  In the insurance industry detecting fraudulent claims is a critical task with
a significant financial impact. A common strategy to identify fraudulent claims
is looking for inconsistencies in the supporting evidence. However, this is a
laborious and cognitively heavy task for human experts as insurance claims
typically come with a plethora of data from different modalities (e.g. images,
text and metadata). To overcome this challenge, the research community has
focused on multimodal machine learning frameworks that can efficiently reason
through multiple data sources. Despite recent advances in multimodal learning,
these frameworks still suffer from (i) challenges of joint-training caused by
the different characteristics of different modalities and (ii) overfitting
tendencies due to high model complexity. In this work, we address these
challenges by introducing a multimodal reasoning framework, AutoFraudNet
(Automobile Insurance Fraud Detection Network), for detecting fraudulent
auto-insurance claims. AutoFraudNet utilizes a cascaded slow fusion framework
and state-of-the-art fusion block, BLOCK Tucker, to alleviate the challenges of
joint-training. Furthermore, it incorporates a light-weight architectural
design along with additional losses to prevent overfitting. Through extensive
experiments conducted on a real-world dataset, we demonstrate: (i) the merits
of multimodal approaches, when compared to unimodal and bimodal methods, and
(ii) the effectiveness of AutoFraudNet in fusing various modalities to boost
performance (over 3\% in PR AUC).
