FairPlay
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About
FairPlay provides AI-powered tools that assess automated decisioning models in minutes to enhance both fairness and profits in lending practices. It features various services like Fair Lending Analysis, Proxy Detection, and Fairness Optimizer to help mitigate bias and improve compliance in financial institutions. The platform allows for rapid evaluations of credit models and policies while promoting transparency and accountability through robust fairness metrics.
Platform
Features
• real-time monitoring
• fair lending analysis
• proxy detection
• second look process for declined loans
• fairness optimizer tool
• customer composition insights
• ai-powered fairness analysis
FAQs
What makes FairPlay different from other Fair Lending solutions?
FairPlay’s automated fairness-as-a-service solutions offer lenders a competitive advantage by enabling quick, reliable, and affordable evaluations of new models, products, data sources, and policy changes for fairness.
If underwriting models do not consider protected attributes like race and sex, aren’t they by definition already fair to protected groups?
No! While such underwriting models may seem fair on the surface, they can still inadvertently perpetuate discrimination due to correlations with protected attributes.
How are you able to consider qualities like race and sex without running afoul of the Equal Credit Opportunity Act (ECOA)?
ECOA does not prohibit consciousness of protected status during model development for the purpose of avoiding bias. FairPlay’s loss function ensures fairness during training without requiring protected status at decision time.
How do you incorporate AI in your models?
FairPlay uses AI to optimize decisions, replicating, explaining, and optimizing credit assessments for fairness and accuracy.
What type of loan products do you support?
FairPlay's solutions apply to various consumer and small business credit products including mortgages, auto loans, credit cards, and more.
How can you tell if an applicant belongs to a protected class?
We use demographic imputation methodologies to estimate race and ethnicity based on names and locations.
How do you measure “fairness”?
Fairness is measured using metrics like adverse impact ratio, denial odds ratio, and marginal effects.
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