How should I practice Propagating Fraud Detector?

Start by reading the full statement, solve the visible examples, then compare the pattern against related Affirm phone screen and onsite interview problems in FastPrep.

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Propagating Fraud Detector

Affirm● MediumFULLTIMEPHONE SCREENONSITE INTERVIEW

Problem statement

Process a batch of events in order while maintaining a set of suspicious personally identifiable information (PII) entries. Each row of events contains exactly five strings: event type, address, phone number, email address, and Social Security number. An empty string means the field is absent. PII identity includes its field, so equal text in different fields represents different entries.

For fraud_flag, add every non-empty PII entry to the suspicious set and append an empty string to the result.
For underwriting, append 1 when at least one non-empty PII entry is already suspicious; otherwise append 0. If the event is suspicious, add all of its non-empty PII entries to the set so suspicion can propagate. A clean underwriting event does not change the set.

Return one result string per event in the original order.

Function

detectPropagatingFraud(events: String[][]) → String[]

Examples

Example 1

events = [["fraud_flag","1 Main St","111","",""],["underwriting","2 Oak St","111","b@example.com","S2"],["underwriting","3 Pine St","333","b@example.com","S3"],["underwriting","4 Elm St","444","d@example.com","S4"]]return = ["","1","1","0"]

The fraud flag seeds phone 111. The second event is suspicious through that phone and adds all of its PII, including email b@example.com. That email makes the third event suspicious. The fourth event shares nothing suspicious.

Example 2

events = [["underwriting","A","P","",""],["fraud_flag","B","Q","",""],["underwriting","A","Q","",""],["underwriting","C","P","",""]]return = ["0","","1","0"]

The first underwriting event is clean, so it does not seed address A or phone P. The fraud flag seeds phone Q. The third event is suspicious through Q and propagates address A, but it still does not add phone P. The fourth event shares only P and therefore remains clean.

Example 3

events = [["fraud_flag","same","","",""],["underwriting","","same","",""]]return = ["","0"]

The first row marks the address value suspicious. The equal text in the second row is a phone value, so it is a different PII identity and does not match.

Constraints

1 <= events.length <= 100000
events[i].length == 5
events[i][0] is either underwriting or fraud_flag.
Every non-empty PII value has length from 1 through 100.
Every event contains at least one non-empty PII field.