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Why Credit Scoring?
Credit scoring is a numerical system used by lenders to evaluate a person’s creditworthiness based on their credit history and financial behavior and, as such, it affects key decisions—from loan approvals to housing and jobs. But algorithms producing it can unintentionally perpetuate biases based on sensitive attributes such as gender, age, or nationality. With global regulations (e.g. EU AI Act, EBA guidelines) demanding fairness and transparency, ensuring these systems are unbiased is more than good practice—it’s essential.
Data & Model Setup
We work on the widely used UCI German Credit dataset (1,000 entries with 20 features). First, we produce a scorecard-based credit scoring model built using Optibinning (for Weight‑of‑Evidence binning), then we analyse it on a few selected attributes deemed “sensitive”, in this case gender, age, and foreign nationality.
Step 1 – Detecting Bias with BRIO
BRIO analyzes the model’s predictions and engages two key modules:
- Bias‑detection: Measures distributional disparities using divergence metrics (e.g., Kullback–Leibler, Jensen–Shannon).
- Unfairness‑risk: Aggregates class‑wise “hazard values” to highlight where bias poses significant risk.
The data shows that males tend to exhibit better default risk outcomes compared to females, older age groups perform better than younger ones, and domestic workers are favoured comparing to foreigners. We note that the model’s predictions essentially reflect what was found by the default analysis, though the relative difference between the various sensitive classes turns out to be attenuated in the case of foreigners and accentuated in the case of gender and age groups.
Step 2 – Examining Bias Amplification
BRIO’s advanced tech lets us also trace where bias arises:
- Does the training sample reflect real-world distributions?
- Does the model amplify divergence versus the training sample?
We provide a combined view of where intervention is most needed. For example, the tool found that age-related disparities were amplified in model outputs compared to data alone, while for nationality the difference is negative, suggesting that the model effectively corrects the minor bias present in the data.
Step 3 – Balancing Fairness & Revenue
BRIO also links unfairness risk to business impact. It plots:
- Expected provisions (loss reserves) and bad‑rate across credit score thresholds.
- A fairness‑risk curve showing its divergence between data and the model.
In the case of the German dataset, a score threshold around 620 hit an optimal balance—maximizing profit while mitigating fairness violations.
Key Takeaways for MIRAI Clients
- Model‑agnostic: Compatible with any ML or DL system.
- Sensitive‑feature exploration: Scalable analysis across multiple demographic factors.
- Bias‑risk quantification: Produces clear hazard values to guide audits.
- Business‑fairness alignment: Integrates fairness into revenue-driven decisions—no need to sacrifice profit.
Ready to Level Up Your AI?
With BRIO, MIRAI empowers organizations to detect, quantify, and mitigate unfairness in AI-enabled credit scoring. It balances ethical responsibility with business needs—fueling trust, compliance, and smarter lending.
Curious to see BRIO in action on your systems? Reach out to MIRAI to learn how fairness-aware AI can transform your risk analysis practice.
References:
Coraglia et al., Evaluating AI fairness in credit scoring with the BRIO tool, arXiv (June 2024) (arxiv.org, researchgate.net).
Buda et al., Bias Amplification Chains in ML-based Systems with an Application to Credit Scoring, BEWARE 2024 (researchgate.net, air.unimi.it).
