A finished ACC-337 Topic 4 rule-based anomaly screen example, applying three rules written before any query to a year of fuel card transactions and passing the surviving hits on for review. Searches like "acc 337 topic 4 assignment example", "acc337 topic 4 sample" and "acc-337 topic 4 example" land here.
What a finished ACC-337 Topic 4 rule-based anomaly screen looks like
The finished screen puts its rules first, above any result. A purchase larger than the truck's recorded tank capacity is flagged, because no single fill can exceed the tank. A purchase on a day the dispatch log shows the truck parked is flagged, and so is a pair of purchases on one card within thirty minutes at stations more than twenty miles apart. Each rule names the false alarms expected, such as a refrigeration unit filled on the same card. The results follow: 14, 37 and 6 hits, 57 in all, about two in every thousand transactions. Follow-up on the 37 finds that 29 fall on two dates when the dispatch log was never updated, which is a data finding rather than a fuel one. The remaining 28 go to the fleet manager as items to review.
How an ACC-337 Topic 4 example is structured
The screen is ordered so that the rules visibly come before the results. It opens with the question the controller asked, whether fuel cards are being used for anything other than company trucks, and the dataset to be searched. The rule sheet follows, stating the three rules precisely enough to code, each with its rationale, its threshold and the false alarms it should produce, and recording the date they were written. Hit counts for each rule come next. The largest group is then investigated, separating the 29 hits caused by a gap in the dispatch log from the 8 that remain. A list of the 28 transactions passed on for review follows, with the rule each one tripped. The final part names what the screen could not detect, such as a driver buying fuel within the tank's capacity and diverting it later.
Rules dated before the query
The rule sheet carries the day it was written, earlier than the first query, so nobody can suspect the thresholds were tuned to the results.
Every threshold given a reason
Tank capacity, a parked day and thirty minutes over twenty miles each come with one line explaining why that boundary separates ordinary use from something worth checking.
False alarms predicted in advance
A refrigeration unit fueled on the truck's card is named as a likely false positive for the capacity rule before any hit appears, which speeds the follow-up.
A data gap found by the screen
Twenty-nine hits cluster on two dates with no dispatch entries, and the example reports that as a problem in the log rather than in the fuel purchases.
Hits passed on, not judged
The 28 remaining transactions go to the fleet manager as items to review, since a rule identifies what is unusual and cannot say what happened.
What the rules cannot see
Fuel bought within tank capacity and diverted later trips none of the three rules, and the screen states that limit instead of implying full coverage.
Where marks go in ACC-337 Topic 4
Browsing the data first and choosing thresholds afterward is the error this assignment most often exposes, and markers can usually tell from how neatly the hits fit a story. A rule stated loosely, such as unusually large purchases, cannot be coded the same way twice, so its results cannot be reproduced. Screens that report every hit as misuse overstate what a rule can establish, and the 29 dispatch-log hits show how quickly that goes wrong. Leaving out the expected false alarms slows the follow-up and makes the hit count look more alarming than it is. Papers giving counts without saying what share of the population they represent leave the reader unable to judge scale. Omitting what the rules would miss implies a coverage the screen does not have, which a careful reader will question.
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ACC-337 Topic 4 questions, answered
How do I pick a threshold without looking at the data?
Derive it from something outside the dataset: a physical limit such as tank capacity, a policy such as an approval ceiling, or an operational fact such as the hours a depot is open. Those boundaries mean something before any transaction is examined. Where no outside anchor exists, state the threshold as a judgment, give the reasoning, and write it down with a date before running the query.
Does a hit mean something went wrong?
No. A hit means a transaction met a rule, and the rule was written to catch the unusual, not to prove misconduct. Some hits will be legitimate exceptions, some will expose problems in the data itself, as the dispatch log did here, and a few may warrant a closer look. The example passes those few to the person responsible and stops there.
How many rules should a screen use?
As many as the question needs and each one can be justified, which at an introductory level usually means a handful. Every added rule produces more hits to follow up, so a rule with no clear rationale costs time without adding insight. A few rules tied closely to the controller's question give a screen that can actually be worked through within the assignment.