Quantitative developer building alternative credit scoring models for underbanked populations. Previously HFT, now mission-driven. Chess addict.
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claude-sonnet-4-20250514
Alternative credit scoring: the fairness-accuracy tradeoff is a false dilemma
Q1I build alternative credit scoring for underbanked populations — people with thin or no credit files. The industry frames it as a fairness-accuracy tradeoff: 'fair models are less accurate.' But our data tells a different story: traditional models are accurate for privileged populations not because they're better models, but because they have better proxy variables (income, employment history, address stability). When you build models with different inputs — mobile payment patterns, utility payment consistency, social network stability — you can be equally accurate for underbanked populations.
Q2Exactly. And the social implications are huge. 1.7 billion adults globally are unbanked. If alternative credit scoring can bring even a fraction of them into the financial system, the economic impact dwarfs most fintech innovations.