Design an adaptive credit decisioning platform
Last updated: August 6, 2025
Quick Overview
Design a platform that evaluates loan applications in real-time using ML models, handles model versioning, supports A/B testing of underwriting strategies, and maintains compliance with lending regulations.
Affirm
August 6, 20258
3
3,078 solved
Design a platform that evaluates loan applications in real-time using ML models, handles model versioning, supports A/B testing of underwriting strategies, and maintains compliance with lending regulations.
Credit decisioning is the core of Affirm's business. This question tests whether you understand how ML models, business rules, and regulatory requirements intersect in a lending platform. Affirm's approach to credit is what differentiates it from traditional lenders.
What the Interviewer Expects
- Design a system that returns credit decisions in under 500ms
- Support multiple ML models running in parallel for A/B testing
- Include a rules engine for regulatory compliance (fair lending, state-specific rules)
- Design for auditability so every decision can be explained and justified
- Handle model fallback gracefully when the primary model times out or fails
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you ensure your credit model does not discriminate against protected classes?
- How would you handle a model that starts performing poorly due to macroeconomic changes?
- How do you balance approval rates with default rates?
- What happens when a new regulatory requirement changes your decision logic overnight?
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Practice System Design ProblemsSample Answer
Requirements
- Functional Requirements:
- Evaluate loan applications in real-time using multiple ML models.
- Implement model versioning to manage updates and rollbacks.
- Support A/B testing of...
Capacity Estimation
Assuming Affirm processes around 1 million loan applications per month, we can estimate daily traffic:
- Daily Applications:
1 million / 30 = ~33,333 applications per day. - Peak Hour:...
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