A regional bank's finance operations team automates two processes. The first automatically approves and reimburses any expense report under $50 whenever the total matches a fixed threshold coded directly into the workflow. The second reviews years of past transaction records, learns the spending patterns that preceded confirmed fraud cases, and flags new transactions that resemble those patterns even though no explicit threshold was written for them. Which process is best classified as an artificial intelligence workload?
Select an answer to reveal the explanation.
Short Explanation
You can tell it's artificial intelligence when the system learns the decision logic from data instead of following a fixed rule you or a developer wrote. The fraud-pattern flagger does that; the $50 expense approver doesn't.
Full Explanation
The fraud-pattern flagging process is the artificial intelligence workload because its decision logic comes from a model that learned patterns in historical transaction data, rather than from a rule a developer typed in advance; it can recognize new transactions that resemble past fraud even though no one specified the exact conditions to check. The $50 expense approval process is not artificial intelligence because it follows a fixed, explicitly coded threshold with no learning involved, which is traditional deterministic programming, not artificial intelligence, even though it runs unattended. The view that both processes are artificial intelligence is incorrect because automation and artificial intelligence are not the same thing; a system can run without human intervention and still just be executing rules a person wrote, which describes the expense approval process. The view that neither process is artificial intelligence is also incorrect because the ability to explain reasoning in natural language describes a specific responsible artificial intelligence property called transparency or interpretability, not a requirement for something to count as artificial intelligence in the first place; many valid artificial intelligence systems, including this fraud model, qualify without producing natural-language explanations. Exam caveat: don't let hands-off automation or the absence of plain-language explanations distract you from the real test, which is whether the behavior comes from learned patterns in data or from fixed coded rules.