Emerging phenomena of ubiquitous multisource data offer promising avenues for making breakthroughs in financial risk prediction. While most existing methods for financial risk prediction are based on a single information source, which may not adequately capture various complex factors that jointly influence financial risks, we propose a hybrid-strategy-based self-adaptive method to effectively leverage heterogeneous soft information drawn from a variety of sources. The method uses a proposed new feature- sparsity learning method to adaptively integrate multisource heterogeneous soft features with hard features and a proposed improved evidential reasoning rule to adaptively aggregate base classifier predictions, thereby alleviating both the declarative bias and the procedural bias of the learning process. Evaluation in two cases at the individual level (concerning borrowers at a P2P lending platform) and the company level (concerning listed companies in the Chinese stock market) showed that, compared with relying solely on hard features, effectively incorporating multisource heterogeneous soft features using our proposed method enabled earlier prediction of financial risks with desirable performance.

中文翻译：

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利用多源异构数据进行金融风险预测：一种基于混合策略的新型自适应方法

无处不在的多源数据的新兴现象为金融风险预测的突破提供了有希望的途径。虽然大多数现有的金融风险预测方法都基于单一信息源，可能无法充分捕捉共同影响金融风险的各种复杂因素，但我们提出了一种基于混合策略的自适应方法，以有效利用从各种来源。该方法使用提出的新特征稀疏学习方法将多源异构软特征与硬特征自适应地集成，并使用改进的证据推理规则来自适应地聚合基分类器预测，从而减轻学习过程的声明性偏差和程序性偏差。