FairPlay is the first Fairness-as-a-Service solution designed specifically for financial institutions, leveraging AI-powered tools to enhance model fairness and profitability. It automates the assessment of loan portfolios to identify biases, conduct fair lending analysis, and optimize underwriting and pricing processes efficiently. FairPlay offers features including Fair Lending Analysis, Customer Composition views, Redlining assessments, and Proxy Detection to ensure equitable lending practices while maintaining compliance with relevant regulations. The product is aimed at fintechs, banks, and sponsor banks, providing tailored solutions for each segment. With FairPlay, lenders can expect quick evaluations of models, creating opportunities for increased approval rates without raising risk levels, ultimately promoting economic inclusion and improved financial outcomes.
• real-time monitoring
• fair lending analysis
• proxy detection
• second look
• fairness optimizer
• customer composition
• demographic imputation methodologies
• automated fairness assessments
• redlining assessments
FairPlay offers automated solutions for quick, reliable, and affordable evaluations of credit models and practices, enabling lenders to quickly adapt while improving profits and inclusion.
No! Models can still inadvertently perpetuate discrimination due to correlations with non-protected factors; continuous monitoring is essential for fairness.
ECOA does not prohibit awareness of protected status during model development to build unbiased systems. FairPlay’s loss function minimizes disparities while maintaining robust performance.
FairPlay uses AI to optimize decisions by replicating and explaining them. Its loss function helps models achieve multiple targets including high approval rates and fairness.
FairPlay supports a diversity of lending products including mortgages, auto loans, credit cards, and Buy Now Pay Later options.
By optimizing credit models to assess underrepresented groups more effectively, FairPlay’s methods improve underwriting for protected classes.
Demographic imputation methodologies are used to estimate race and ethnicity based on names and geographic locations.
Fairness is assessed using various metrics like adverse impact ratio, denial odds ratio, and marginal effects to evaluate potential disparate impacts.
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