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 problems in FastPrep.

Is Propagating Fraud Detector associated with Affirm?

FastPrep tags this as a Affirm phone screen coding practice problem when the problem catalog has that company and stage context.

Problem · Array

Propagating Fraud Detector

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AffirmFULLTIMEPHONE SCREEN

Problem statement

Process a batch of events in order while maintaining a set of suspicious PII entries. Each row of events contains exactly five strings in this order:

the event type, either underwriting or fraud_flag;
an address;
a phone number;
an email address;
a Social Security number.

An empty string means that the corresponding PII field is absent. PII identity includes its field: two equal strings in different fields are different entries.

Apply these rules to each event:

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

Return one string per input event in the same 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","1"]

The first event is initially clean and does not seed suspicion. The fraud flag seeds phone Q. The third event is suspicious through Q and then propagates suspicion to address A. Its phone P also becomes suspicious, so the fourth event is flagged.

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.