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Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

What difficulty level is this interview question?

This is a medium difficulty Analytics & Experimentation question, commonly asked during Technical Screen rounds at OneMain Financial.

What role is this question designed for?

This question is commonly asked for Data Scientist candidates at OneMain Financial during technical interviews.

Maximize credit card portfolio profit | OneMain Financial Interview Question

Quick Overview

This question evaluates competence in credit-risk quantification, portfolio profitability analysis, approval decisioning and experiment design for a Data Scientist role, requiring estimation of expected credit loss, per-account economics, cutoff-based approval rules, and validation planning within an analytics & experimentation domain.

Credit Card Approval and Profit Optimization (12‑month horizon)

Context

You receive 10,000 credit card applications per month. Applicants are grouped into risk bands with given portfolio share and annual probability of default (PD). You can estimate expected credit loss (ECL) using:

EAD (exposure at default) = 80% of credit limit
LGD (loss given default) = 90%

Revenues and costs per approved account (12‑month):

Interchange revenue = 1.8% of annual spend
Expected annual spend per approved = $6,000
Variable servicing cost = $24/year
Acquisition cost = $50 per approved
Baseline credit limit = $2,500

Risk bands (share of applicants; PD):

A: 10%; PD = 0.2%
B: 20%; PD = 1%
C: 30%; PD = 3%
D: 25%; PD = 6%
E: 15%; PD = 10%

Assume PDs are 12‑month, spend is not constrained by the baseline limit, and other revenues (e.g., interest) are ignored.

Tasks

Compute, for each band, the expected credit loss (ECL) per approved and the expected profit per approved over 12 months.
Choose an approval cutoff (approve A…?, B…?, etc.) that maximizes total monthly profit for 10,000 applicants. Report the monthly portfolio ECL and the approval rate under your cutoff.
If limits can vary by band (e.g., halve D and E limits), recompute and state whether the optimal cutoff changes.
Outline a safe experiment to validate the decisioning policy before full deployment.

Quick Overview

Context

EAD (exposure at default) = 80% of credit limit

LGD (loss given default) = 90%

Revenues and costs per approved account (12‑month):

Interchange revenue = 1.8% of annual spend

Expected annual spend per approved = $6,000

Variable servicing cost = $24/year

Acquisition cost = $50 per approved

Baseline credit limit = $2,500

Risk bands (share of applicants; PD):

A: 10%; PD = 0.2%

B: 20%; PD = 1%

C: 30%; PD = 3%

D: 25%; PD = 6%

E: 15%; PD = 10%

Assume PDs are 12‑month, spend is not constrained by the baseline limit, and other revenues (e.g., interest) are ignored.

Tasks

Compute, for each band, the expected credit loss (ECL) per approved and the expected profit per approved over 12 months.

Choose an approval cutoff (approve A…?, B…?, etc.) that maximizes total monthly profit for 10,000 applicants. Report the monthly portfolio ECL and the approval rate under your cutoff.

If limits can vary by band (e.g., halve D and E limits), recompute and state whether the optimal cutoff changes.

Outline a safe experiment to validate the decisioning policy before full deployment.

Maximize credit card portfolio profit

Quick Overview

Credit Card Approval and Profit Optimization (12‑month horizon)

Context

Tasks

Solution

Comments (0)

Maximize credit card portfolio profit

Quick Overview

Credit Card Approval and Profit Optimization (12‑month horizon)

Context

Tasks

Solution

Comments (0)