Assess LLMs for fraud detection
Overview
This question evaluates a candidate's ability to compare large language models and traditional supervised models for fraud detection, testing competencies in multi-modal data handling, representation learning versus feature engineering, latency and cost trade-offs, interpretability and governance, adversarial robustness, privacy/compliance, and end-to-end lifecycle operations in production ML systems. It is commonly asked in Machine Learning interviews to assess both conceptual understanding and practical application skills for designing hybrid architectures, evaluation plans, and operational strategies for large-scale, real-time fraud detection systems.
LLMs in Fraud Detection: Near-Term vs. Long-Term Roles
Context
You are designing fraud detection for a large-scale digital payments platform with:
- Real-time transaction scoring requiring sub-100 ms p95 latency and high availability.
- Multiple data sources: structured transaction/device/network signals; unstructured text (support chats, claims, merchant descriptions, KYC docs, emails); and semi-structured logs.
- A human review workflow for escalations and investigations.
Task
-
Compare large language models (LLMs) to traditional supervised models for fraud detection across:
- Data modalities unlocked (text, logs, documents)
- Feature engineering vs. representation learning
- Accuracy (new pattern discovery vs. steady-state classification)
- Latency and cost
- Interpretability and governance
- Robustness to adaptive adversaries
- Privacy/compliance
- Lifecycle operations (training, deployment, monitoring, updates)
- Describe the near-term and long-term roles LLMs should play in this stack.
- Propose a hybrid architecture that integrates LLMs and traditional models to balance accuracy, latency, cost, and risk.
- Propose an evaluation plan (offline and online) to justify adoption, including metrics, ablations, cost/latency modeling, and risk controls.
Solution
Login required