is the bank's primary ongoing obligation to regulators regarding this new model?
- If the firm's EBITDA declines, when can the lender intervene?