Adversarial training for P2P lending models shows mixed robustness across attack types

Researchers have evaluated the robustness of machine learning models used in peer-to-peer lending against various adversarial attacks. The study found that while adversarial training significantly improves a model's defense against the specific attack it was trained on, this robustness does not always transfer well to different types of attacks, particularly non-gradient-based ones. The findings suggest that mixed-attack adversarial training offers the most balanced defense across diverse manipulation methods, which is crucial for effective credit model governance. AI

IMPACT Highlights the need for diverse adversarial training strategies to ensure reliable credit scoring models in real-world scenarios.

RANK_REASON Academic paper detailing a robustness evaluation of machine learning models. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

• DeepFool
• Fast Gradient Sign Method
• feedforward neural network
• FGSM
• Lending Club
• logistic regression model
• Projected Gradient Descent
• salt and pepper
• S&P 500
• Transformer++

• paper
• safety

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