Insurance data, in an ideal world, would perfectly reflect reality. However, data is often inherently biased, whether due to uneven sampling or systemic selection effects. Earnix, a firm looking to redefine the insurance and banking sectors, has opened up on the critical topic of model bias and how to address it effectively.
In relation to insurance data, the push for fairness is echoed by regulatory bodies worldwide. The European Insurance and Occupational Pensions Authority (EIOPA) advocates for fairness metrics like Demographic Parity and Equalised Odds, aiming to protect consumers from discriminatory practices.
Similarly, the UK’s Financial Conduct Authority (FCA) and the US Consumer Financial Protection Bureau (CFPB) focus on mitigating price discrimination and ensuring algorithmic transparency.
With regulators emphasising fairness, insurers and banks need tools to identify and address biases while maintaining model performance.
Innovation through Earnix Labs
Earnix Labs serves as the company’s innovation hub, turning cutting-edge ideas into actionable tools for insurers and banks.
This collaborative process allows customers to test early-stage features, providing valuable feedback that shapes solutions like the Model Analysis Lab.
The Earnix Model Analysis Lab provides insurers and banks with tools to measure, visualise, and mitigate biases in data and models.
Metrics such as Demographic Parity Difference, Equalised Odds, and Equal Opportunity are central to this process. For example, demographic parity measures disparities in outcomes between groups, while equalised odds focus on maintaining consistent true and false positive rates across demographics.
The lab also incorporates advanced feature importance analysis, helping users understand how sensitive variables interact with others. This insight enables targeted interventions to reduce bias.
Automating bias mitigation
Manual bias adjustments can compromise data utility and model performance. The Model Analysis Lab employs automated algorithms to address these challenges effectively.
Built on Microsoft’s open-source Fairlearn package, the lab optimises fairness metrics while preserving predictive accuracy. For instance, its optimisation algorithm adjusts weights in the loss function, balancing positive outcomes across groups without significant performance loss.
The results speak for themselves: models achieve fairer outcomes with minimal impact on accuracy, such as reducing demographic parity difference to 4% while maintaining 87% accuracy.
Earnix’s Model Analysis Lab represents a significant step forward in addressing bias in financial analytics.
By automating bias detection and mitigation, it helps insurers and banks meet ethical and regulatory standards without sacrificing performance. As the FinTech landscape continues to evolve, tools like these ensure fairness and compliance remain at the forefront of innovation.
Read the full blog from Earnix here.
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