Per-account scoring catches the lone fraudster and misses the ring whose accounts each look clean. The strong answer reframes the problem as a graph, makes entity resolution the step everything else depends on, and treats a flagged ring as a case for an investigator, not an automatic block.
Updated Aug 2026 · Grounded in real Forward Deployed Engineer interview loops and written to a senior-engineer editorial bar.
Per-account scoring catches the lone fraudster and misses the ring whose accounts each look clean. The strong answer reframes the problem as a graph, makes entity resolution the step everything else depends on, and treats a flagged ring as a case for an investigator, not an automatic block.
This deliberately follows the single-transaction fraud question and tests whether the candidate can see why rings are a different problem. The naive answer applies a better per-transaction classifier; the insight is that ring members are individually unremarkable and only the network of shared identifiers, devices, IPs, and money flows reveals them. The discriminating move is to name entity resolution as the hard step the rest rests on: the graph is only as good as your ability to decide that two accounts are the same actor, and fraudsters deliberately avoid reusing obvious identifiers. The follow-up that scores: 'a connected component lights up, do you auto-block it?' The right answer is no, you generate an investigation case with the evidence subgraph for a human, because false positives on a tight component can freeze a legitimate family or small business sharing an address. Saying 'train a GNN' without the entity-resolution layer or the human review loop reads as someone who has only seen the Kaggle version.
No comments yet — be the first to share your approach.
