Risk Assessment & Underwriting Flashcards
7 cards from real CFP practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Risk Assessment & Underwriting flashcards as text
Which machine learning technique is most commonly used in fintech underwriting to detect non-linear relationships between borrower attributes and default probability?
Answer: Gradient boosting
Gradient boosting models (e.g., XGBoost, LightGBM) excel at capturing complex non-linear patterns in credit risk data.
In open banking-based underwriting, what data source provides the most real-time view of a borrower's cash flow behavior?
Answer: Bank transaction feeds via API
Bank transaction feeds accessed through open banking APIs offer up-to-date, granular cash flow data not available from static bureau reports.
A fintech lender uses alternative data to underwrite thin-file applicants. Which regulatory concern is MOST relevant when using social media activity as a risk signal?
Answer: Fair lending and proxy discrimination
Social media data can inadvertently serve as proxies for protected classes, raising ECOA and Fair Housing Act compliance concerns.
What does a Gini coefficient of 0.70 indicate about a credit scoring model's discriminatory power?
Answer: Strong separation between good and bad borrowers
A Gini coefficient of 0.70 (equivalent to AUC of 0.85) reflects strong ability to rank-order creditworthiness.
Which underwriting approach best mitigates model risk when deploying an AI-based credit decision system?
Answer: Implementing a challenger model framework with ongoing monitoring
A champion-challenger framework allows continuous comparison of model performance and catches degradation before it causes significant losses.
In embedded finance underwriting, a merchant's point-of-sale sales data is used to extend working capital loans. This is an example of which underwriting innovation?
Answer: Revenue-based underwriting
Revenue-based underwriting uses real-time sales or revenue data to assess repayment capacity, especially for SMBs without strong credit histories.
What is the primary purpose of a vintage analysis in fintech lending risk management?
Answer: Tracking loan cohort performance over time to identify underwriting quality trends
Vintage analysis groups loans by origination period and tracks their delinquency/loss curves to reveal whether underwriting standards are tightening or loosening.