Senior interview questionAsked at PayPal · interview write-up
What role should LLMs play in fraud detection compared with traditional supervised models, considering data types, accuracy, latency, cost and interpretability?
Model answer
LLMs aren't the core scorer. Fraud signals are mostly tabular and behavioural: amounts, velocities, device and graph features. Gradient-boosted trees or deep models trained on labels do better there, score in milliseconds at tiny cost, and give stable feature attributions for explanations.
LLMs earn a place around the model: turning unstructured text such as dispute notes, merchant descriptions and chats into features, summarising cases for investigators, drafting reason codes, and drafting rules. Keep them off the authorization path, validate their features like any other, and guard against prompt injection in user-written text.
Practise the rest
40 ML system design questions, 492 in all, each with a model answer. Free.
More from PayPal interview questions.
More ML system design interview questions
- You need to copy 500 GB of weights to 1,000 GPU workers over shared 10 Gbps links. What's the lower bound on time, and what would you build?
- Design an LLM coding assistant end to end: data, model and evaluation.
- Design a job scheduler for ML training and batch inference on a shared GPU cluster.
- Training a retrieval model over a catalogue of millions of items, you use sampled softmax. Why does it need a correction, and how do you choose the classification loss?
- Design an inference API that converts PDFs into Markdown using several models in stages.
- How can LLMs be incorporated into recommendation systems, from metadata enrichment and cold start to generative retrieval and semantic reranking?