CORES Loss Data Collection & Analysis 2 — Questions and Answers
Question 1: Which loss data attribute is MOST critical for determining whether an event should be classified as operational risk versus credit or market risk?
- Gross loss amount
- Loss causation and trigger event (Correct answer)
- Recovery timeline
- Business line affected
Correct answer: Loss causation and trigger event
The root cause and trigger of the loss determines its risk category; an operational failure causing a credit loss is still classified as operational risk.
Question 2: Under the Basel Committee's Loss Data Collection guidelines, what is the recommended minimum gross loss threshold for including retail banking events in an internal loss database?
- $1,000
- $10,000 (Correct answer)
- $20,000
- $50,000
Correct answer: $10,000
Basel guidance suggests a €10,000 (approximately $10,000) minimum threshold for retail banking to ensure data collection is practical and material.
Question 3: A bank records a $5M fraud loss in Q1 but recovers $3M through insurance in Q3. What net loss figure should appear in the operational risk capital model?
- $5M gross loss only
- $3M recovery only
- $2M net loss
- $5M with $3M shown as a separate recovery line (Correct answer)
Correct answer: $5M with $3M shown as a separate recovery line
Best practice requires gross loss and recoveries to be tracked separately to preserve the full picture of exposure and recovery effectiveness.
Question 4: Which method is used to combine internal loss data with external loss data to address the scarcity of high-severity tail events in internal databases?
- Monte Carlo simulation
- Scenario analysis blending
- Scaled external data integration (Correct answer)
- Loss distribution approach weighting
Correct answer: Scaled external data integration
External loss data is typically scaled or adjusted for firm size before being blended with internal data to enrich the tail of the loss distribution.
Question 5: What does the term 'near-miss' refer to in the context of operational risk loss data collection?
- A loss that almost exceeded the reporting threshold
- An event that could have resulted in a loss but did not due to intervention or luck (Correct answer)
- A loss recovered within 30 days
- A loss caused by a third-party vendor
Correct answer: An event that could have resulted in a loss but did not due to intervention or luck
Near-misses are events where a loss was avoided; capturing them provides valuable risk intelligence even though no financial loss occurred.
Question 6: When building a loss distribution for AMA capital calculation, which statistical technique is commonly applied to model the frequency of operational loss events?
- Lognormal distribution
- Poisson distribution (Correct answer)
- Gumbel distribution
- Weibull distribution
Correct answer: Poisson distribution
Poisson distribution is the standard choice for modeling the count of discrete loss events per period in operational risk frameworks.
Question 7: A firm's loss database shows clustering of events at the end of each quarter. What data quality issue does this most likely indicate?
- Systemic underreporting during peak periods
- Delayed booking or reporting bias distorting event dates (Correct answer)
- Seasonality in operational risk
- Threshold manipulation by business lines
Correct answer: Delayed booking or reporting bias distorting event dates
Quarter-end clustering typically reflects delayed recognition or booking of losses rather than true seasonality, introducing timing bias into analytics.
CORES Loss Data Collection & Analysis 2
Which loss data attribute is MOST critical for determining whether an event should be classified as operational risk versus credit or market risk?