AI-Driven Innovations in Blockchain-Based Trade Finance BBLC Practice Questions (Page 7)

Questions 31–35

1. 31application · medium

   A logistics company uses a blockchain-based trade platform to coordinate shipments. They want to anticipate port congestion and adjust routing decisions before delays cascade. Which AI capability should they prioritize?

   • AComputer vision to inspect container damage at loading docks.
   • BBlockchain consensus optimization to speed up transaction finality on the trade platform.
   • CPredictive analytics that ingest historical shipping data, weather patterns, and real-time port status feeds to forecast congestion probability.
   • DNatural language processing to automatically summarize shipping contracts and extract delivery deadlines.
   Reveal Solution Discussion Select an answer first
2. 32foundation · easy

   In trade finance, how does AI contribute to credit risk assessment?

   • ABy storing credit reports on a centralized server
   • BBy guaranteeing that all loans are repaid
   • CBy eliminating the need for credit scoring
   • DBy using alternative data sources to build more comprehensive borrower profiles
   Reveal Solution Discussion Select an answer first
4. 33application · medium

   A mid-sized bank processes letters of credit for SME importers. The bank wants to reduce fraudulent invoice submissions while keeping the existing blockchain-based trade platform unchanged. The compliance team also needs to flag suspicious patterns for AML reporting. Which approach should the bank adopt?

   • AReplace the blockchain platform with a centralized database that uses rule-based checks to validate invoice amounts against purchase orders.
   • BIncrease the number of required signatures on the smart contract to ensure multiple parties approve each payment.
   • CDeploy an AI model that analyzes historical invoice data and transaction patterns to score each submission for fraud risk before the smart contract executes payment.
   • DAdd a manual review step for every invoice above $10,000, using a checklist of common fraud indicators.
   Reveal Solution Discussion Select an answer first
5. 34expert · hard

   A global trade finance platform is considering integrating AI models with its blockchain to improve fraud detection and predictive analytics. The platform must comply with GDPR, which restricts the transfer of personal data outside the EU. The AI models need access to transaction data from multiple regions. Which architecture best balances AI effectiveness with regulatory compliance?

   • AStore all transaction data on a private blockchain within the EU and run AI models only on that data, ignoring non-EU data to ensure compliance.
   • BDeploy a single centralized AI model that accesses all transaction data stored on the blockchain, regardless of region, to maximize predictive accuracy.
   • CAnonymize all personal data before storing on the blockchain, then allow AI models to access the anonymized data globally without restrictions.
   • DUse federated learning where AI models are trained locally in each region on local data, and only model updates (not raw data) are shared to a central model, with blockchain providing an audit trail of model updates.
   Reveal Solution Discussion Select an answer first
6. 35expert · hard

   A bank's AI fraud detection system for trade finance is generating too many false positives, causing delays for legitimate customers. The bank also needs to meet AML reporting deadlines. The compliance team suggests tuning the model to reduce false positives, but the risk team worries this might increase false negatives. What is the most balanced approach?

   • AKeep the current threshold and add a manual review queue for all flagged transactions.
   • BSet the threshold to zero false positives, accepting that some suspicious transactions may go undetected.
   • CDisable the AI model and use a simple rule-based system that only flags transactions above a certain amount.
   • DAdjust the model's decision threshold based on a cost-benefit analysis that weighs the cost of false positives against the regulatory cost of false negatives.
   Reveal Solution Discussion Select an answer first