1. According to BIS 2020 research, what did ML network analysis identify as a better SIFI predictor than balance sheet size?
Correct. Network centrality — how deeply connected an institution is to others — proved a better contagion risk predictor than absolute size.
BIS network analysis found connectivity (graph centrality measures) outperformed size as a predictor of systemic importance — a node that is small but central can transmit shocks more effectively than a large but peripheral one.
2. Question 7
Correct!
Incorrect.
3. What legal concept captures the difficulty of assigning blame when AI harm results from contributions by many different actors?
Correct. The "many hands problem" describes how traditional liability frameworks, designed for single human decision-makers, break down when developers, trainers, deployers, and users all contributed to a harmful outcome.
Incorrect. Legal scholars call this the "many hands problem" — when so many parties contributed to an AI system's harmful output that no single actor can be clearly assigned responsibility.
4. Question 10
Correct!
Incorrect.
5. Question 15
Correct!
Incorrect.
6. Question 4
Correct!
Incorrect.
7. Which institution manages risk across $21.6 trillion in assets using machine learning — potentially creating systemic risk through correlated behavior?
Correct. BlackRock's Aladdin manages risk for over 4,000 asset managers and runs 5,000 daily stress tests. Its dominance means correlated AI behavior could amplify market stress — a concern regulators have flagged.
Incorrect. BlackRock's Aladdin platform oversees $21.6 trillion in assets across 4,000+ institutional clients. Regulators have noted that its widespread use creates correlated behavior risk.
8. Which type of DeFi automation is specifically designed to close positions when collateral ratios fall below a threshold?
Correct. Liquidation bots monitor on-chain collateral ratios continuously and automatically sell undercollateralized positions — protecting lenders but capable of triggering cascading liquidations, as seen in the Three Arrows Capital collapse.
Incorrect. Liquidation bots specifically monitor collateral ratios and automatically close positions that fall below the required threshold — they played a key role in the Three Arrows Capital cascade.
9. The ECB found that Twitter-derived consumer sentiment led the official Consumer Confidence Indicator by what time span?
Correct — two to four weeks, meaningful given the ECB Governing Council meets every six weeks.
The ECB's 2022 working paper found Twitter sentiment led the official CCI by two to four weeks — short-run but policy-relevant given the six-week meeting cycle.
10. Question 3
Correct!
Incorrect.
11. Question 8
Correct!
Incorrect.
12. What is the primary weakness of DSGE models that makes ML a valuable complement?
Correct. DSGE models excel at interpretable, theory-consistent scenario analysis but are calibrated on historical patterns and struggle with genuinely unprecedented shocks.
DSGE models are built on equilibrium relationships derived from economic theory — they perform well in "normal" regimes but fail during structural breaks like COVID-19, where the economy departs radically from historical patterns.
13. Question 15
Correct!
Incorrect.
14. BIS researchers identified three fundamental objections to autonomous AI monetary policy. Which of the following is NOT one of them?
Correct. Computing power is not the objection — the three real objections are accountability, value trade-offs, and AI-AI interaction risk.
The BIS identified three objections: (1) democratic accountability — algorithms can't testify to Congress; (2) inflation-employment trade-offs are political value judgments; (3) AI-AI catastrophic interaction with autonomous trading systems. Computing power was not cited.
15. Question 14
Correct!
Incorrect.
16. What property distinguishes an algorithmic stablecoin from a crypto-collateralized stablecoin like DAI?
Correct. Algorithmic stablecoins rely purely on algorithmic supply and demand adjustments — no collateral backs them. Terra/Luna's failure proved this design is fragile to coordinated attacks.
Incorrect. The key distinction is collateral: algorithmic stablecoins have none — they rely on algorithm-driven token issuance/burning to maintain pegs, which proved catastrophically fragile in Terra/Luna's case.
17. Question 2
Correct!
Incorrect.
18. Question 5
Correct!
Incorrect.
19. In what year did M-Pesa launch in Kenya, and what technology did it originally use?
Correct. M-Pesa launched in 2007 using simple SMS — proving that financial inclusion at scale did not require smartphones, apps, or AI. It later added AI layers for fraud detection and credit scoring.
Incorrect. M-Pesa launched in Kenya in 2007 using basic SMS technology — accessible on any mobile phone. This simplicity was key to its rapid adoption among low-income users.