用深度神经网络预测贷款违约概率和时间，降低投资组合风险。
Minimizing the Value-at-Risk of Loan Portfolio via Deep Neural Networks
- 设计双模型预测违约概率与时间，优化贷款组合风险
- 在不同置信水平下，显著降低投资组合的VaR值
- 参数少的DeNN模型在多数场景下表现优于复杂模型
P2P借贷中的风险管理至关重要。投资者通常通过分散投资来降低风险，而非集中于单一贷款。此时，投资者希望最小化贷款组合的损失风险，如价值在险（VaR）或条件价值在险（CVaR）。本文提出两种深度神经网络模型：低自由度的DeNN与高自由度的DSNN，用于同时预测贷款的违约概率及其违约时间。实验表明，相比基准方法，两种模型在不同置信水平下均能显著降低投资组合的VaR。更有趣的是，在大多数场景中，低自由度的DeNN模型表现优于复杂的DSNN模型。
原文摘要 · Abstract (English)
Risk management is a prominent issue in peer-to-peer lending. An investor may naturally reduce his risk exposure by diversifying instead of putting all his money on one loan. In that case, an investor may want to minimize the Value-at-Risk (VaR) or Conditional Value-at-Risk (CVaR) of his loan portfolio. We propose a low degree of freedom deep neural network model, DeNN, as well as a high degree of freedom model, DSNN, to tackle the problem. In particular, our models predict not only the default probability of a loan but also the time when it will default. The experiments demonstrate that both models can significantly reduce the portfolio VaRs at different confidence levels, compared to benchmarks. More interestingly, the low degree of freedom model, DeNN, outperforms DSNN in most scenarios.
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