CRISC Information Technology and Security Practice Question
A financial institution is considering adopting a new AI/ML model for credit scoring. The model uses customer demographic data and transaction history. Which of the following risks is MOST likely to cause regulatory penalties if not addressed?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Model bias leading to unfair lending practices
Regulators require explainability for credit decisions to ensure fairness and compliance with regulations like ECOA and GDPR. Model bias can lead to discriminatory outcomes, resulting in significant penalties.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Data privacy of training data
Why it's wrong here
Data privacy is important, but model bias is more directly tied to credit decision outcomes.
- ✗
Model drift due to changing economic conditions
Why it's wrong here
Model drift affects accuracy but is not a primary regulatory concern.
- ✓
Model bias leading to unfair lending practices
Why this is correct
Model bias can violate fair lending laws and result in regulatory fines and reputational damage.
- ✗
Adversarial attacks on the model
Why it's wrong here
Adversarial attacks are a security risk but less likely to cause regulatory penalties directly.
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JA
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This CRISC practice question is part of Courseiva's free ISACA certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the CRISC exam.