The proliferation of social media along with AI techniques have flourished the use of multimodal data to default risk prediction. In this study, we submit a pair of opposite effects for multimodal data based default risk prediction, namely, mutual promotion effects and mutual restriction effetcs. Given such a premise, we firstly extract hard features, social media textual features, and facial attractiveness features for each of the borrowers, and then we propose a novel multi-level co-adaptation regularized multimodal deep learning method. Our proposed method is capable of identifying mutually promoted patterns among multimodal features, besides, our method opens up a novel avenue for balancing the trade-off between feature-wise co-adaptation and modality-wise co-adaptation. Moreover, we theoretically prove that by virtue of our method, multimodal features with multi-level co-adaptations can be automatically detected. The experimental resuts show that our method beats existing methods by a significant margin.
Chen, Gang; Lu, Tian; and Zhang, Chenghong, "Mutually Promoted or Mutually Restricted? A Multi-level Co-adaptation Regularized Deep Learning Method for Multimodal Data Based Default Risk Prediction" (2020). PACIS 2020 Proceedings. 74.
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