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Design a Credit Scoring Platform

KikoffSoftware Engineer · Onsite
MediumSoftware EngineerOnsiteML System Design

Last updated

This question assesses the ability to design an end-to-end machine learning system for real-time credit risk assessment, focusing on data pipelines, model inference, and regulatory compliance.

Problem Statement

Design a credit scoring platform that calculates a credit risk score for users in real-time when they apply for financial products. The platform must handle millions of users and integrate with both internal user profile data and external credit bureau reports.

Functional Requirements

  • Real-time scoring: A user requests a score; the system must return a result within 500ms.
  • Model versioning: Support A/B testing of different model versions.
  • Auditability: The platform must log exactly which features and model version were used to generate each score for regulatory compliance.

Non-Functional Requirements

  • High availability and scalability for bursty traffic.
  • Data privacy and security, as the system handles PII (Personally Identifiable Information).
  • Low latency in feature retrieval.

Deliverables

  1. High-level architecture diagram (described in text).
  2. API design for the scoring service.
  3. Strategy for managing and updating feature sets (e.g., transactional data vs. static profile data).
  4. Approach for model monitoring and detecting data drift.

Hints

Hint 1

Consider using a feature store to bridge the gap between offline training data and online inference needs.

Hint 2

Decouple the scoring service from the data ingestion pipelines to ensure that slow external data fetching does not block the real-time request.

Hint 3

Think about how to implement a 'shadow mode' or 'canary deployment' to safely test new model versions before full roll-out.

Follow-up questions

  • How would you handle a scenario where an external credit bureau API experiences downtime or high latency?
  • How does the system ensure fairness and prevent bias in the credit scores?
  • If the business introduces a new 'repayment history' feature, how would you update the inference pipeline without downtime?